Last updated on 2018-06-17 19:49:30 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 3.1.6 | 2.96 | 40.95 | 43.91 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 3.1.6 | 1.60 | 31.46 | 33.06 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 3.1.6 | 50.15 | NOTE | |||
| r-devel-linux-x86_64-fedora-gcc | 3.1.6 | 46.23 | NOTE | |||
| r-devel-windows-ix86+x86_64 | 3.1.6 | 7.00 | 56.00 | 63.00 | NOTE | |
| r-patched-linux-x86_64 | 3.1.6 | 2.97 | 37.73 | 40.70 | ERROR | |
| r-patched-solaris-x86 | 3.1.6 | 70.40 | NOTE | |||
| r-release-linux-x86_64 | 3.1.6 | 1.88 | 37.31 | 39.19 | ERROR | |
| r-release-windows-ix86+x86_64 | 3.1.6 | 7.00 | 60.00 | 67.00 | NOTE | |
| r-release-osx-x86_64 | 3.1.6 | NOTE | ||||
| r-oldrel-windows-ix86+x86_64 | 3.1.6 | 5.00 | 89.00 | 94.00 | NOTE | |
| r-oldrel-osx-x86_64 | 3.1.6 | NOTE |
Version: 3.1.6
Check: S3 generic/method consistency
Result: NOTE
Found the following apparent S3 methods exported but not registered:
coef.boolean logLik.boolean model.matrix.boolean nobs.boolean
plot.boolprob plot.boolprof predict.boolean print.boolboot
print.boolean print.boolprob print.boolprof print.boolsum
summary.boolboot summary.boolean
See section ‘Registering S3 methods’ in the ‘Writing R Extensions’
manual.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64
Version: 3.1.6
Check: R code for possible problems
Result: NOTE
print.boolprob: no visible global function definition for ‘plot’
print.boolprof: no visible global function definition for ‘plot’
start_msg: no visible global function definition for
‘packageDescription’
Undefined global functions or variables:
packageDescription plot
Consider adding
importFrom("graphics", "plot")
importFrom("utils", "packageDescription")
to your NAMESPACE file.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64
Version: 3.1.6
Check: examples
Result: ERROR
Running examples in ‘boolean3-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: boolean3-package
> ### Title: Modeling Causal Complexity
> ### Aliases: boolean3 boolean3-package
> ### Keywords: package
>
> ### ** Examples
>
> ## Generate some fake data
> require(mvtnorm)
Loading required package: mvtnorm
> set.seed(12345)
> N <- 2000
> Df <- cbind(1, rmvnorm(N, mean=rep(0, 5)))
>
> ## Set coefficients
> beta.a <- c(-2.00, 0.33, 0.66, 1.00)
> beta.b <- c(0.00, 1.50, -0.25)
>
> ## Generate path probabilities following a normal model.
> y.a <- as.vector(pnorm(tcrossprod(beta.a, Df[, 1:4])))
> y.b <- as.vector(pnorm(tcrossprod(beta.b, Df[, c(1, 5, 6)])))
>
> ## AND and OR-model
> or <- function(x, y) { x + y - x * y }
> and <- function(x, y) { x * y }
> y.star.OR <- or(y.a, y.b)
> y.star.AND <- and(y.a, y.b)
>
> ## Observed responses
> y.OR <- rbinom(N, 1, y.star.OR)
> y.AND <- rbinom(N, 1, y.star.AND)
>
> ## Set up data.frame for estimation
> Df <- cbind(1, Df)
> Df <- as.data.frame(Df)
> Df[,1] <- y.OR
> Df[,2] <- y.AND
> names(Df) <- c("y.OR", "y.AND", "x1", "x2", "x3", "x4", "x5")
>
> ## Before estimating, boolean models need to be specified using the
> ## boolprep function.
>
> ## OR model, specified to use a probit link for each causal path. This
> ## matches the data generating process above.
> mod.OR <- boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5,
+ data = Df, family=list(binomial("probit")))
>
> ## IF you really want to, it's also possible to specify a different
> ## link function for each causal path. These should be in the same
> ## order as specified in the model formula.
> mod.OR2 <- boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5,
+ data = Df, family=list(binomial("probit"),
+ binomial("logit")))
>
> ## Fit the prepared model using the nlminb optimizer (the default).
> (fit.OR <- boolean(mod.OR, method="nlminb", control=list(trace=1)))
fn is fn
Looking for method = nlminb
Function has 7 arguments
Analytic gradient not made available.
Analytic Hessian not made available.
Scale check -- log parameter ratio= 2.069957 log bounds ratio= NA
Method: nlminb
0: 2035.0322: 0.555932 0.00473222 0.0518577 0.0496661 0.555932 0.314324 -0.0401190
1: 1125.8726: -0.128002 0.0388608 0.148522 0.127276 -0.0324267 0.721923 -0.0984183
2: 842.16854: -0.894107 0.0450576 0.220895 0.294603 0.0566570 1.33118 -0.124427
3: 801.45277: -1.16971 0.0904748 0.304796 0.364513 -0.181651 1.54390 -0.197683
4: 793.97921: -1.20286 0.147914 0.450047 0.558015 0.0953275 1.78334 -0.178614
5: 780.71673: -1.44471 0.189016 0.538420 0.765386 -0.198236 1.79638 -0.181454
6: 775.40425: -1.51478 0.180762 0.546683 0.747108 -0.0165510 1.79489 -0.229489
7: 774.57448: -1.57276 0.182887 0.545728 0.743597 -0.0714210 1.78130 -0.190647
8: 773.65776: -1.60590 0.198898 0.574778 0.783028 -0.0163173 1.74617 -0.198394
9: 773.45521: -1.67407 0.202747 0.586465 0.808583 -0.0539769 1.72187 -0.223385
10: 773.01515: -1.71678 0.200876 0.598546 0.838030 -0.0208753 1.72345 -0.159004
11: 772.50230: -1.72318 0.206564 0.606078 0.846533 -0.0158345 1.72432 -0.200933
12: 772.18615: -1.78954 0.231404 0.641645 0.890729 -0.0149774 1.74469 -0.180530
13: 771.89407: -1.85032 0.220442 0.644318 0.940353 -0.00765249 1.69664 -0.201119
14: 771.82963: -1.85752 0.227057 0.652232 0.940176 0.00321924 1.70914 -0.192294
15: 771.80804: -1.86904 0.234336 0.658832 0.936483 -0.00922186 1.71128 -0.202710
16: 771.77147: -1.87385 0.245677 0.671660 0.936852 0.000202047 1.70546 -0.194253
17: 771.73610: -1.88837 0.239128 0.666675 0.949032 -0.000940174 1.71251 -0.199984
18: 771.73251: -1.89568 0.239325 0.673717 0.966182 0.00207717 1.70995 -0.190095
19: 771.69796: -1.91188 0.246202 0.678638 0.968550 0.000991727 1.69952 -0.197926
20: 771.68769: -1.91635 0.246152 0.679388 0.968983 0.00465926 1.70560 -0.195221
21: 771.68585: -1.92140 0.245884 0.681944 0.970632 -0.000298807 1.70718 -0.199282
22: 771.67581: -1.92565 0.249540 0.682440 0.975775 0.00323946 1.70555 -0.197016
23: 771.67138: -1.93310 0.248427 0.685083 0.977666 0.00545687 1.70803 -0.197440
24: 771.66787: -1.93884 0.251036 0.686583 0.982438 0.00259472 1.70576 -0.196931
25: 771.66515: -1.94282 0.249059 0.691551 0.986158 0.00554980 1.70248 -0.197777
26: 771.66252: -1.94927 0.253205 0.688497 0.988277 0.00637468 1.70450 -0.196717
27: 771.66179: -1.94943 0.252731 0.690801 0.989858 0.00511422 1.70388 -0.197930
28: 771.66159: -1.95076 0.251950 0.692556 0.990551 0.00589815 1.70452 -0.195798
29: 771.66071: -1.95311 0.252596 0.692511 0.991245 0.00686234 1.70299 -0.197147
30: 771.66048: -1.95352 0.252645 0.692540 0.991439 0.00603419 1.70315 -0.197013
31: 771.66035: -1.95391 0.252809 0.692812 0.992092 0.00645048 1.70345 -0.197051
32: 771.66010: -1.95539 0.253106 0.692932 0.993201 0.00597275 1.70362 -0.197059
33: 771.65989: -1.95682 0.253095 0.693679 0.993688 0.00656203 1.70289 -0.196863
34: 771.65976: -1.95822 0.253576 0.694616 0.994380 0.00638257 1.70290 -0.197323
35: 771.65960: -1.95958 0.253686 0.694374 0.995708 0.00653884 1.70291 -0.197074
36: 771.65960: -1.96080 0.254736 0.695269 0.996154 0.00683515 1.70302 -0.196825
37: 771.65953: -1.96146 0.254391 0.695463 0.996637 0.00680183 1.70275 -0.197052
38: 771.65952: -1.96215 0.254142 0.695673 0.997170 0.00692894 1.70255 -0.196925
39: 771.65951: -1.96220 0.254146 0.695537 0.997255 0.00692672 1.70258 -0.197040
40: 771.65951: -1.96221 0.254149 0.695523 0.997250 0.00689011 1.70258 -0.197017
41: 771.65951: -1.96219 0.254155 0.695486 0.997245 0.00689580 1.70257 -0.197015
42: 771.65951: -1.96217 0.254157 0.695483 0.997240 0.00689316 1.70257 -0.197019
Post processing for method nlminb
Successful convergence!
Compute Hessian approximation at finish of nlminb
Compute gradient approximation at finish of nlminb
Save results from method nlminb
$par
(Intercept) x1 x2 x3 (Intercept) x4
-1.962174116 0.254157295 0.695482511 0.997240458 0.006893159 1.702572353
x5
-0.197019270
$message
[1] "relative convergence (4)"
$convcode
[1] 0
$value
[1] 771.6595
$fevals
function
54
$gevals
gradient
408
$nitns
[1] 42
$kkt1
[1] TRUE
$kkt2
[1] TRUE
$xtimes
user.self
0.66
Assemble the answers
$call
boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5, data = Df,
family = list(binomial("probit")))
$link
$link$a
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$link$b
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$model
$model$submods
$model$submods$a
[1] "x1" "x2" "x3"
$model$submods$b
[1] "x4" "x5"
$model$boolmod
expression((a %|% b))
$N
[1] 2000
$k
[1] 7
$coef.labels
[1] "(Intercept)_a" "x1_a" "x2_a" "x3_a"
[5] "(Intercept)_b" "x4_b" "x5_b"
$coef.idx
$coef.idx$a
[1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE
$coef.idx$b
[1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE
$response
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
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0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1
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1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1
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0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1
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1 0 0 1 1 1 1 1 0 0 1 1 1 0 1 1
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1 0 0 1 1 1 1 0 1 1 0 0 1 0 1 1
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1 1 0 1 1 0 1 1 0 0 1 1 1 0 1 0
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0 0 1 1 0 0 0 0 1 1 0 0 1 1 1 1
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1 0 1 1 0 1 0 0 1 1 0 0 0 0 1 0
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1 1 1 0 1 0 1 0 1 1 0 1 1 1 0 0
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1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1
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1 1 1 0 1 1 0 0 0 1 0 0 0 0 1 1
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0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1
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1 1 0 1 1 1 1 1 0 0 0 1 1 0 1 1
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0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 1
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0 0 0 1 1 1 0 0 0 1 1 1 1 0 0 0
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1 0 1 1 0 1 1 1 0 1 1 1 0 0 1 0
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0 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1
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1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 0
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1 1 1 0 0 0 1 0 1 0 1 0 0 0 1 0
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1 1 1 1 0 1 0 0 1 1 1 1 0 0 0 0
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0 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1
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0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1
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0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 1
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0 1 1 1 0 1 0 1 0 1 1 0 1 1 1 1
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1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0
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1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 0
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0 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
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0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1
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1 0 0 1 1 1 1 0 1 1 0 1 1 1 1 0
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1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 1
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0 0 0 1 1 0 1 1 1 0 0 1 1 0 0 1
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0 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0
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0 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1
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1 0 0 0 1 1 1 1 0 1 0 1 1 1 1 0
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0 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0
1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
1 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0
1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 0
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0 0 1 0 1 1 1 1 1 0 0 1 1 1 0 0
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0 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1
1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0
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1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1
1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 1
1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
0 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0
1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
0 0 1 0 1 0 1 1 0 0 1 0 1 1 0 1
1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
0 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1
1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
0 1 1 0 1 1 0 1 1 0 0 1 1 0 1 1
1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
0 1 1 1 0 0 0 0 0 1 1 0 1 0 1 0
1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0
1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
0 1 0 0 0 0 1 1 0 1 1 0 1 0 0 0
1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 1
1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
1 0 1 1 0 0 1 0 1 1 0 0 0 0 0 1
1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
1 0 0 0 1 1 0 0 1 0 1 1 1 1 0 0
1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
1 0 0 1 1 0 1 0 0 1 0 1 0 0 1 0
1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
1 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0
1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
1 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0
1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456
1 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1
1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472
1 1 1 1 1 0 0 1 0 1 0 1 1 1 0 0
1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488
0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1
1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1
1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520
1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1
1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
1 0 0 1 1 0 0 1 0 0 1 1 1 1 1 0
1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552
1 0 1 1 1 1 1 0 1 0 0 1 1 1 0 0
1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568
0 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0
1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584
0 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1
1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600
1 1 1 1 0 1 0 0 0 0 1 0 1 1 0 1
1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
0 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1
1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632
0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 1
1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648
1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1
1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664
1 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0
1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680
1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0
1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696
1 0 1 1 0 0 1 1 0 0 1 1 1 1 1 1
1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712
1 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0
1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1
1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744
0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0
1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760
0 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1
1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776
0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 1
1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792
1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1
1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808
1 1 1 0 1 0 0 0 1 1 1 0 0 1 0 0
1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824
0 1 1 0 0 1 0 0 1 1 1 1 0 1 1 1
1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840
1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0
1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856
0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 0
1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872
0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 0
1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888
0 1 0 0 1 0 0 0 1 1 0 1 1 1 0 1
1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904
0 0 0 1 1 1 1 1 1 1 0 0 0 1 0 0
1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 0
1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936
1 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0
1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952
1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1
1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968
1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 0
1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
1 1 1 0 1 1 0 1 0 0 0 1 0 1 1 0
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1
$frame
$frame$a
(Intercept) x1 x2 x3
1 1 0.5855288178 0.7094660175 -0.1093033147
2 1 -1.8179559677 0.6300985511 -0.2761841052
3 1 -0.1162478064 1.8173120437 0.3706278643
4 1 0.8168998395 -0.8863575212 -0.3315775899
5 1 0.7796219246 1.4557850825 -0.6443284292
6 1 1.8050975188 -0.4816473637 0.6203798013
7 1 0.8118731786 2.1968335463 2.0491903374
8 1 0.4911882793 -0.3240865787 -1.6620502439
9 1 1.1285108336 -2.3803580614 -1.0602655522
10 1 1.4607294031 -1.4130987779 0.5674032534
11 1 -0.5403860739 1.9476926647 0.0535902704
12 1 0.2779536948 0.6911712729 0.8237953287
13 1 0.1495919806 -1.3425314817 0.5533030755
14 1 -1.8323773062 0.8881394327 1.5934884724
15 1 0.0546155753 -0.7846493732 -1.0493528190
16 1 0.9426008507 0.8262582872 -0.8115404902
17 1 0.6453830742 1.0431435516 -0.3043691127
18 1 1.8670991846 0.6720424690 -0.3079533808
19 1 -0.9639014790 -0.8550825111 1.8869469420
20 1 0.6873321006 -0.5050435175 2.1577198165
21 1 0.2239254075 -1.1562233302 0.4224185278
22 1 -0.5360479985 -0.3116060846 1.5561096426
23 1 -1.2301722469 -1.3240586922 1.2612422744
24 1 -0.5050898063 -0.0521535933 0.6288606251
25 1 1.5448636020 1.3214520172 0.3221515754
26 1 -1.1588210228 -1.8453682894 1.1573252871
27 1 1.6421919908 0.8836548332 0.5248758910
28 1 -1.0479137102 -1.0111225236 0.6689216525
29 1 -1.1402641448 -1.2937152900 -0.5946987691
30 1 0.5401695677 -1.5472919670 0.8496529299
31 1 -1.6193283224 0.5483979230 0.1952821538
32 1 -0.2509466239 1.6993466685 -0.3442987963
33 1 -0.4876385152 0.3031512373 -0.2419740196
34 1 -0.2806491388 0.6330173442 -1.2398183431
35 1 0.1999204838 1.3471927776 0.0360734878
36 1 0.4809501553 2.4835500893 0.4013649875
37 1 -0.9117394242 -0.0490446909 -0.4053874767
38 1 0.0764175190 1.4537473512 0.3741210834
39 1 0.5435221079 -0.5051860029 0.7867957894
40 1 0.7984337703 0.8508604376 -0.4435679681
41 1 -1.4361457062 -0.6292596473 0.2435217656
42 1 0.1052118160 -1.7417130945 0.6452469938
43 1 0.9919506777 -0.8592507612 -0.2815796883
44 1 0.3156128247 0.6602933781 -1.7222024071
45 1 0.8678217426 -2.2900441795 -0.1501902882
46 1 0.6722680417 -0.2093011363 0.0121825061
47 1 0.0784375327 -0.7792610704 0.1665596687
48 1 -0.4678883695 0.7583745568 -0.6417363578
49 1 -0.7000758167 -0.5674015851 -0.2613939259
50 1 0.7711037397 2.7474035422 -0.0839347571
51 1 -0.8086741277 1.0011198474 0.4560525046
52 1 0.6417691636 -0.4150210287 -0.4595756762
53 1 0.7108899984 1.2676017522 -0.1431510558
54 1 -0.1625889099 0.0417091744 0.4830398961
55 1 -0.6346498942 -0.7019630314 0.5768503789
56 1 1.1471271903 0.0147936486 -0.3117392439
57 1 -1.5138640797 0.1642809977 -0.8708652186
58 1 0.3573696852 0.1023930632 -0.6752668252
59 1 -0.4282856231 -0.7139247637 -0.1903840656
60 1 0.1837369095 -2.1503105297 -0.6229665257
61 1 0.5222821685 0.0097937599 -0.4405261962
62 1 0.0382097874 1.1948056324 0.3439583521
63 1 -0.9180582028 -0.0878073286 1.3202937198
64 1 -0.3157324793 -0.5750961042 -1.4063564356
65 1 0.3803156970 0.6051367728 1.0196741368
66 1 0.9331921670 0.4754354212 0.3902816000
67 1 1.4239835987 0.4847305112 0.3492364459
68 1 0.3670449214 -1.5191990495 1.5498033320
69 1 2.0767099333 -0.3076505656 0.9523708852
70 1 -0.1420633784 -1.1830257110 0.5453184095
71 1 0.2929403027 -0.0867079053 -1.4663594955
72 1 -0.3603208006 0.3505937650 0.0282576562
73 1 -0.3758234363 -1.8128337643 0.2886002111
74 1 0.6504302424 0.3102549854 1.6683571689
75 1 -0.1460263920 1.7014318317 0.4713578806
76 1 -0.7789022252 1.1633248478 -1.9645441952
77 1 -0.4772722914 -0.1025805088 0.3686961762
78 1 -0.1505570976 0.9042435562 2.2420361375
79 1 0.7982513947 0.4981721634 0.1195602234
80 1 0.2627041248 0.6407388780 0.3070898308
81 1 0.6279651131 0.0021439509 0.2843777227
82 1 0.8281942395 -0.0848186457 -0.4347199169
83 1 -0.9712292030 -0.1609133622 1.2046870348
84 1 -0.0199197380 0.3280164800 -0.9306286167
85 1 0.5142421022 0.2931683808 -0.5626548305
86 1 -0.4300125693 1.3319083517 -0.7632671561
87 1 -0.8225400839 -0.0476483565 0.1706780107
88 1 0.4906677770 2.2391260841 0.8922123090
89 1 0.3942498996 0.6325214944 0.1025911025
90 1 0.5008706583 -0.9094679509 -2.1972085120
91 1 0.7130781039 1.0282685391 -0.5706327254
92 1 1.2035448160 1.4961826970 -0.1565984322
93 1 -1.2861886466 -0.2725228551 0.1569148369
94 1 -0.8552506726 -1.0057287350 -0.9139523908
95 1 1.2996376499 -0.0293687832 -2.2366218376
96 1 -0.4975791431 -0.6010274043 -0.5121978141
97 1 -0.0240353973 -0.1273903376 -0.1318856204
98 1 0.2109307870 0.7523360895 0.1383795467
99 1 -1.1627837168 0.8546585194 -0.2420958786
100 1 0.2241698326 -0.7368249216 -0.8467107405
101 1 -1.4203238585 -2.4669386469 0.4847158355
102 1 -0.1629454633 0.2204557848 0.8762108117
103 1 -1.9984928459 -1.2347803996 0.8463037177
104 1 0.7674546604 0.8671015019 -1.2545811490
105 1 0.4130208684 0.5642562609 0.7260659521
106 1 0.2242920505 0.7864091404 -0.4233628372
107 1 -0.1835378759 0.8348719973 -0.5872005112
108 1 0.6454110524 -0.3123199556 1.1854482829
109 1 2.1274630349 -0.1877064794 0.0987578449
110 1 2.0930679948 -0.6922359279 -2.2871981109
111 1 -0.4712225866 -0.0332096774 0.3773670678
112 1 2.1185039306 -1.0874598695 1.2160333866
113 1 0.1128439417 0.0652135748 -0.6306272111
114 1 -0.6816232013 -0.0371627219 -0.2872743152
115 1 0.8366902458 -0.3298962638 0.5348022561
116 1 0.5761647328 1.2362238791 0.4755396380
117 1 0.6800620909 -1.3502015698 0.2451024445
118 1 0.2013818061 -1.5754628329 0.0235387774
119 1 -0.5269106809 -1.3128588906 0.8008613463
120 1 1.6818084980 -0.2263103959 -0.1979290319
121 1 -1.6366290973 0.2115625932 -0.4648317284
122 1 -1.3217017095 0.0766927813 -0.3849908794
123 1 -0.4582940078 0.2288621447 -1.3964076285
124 1 0.8028908422 -0.0605252613 0.0893286191
125 1 1.4491694254 0.2055104719 -0.0561199677
126 1 -0.0645002911 0.1324295959 0.0347150846
127 1 -1.0427231529 -0.2136226802 1.7779470769
128 1 -0.6131604214 0.4959067804 0.2456385863
129 1 1.6343509612 0.9754357644 1.0840779376
130 1 1.3355214732 -0.1555622761 0.9266084982
131 1 -0.8186466261 -1.2368228612 2.3172256458
132 1 -1.8112897399 -1.1451476862 1.2808728222
133 1 -1.3686015604 0.5439404116 -1.2380301684
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135 1 -0.3843675980 -1.4289357321 0.9634176892
136 1 -1.8070446596 1.3554384433 0.4456801661
137 1 -0.6761129137 1.1360496515 2.1693321275
138 1 1.3026361736 -2.7783255103 0.3413115606
139 1 1.6333026798 -1.8696215131 -0.2500888468
140 1 -2.1379097749 -0.5772610475 0.2364494516
141 1 2.3036270054 2.0208958969 -0.0578785187
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143 1 -0.0053473432 -0.5330032870 -0.5154151412
144 1 0.9979542440 -1.1631892607 1.6808938377
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148 1 0.3479534670 -1.8995713794 -0.0576824160
149 1 -0.9607436927 -0.8048246106 -0.4959467622
150 1 1.3954864903 3.0936966283 1.9008256312
151 1 0.4617653862 0.0972093387 0.6760255642
152 1 -0.6965953854 -1.2771354355 -0.0924237284
153 1 -1.0308001566 -1.3195143547 -0.0842057018
154 1 0.8633075423 0.9693708778 0.6141290402
155 1 1.8748716168 -0.2375574567 -0.4527795383
156 1 0.8342015841 0.9258648155 0.9431551521
157 1 0.2198254203 -1.0052373893 0.3731011636
158 1 1.0048154415 -0.3738294199 0.9361085041
159 1 0.6292445983 1.0372345783 0.8706170692
160 1 0.2394069412 -1.2561203802 0.2235698835
161 1 -0.8174921146 -0.2492658717 0.4629986479
162 1 1.0764874344 1.1492957868 -1.6221232769
163 1 -0.7405849691 0.2524539178 -0.3369977389
164 1 0.6830537666 0.0783915682 0.5333680907
165 1 0.9401879679 -0.6636770743 -0.8424122326
166 1 0.6504298701 -0.6983808622 -0.4375006119
167 1 0.8969177714 -0.6397434859 -0.0761318178
168 1 -1.9884849273 0.6315952724 -1.9441453638
169 1 0.8878925317 -1.1031640614 -0.3026337057
170 1 0.2574523837 -2.1737384365 0.0261676793
171 1 0.2638040223 0.8894580105 -0.5316916219
172 1 0.8050696694 -0.8247414704 0.6140076455
173 1 -1.0362756757 -2.0581829719 -1.1837015813
174 1 1.7072109072 -0.4014458473 -0.0780482412
175 1 0.2056696111 0.4201750615 1.0637149265
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213 1 -0.8410156594 -0.5255775617 0.9646760715
214 1 -1.0594503837 -0.2031988588 -0.2605225022
215 1 2.4570741656 -1.4214051662 -1.1706711902
216 1 -0.0336040265 1.0677884112 -1.0843643358
217 1 -0.8257088759 0.4902205365 -0.9265318611
218 1 -0.3655132661 -0.0063319208 -2.3805307769
219 1 -0.4840289041 -0.9261609666 -0.2859256147
220 1 1.2062978792 -0.0248296420 -0.9045873519
221 1 1.1480913567 0.4550137293 2.0219463843
222 1 1.3517073357 0.4395145976 1.5716217015
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224 1 0.9666831878 -0.4204558998 0.7782147447
225 1 -1.5574704016 -0.5484079137 0.2172601824
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229 1 -2.1995596199 0.1022425497 -1.7009442521
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1930 1 -2.771234e-01 0.0987148898
1931 1 -2.123883e-01 -0.7708787733
1932 1 1.524831e+00 -0.3909979096
1933 1 1.754834e-01 -0.3555536583
1934 1 -1.893939e+00 -0.1499947922
1935 1 -7.669144e-01 1.3458038956
1936 1 -2.528206e-01 1.3359909556
1937 1 8.316751e-01 0.3266016616
1938 1 1.010643e+00 -1.0517406052
1939 1 1.796983e+00 -0.5284336240
1940 1 7.229651e-01 -1.1143800298
1941 1 4.178250e-01 -0.0049648993
1942 1 1.196199e-01 0.4954238593
1943 1 1.825934e+00 1.9632261086
1944 1 -1.567389e+00 0.0977832811
1945 1 2.665720e-01 -1.3769637483
1946 1 -2.851051e-02 -0.1787249146
1947 1 7.168300e-01 2.4089706463
1948 1 9.548940e-02 1.1836679379
1949 1 -1.699918e-01 -0.1629387394
1950 1 7.732266e-01 0.4788108005
1951 1 1.055910e+00 1.4768883954
1952 1 1.301988e+00 1.2483121552
1953 1 -5.629535e-01 1.8736015682
1954 1 -1.046239e+00 -1.4697312412
1955 1 7.296283e-01 0.3759873430
1956 1 -1.134532e+00 -0.7568702574
1957 1 -2.730316e+00 0.7899478570
1958 1 6.522059e-01 -0.7758246883
1959 1 -9.077401e-01 1.7400984741
1960 1 -1.746243e-01 0.7273764157
1961 1 -1.749175e+00 0.0412251647
1962 1 6.351639e-01 -0.7465530475
1963 1 3.928764e-01 -0.4669105152
1964 1 1.469288e-01 0.5619024171
1965 1 -4.416916e-02 0.9213398038
1966 1 5.095332e-01 1.3139610919
1967 1 1.036623e+00 -0.6321055477
1968 1 -1.017870e+00 1.1922587748
1969 1 1.540566e+00 -0.2953318428
1970 1 1.905289e+00 -0.7622792482
1971 1 4.042105e-01 0.5947329044
1972 1 -7.069767e-01 1.5472486383
1973 1 -1.008285e+00 0.6816846166
1974 1 4.863330e-01 -0.0116558350
1975 1 -7.022979e-01 0.7742367691
1976 1 3.093437e-01 0.2707596966
1977 1 -1.078647e+00 -0.1883596530
1978 1 5.902743e-01 0.6246672753
1979 1 6.584944e-01 1.4193192881
1980 1 2.048185e+00 -0.9067758246
1981 1 -1.448530e+00 -0.3987914948
1982 1 1.267581e+00 1.4135953220
1983 1 -6.959804e-01 0.2584286770
1984 1 7.405956e-02 -1.2725183814
1985 1 4.446582e-02 -0.0753325430
1986 1 -4.317039e-01 0.0703880778
1987 1 1.688329e+00 -0.7760323143
1988 1 1.932779e-01 -1.6246009465
1989 1 2.666542e-01 0.8940843318
1990 1 -7.428853e-01 1.4278914745
1991 1 2.081491e-01 1.8593265851
1992 1 -8.529617e-01 -0.3979564585
1993 1 9.235143e-01 0.4129440992
1994 1 1.354395e+00 -0.2755167794
1995 1 5.697907e-01 0.8813467819
1996 1 -2.468826e-02 1.6829380542
1997 1 6.948899e-01 0.6945449748
1998 1 -7.752261e-02 0.5096265680
1999 1 4.085734e-01 0.4243439554
2000 1 -1.587949e+00 0.0181455085
attr(,"assign")
[1] 0 1 2
$calc_lik
function (beta)
{
for (n in names(obj$model$submods)) assign(n, eval(get(n)))
p <- eval(obj$model$boolmod)
log.p <- log(p)
log.p1 <- log(1 - p)
eval(llik)
}
<bytecode: 0x1495300>
<environment: 0x4c9fa38>
$method
[1] "nlminb"
$model.fit
$nlminb
$nlminb$par
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlminb -1.962174 0.2541573 0.6954825 0.9972405 0.006893159 1.702572
x5
nlminb -0.1970193
$nlminb$nhatend
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 115.457214 31.281924 63.6547716 100.2494647 95.25488 -21.8659765
[2,] 31.281924 117.944232 1.8807743 19.3257198 10.65405 -5.9115098
[3,] 63.654772 1.880774 134.6346070 31.1730783 39.15753 -0.5340214
[4,] 100.249465 19.325720 31.1730783 145.7170857 55.20325 -7.2822566
[5,] 95.254883 10.654048 39.1575254 55.2032529 573.49201 37.1084645
[6,] -21.865976 -5.911510 -0.5340214 -7.2822566 37.10846 171.9634863
[7,] -2.522868 -7.597161 -2.7946852 0.8885202 10.58556 38.7621375
[,7]
[1,] -2.5228681
[2,] -7.5971613
[3,] -2.7946852
[4,] 0.8885202
[5,] 10.5855587
[6,] 38.7621375
[7,] 559.8837337
$nlminb$value
[1] -771.6595
$nlminb$iterations
[1] 42
$nlminb$conv
[1] 0
$nlminb$detail
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlminb -1.962174 0.2541573 0.6954825 0.9972405 0.006893159 1.702572
x5 value fevals gevals niter convcode kkt1 kkt2 xtimes
nlminb -0.1970193 771.6595 54 408 42 0 TRUE TRUE 0.66
attr(,"class")
[1] "boolfit" "list"
attr(,"class")
[1] "boolean"
>
> ## Multiple optimizers can be specified in a single call to boolean.
> ## Here we fit with the nlm and nlminb optimizers.
> (fit1.OR <- boolean(mod.OR, method=c("nlm", "nlminb")))
$call
boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5, data = Df,
family = list(binomial("probit")))
$link
$link$a
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$link$b
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$model
$model$submods
$model$submods$a
[1] "x1" "x2" "x3"
$model$submods$b
[1] "x4" "x5"
$model$boolmod
expression((a %|% b))
$N
[1] 2000
$k
[1] 7
$coef.labels
[1] "(Intercept)_a" "x1_a" "x2_a" "x3_a"
[5] "(Intercept)_b" "x4_b" "x5_b"
$coef.idx
$coef.idx$a
[1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE
$coef.idx$b
[1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE
$response
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
1 1 1 0 0 0 1 1 1 0 0 1 0 1 0 1
33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
0 1 1 1 1 0 0 1 1 1 1 0 0 1 0 1
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1
65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
1 1 1 1 1 0 0 1 1 1 1 1 0 1 0 0
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0
97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
1 1 0 1 0 0 0 0 0 0 1 1 1 0 1 1
113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
1 0 0 0 0 1 0 1 0 1 1 1 0 0 1 1
129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1
145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
0 1 1 0 0 1 0 1 1 1 0 1 1 1 0 1
161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1
177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
1 1 0 1 1 0 1 1 1 1 1 0 0 0 0 0
193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
1 0 1 1 1 0 0 0 0 1 0 1 1 1 0 1
209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
0 1 0 0 1 0 1 1 1 1 1 0 1 0 1 1
225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0
241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
1 1 1 1 0 0 1 0 1 0 0 0 0 0 1 0
257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
1 0 1 1 0 1 0 1 1 0 1 0 1 0 1 0
273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1
289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304
1 1 0 0 1 1 1 1 0 1 0 0 1 1 1 0
305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320
1 1 1 0 0 1 0 0 0 0 0 0 1 1 0 1
321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
0 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1
337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352
0 1 0 1 1 0 1 1 1 1 0 1 1 1 1 0
353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
1 1 1 1 1 1 1 0 1 0 1 1 0 0 0 0
369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384
1 0 0 0 1 1 0 1 0 0 1 1 1 0 1 0
385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1
401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416
1 0 0 1 1 1 1 1 0 0 1 1 1 0 1 1
417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1
433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448
1 0 0 1 1 1 1 0 1 1 0 0 1 0 1 1
449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464
1 1 0 1 1 0 1 1 0 0 1 1 1 0 1 0
465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 1
481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
1 0 1 1 1 1 0 0 1 0 1 0 0 0 0 1
497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
0 0 1 1 0 0 0 0 1 1 0 0 1 1 1 1
513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
1 0 1 1 0 1 0 0 1 1 0 0 0 0 1 0
529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
0 1 0 1 0 0 0 1 1 0 1 1 1 1 0 1
545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560
1 1 1 0 1 1 0 1 1 1 1 1 0 0 0 1
561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
1 1 1 1 0 1 1 0 1 0 1 0 1 0 0 0
577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592
1 1 1 0 1 0 1 0 1 1 0 1 1 1 0 0
593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608
1 1 1 0 0 0 1 1 0 0 1 1 0 1 0 0
609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
1 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0
625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640
0 1 0 1 1 0 1 1 0 1 0 0 0 1 0 1
641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1
657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672
1 1 1 0 1 1 0 0 0 1 0 0 0 0 1 1
673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
0 0 1 0 1 1 1 0 1 1 0 1 1 0 0 0
689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704
0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720
0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1
721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
1 1 0 1 1 1 1 1 0 0 0 1 1 0 1 1
737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752
0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 1
753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
1 0 1 1 0 1 1 1 0 1 1 1 0 0 0 0
769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784
0 0 0 1 1 1 0 0 0 1 1 1 1 0 0 0
785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
1 0 1 1 0 1 1 1 0 1 1 1 0 0 1 0
801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
0 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1
817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 0
833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848
1 1 1 0 0 0 1 0 1 0 1 0 0 0 1 0
849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864
1 1 1 1 0 1 0 0 1 1 1 1 0 0 0 0
865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880
0 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1
881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896
0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1
897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912
0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 1
913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
0 0 0 1 1 0 1 1 0 0 0 1 0 0 1 1
929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944
0 1 1 1 0 1 0 1 0 1 1 0 1 1 1 1
945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0
961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 0
977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
0 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1
1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
1 0 0 1 1 1 1 0 1 1 0 1 1 1 1 0
1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 1
1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
0 0 0 1 1 0 1 1 1 0 0 1 1 0 0 1
1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
0 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0
1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088
0 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1
1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
1 0 0 0 1 1 1 1 0 1 0 1 1 1 1 0
1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
0 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0
1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
1 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0
1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 0
1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168
0 0 1 0 1 1 1 1 1 0 0 1 1 1 0 0
1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
0 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1
1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0
1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216
1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1
1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 1
1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
0 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0
1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
0 0 1 0 1 0 1 1 0 0 1 0 1 1 0 1
1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
0 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1
1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
0 1 1 0 1 1 0 1 1 0 0 1 1 0 1 1
1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
0 1 1 1 0 0 0 0 0 1 1 0 1 0 1 0
1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0
1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
0 1 0 0 0 0 1 1 0 1 1 0 1 0 0 0
1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 1
1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
1 0 1 1 0 0 1 0 1 1 0 0 0 0 0 1
1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
1 0 0 0 1 1 0 0 1 0 1 1 1 1 0 0
1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
1 0 0 1 1 0 1 0 0 1 0 1 0 0 1 0
1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
1 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0
1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
1 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0
1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456
1 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1
1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472
1 1 1 1 1 0 0 1 0 1 0 1 1 1 0 0
1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488
0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1
1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1
1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520
1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1
1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
1 0 0 1 1 0 0 1 0 0 1 1 1 1 1 0
1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552
1 0 1 1 1 1 1 0 1 0 0 1 1 1 0 0
1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568
0 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0
1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584
0 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1
1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600
1 1 1 1 0 1 0 0 0 0 1 0 1 1 0 1
1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
0 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1
1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632
0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 1
1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648
1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1
1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664
1 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0
1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680
1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0
1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696
1 0 1 1 0 0 1 1 0 0 1 1 1 1 1 1
1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712
1 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0
1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1
1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744
0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0
1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760
0 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1
1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776
0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 1
1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792
1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1
1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808
1 1 1 0 1 0 0 0 1 1 1 0 0 1 0 0
1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824
0 1 1 0 0 1 0 0 1 1 1 1 0 1 1 1
1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840
1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0
1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856
0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 0
1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872
0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 0
1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888
0 1 0 0 1 0 0 0 1 1 0 1 1 1 0 1
1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904
0 0 0 1 1 1 1 1 1 1 0 0 0 1 0 0
1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 0
1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936
1 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0
1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952
1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1
1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968
1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 0
1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
1 1 1 0 1 1 0 1 0 0 0 1 0 1 1 0
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1
$frame
$frame$a
(Intercept) x1 x2 x3
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3 1 -0.1162478064 1.8173120437 0.3706278643
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1956 1 -1.134532e+00 -0.7568702574
1957 1 -2.730316e+00 0.7899478570
1958 1 6.522059e-01 -0.7758246883
1959 1 -9.077401e-01 1.7400984741
1960 1 -1.746243e-01 0.7273764157
1961 1 -1.749175e+00 0.0412251647
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1963 1 3.928764e-01 -0.4669105152
1964 1 1.469288e-01 0.5619024171
1965 1 -4.416916e-02 0.9213398038
1966 1 5.095332e-01 1.3139610919
1967 1 1.036623e+00 -0.6321055477
1968 1 -1.017870e+00 1.1922587748
1969 1 1.540566e+00 -0.2953318428
1970 1 1.905289e+00 -0.7622792482
1971 1 4.042105e-01 0.5947329044
1972 1 -7.069767e-01 1.5472486383
1973 1 -1.008285e+00 0.6816846166
1974 1 4.863330e-01 -0.0116558350
1975 1 -7.022979e-01 0.7742367691
1976 1 3.093437e-01 0.2707596966
1977 1 -1.078647e+00 -0.1883596530
1978 1 5.902743e-01 0.6246672753
1979 1 6.584944e-01 1.4193192881
1980 1 2.048185e+00 -0.9067758246
1981 1 -1.448530e+00 -0.3987914948
1982 1 1.267581e+00 1.4135953220
1983 1 -6.959804e-01 0.2584286770
1984 1 7.405956e-02 -1.2725183814
1985 1 4.446582e-02 -0.0753325430
1986 1 -4.317039e-01 0.0703880778
1987 1 1.688329e+00 -0.7760323143
1988 1 1.932779e-01 -1.6246009465
1989 1 2.666542e-01 0.8940843318
1990 1 -7.428853e-01 1.4278914745
1991 1 2.081491e-01 1.8593265851
1992 1 -8.529617e-01 -0.3979564585
1993 1 9.235143e-01 0.4129440992
1994 1 1.354395e+00 -0.2755167794
1995 1 5.697907e-01 0.8813467819
1996 1 -2.468826e-02 1.6829380542
1997 1 6.948899e-01 0.6945449748
1998 1 -7.752261e-02 0.5096265680
1999 1 4.085734e-01 0.4243439554
2000 1 -1.587949e+00 0.0181455085
attr(,"assign")
[1] 0 1 2
$calc_lik
function (beta)
{
for (n in names(obj$model$submods)) assign(n, eval(get(n)))
p <- eval(obj$model$boolmod)
log.p <- log(p)
log.p1 <- log(1 - p)
eval(llik)
}
<bytecode: 0x1495300>
<environment: 0x4c9fa38>
$method
[1] "nlm" "nlminb"
$model.fit
$nlm
$nlm$par
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlm -1.962176 0.2541561 0.6954833 0.9972418 0.006893525 1.702574
x5
nlm -0.197018
$nlm$nhatend
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 115.456997 31.281691 63.6547636 100.2494259 95.25477 -21.8660312
[2,] 31.281691 117.943969 1.8807256 19.3256601 10.65402 -5.9115476
[3,] 63.654764 1.880726 134.6344701 31.1731459 39.15750 -0.5340373
[4,] 100.249426 19.325660 31.1731459 145.7169853 55.20325 -7.2822927
[5,] 95.254774 10.654019 39.1575019 55.2032487 573.49187 37.1083998
[6,] -21.866031 -5.911548 -0.5340373 -7.2822927 37.10840 171.9631686
[7,] -2.522996 -7.597149 -2.7947502 0.8884992 10.58570 38.7618385
[,7]
[1,] -2.5229956
[2,] -7.5971495
[3,] -2.7947502
[4,] 0.8884992
[5,] 10.5857040
[6,] 38.7618385
[7,] 559.8836759
$nlm$value
[1] -771.6595
$nlm$iterations
[1] 24
$nlm$conv
[1] 0
$nlm$detail
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlm -1.962176 0.2541561 0.6954833 0.9972418 0.006893525 1.702574
nlminb -1.962174 0.2541573 0.6954825 0.9972405 0.006893159 1.702572
x5 value fevals gevals niter convcode kkt1 kkt2 xtimes
nlm -0.1970180 771.6595 NA NA 24 0 TRUE TRUE 0.297
nlminb -0.1970193 771.6595 54 408 42 0 TRUE TRUE 0.660
$nlminb
$nlminb$par
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlminb -1.962174 0.2541573 0.6954825 0.9972405 0.006893159 1.702572
x5
nlminb -0.1970193
$nlminb$nhatend
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 115.457214 31.281924 63.6547716 100.2494647 95.25488 -21.8659765
[2,] 31.281924 117.944232 1.8807743 19.3257198 10.65405 -5.9115098
[3,] 63.654772 1.880774 134.6346070 31.1730783 39.15753 -0.5340214
[4,] 100.249465 19.325720 31.1730783 145.7170857 55.20325 -7.2822566
[5,] 95.254883 10.654048 39.1575254 55.2032529 573.49201 37.1084645
[6,] -21.865976 -5.911510 -0.5340214 -7.2822566 37.10846 171.9634863
[7,] -2.522868 -7.597161 -2.7946852 0.8885202 10.58556 38.7621375
[,7]
[1,] -2.5228681
[2,] -7.5971613
[3,] -2.7946852
[4,] 0.8885202
[5,] 10.5855587
[6,] 38.7621375
[7,] 559.8837337
$nlminb$value
[1] -771.6595
$nlminb$iterations
[1] 42
$nlminb$conv
[1] 0
$nlminb$detail
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlm -1.962176 0.2541561 0.6954833 0.9972418 0.006893525 1.702574
nlminb -1.962174 0.2541573 0.6954825 0.9972405 0.006893159 1.702572
x5 value fevals gevals niter convcode kkt1 kkt2 xtimes
nlm -0.1970180 771.6595 NA NA 24 0 TRUE TRUE 0.297
nlminb -0.1970193 771.6595 54 408 42 0 TRUE TRUE 0.660
attr(,"class")
[1] "boolfit" "list"
attr(,"class")
[1] "boolean"
>
> ## Re-fit, with BFGS and a higher maximum number of iterations. All
> ## of the options that go along with nlm(), optim(), and genoud() should
> ## be transparently passable through boolean.
> (fit2.OR <- boolean(mod.OR, method="BFGS", control = list(maxit = 500)))
$call
boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5, data = Df,
family = list(binomial("probit")))
$link
$link$a
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$link$b
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$model
$model$submods
$model$submods$a
[1] "x1" "x2" "x3"
$model$submods$b
[1] "x4" "x5"
$model$boolmod
expression((a %|% b))
$N
[1] 2000
$k
[1] 7
$coef.labels
[1] "(Intercept)_a" "x1_a" "x2_a" "x3_a"
[5] "(Intercept)_b" "x4_b" "x5_b"
$coef.idx
$coef.idx$a
[1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE
$coef.idx$b
[1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE
$response
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
1 1 1 0 0 0 1 1 1 0 0 1 0 1 0 1
33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
0 1 1 1 1 0 0 1 1 1 1 0 0 1 0 1
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1
65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
1 1 1 1 1 0 0 1 1 1 1 1 0 1 0 0
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0
97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
1 1 0 1 0 0 0 0 0 0 1 1 1 0 1 1
113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
1 0 0 0 0 1 0 1 0 1 1 1 0 0 1 1
129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1
145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
0 1 1 0 0 1 0 1 1 1 0 1 1 1 0 1
161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1
177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
1 1 0 1 1 0 1 1 1 1 1 0 0 0 0 0
193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
1 0 1 1 1 0 0 0 0 1 0 1 1 1 0 1
209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
0 1 0 0 1 0 1 1 1 1 1 0 1 0 1 1
225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0
241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
1 1 1 1 0 0 1 0 1 0 0 0 0 0 1 0
257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
1 0 1 1 0 1 0 1 1 0 1 0 1 0 1 0
273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1
289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304
1 1 0 0 1 1 1 1 0 1 0 0 1 1 1 0
305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320
1 1 1 0 0 1 0 0 0 0 0 0 1 1 0 1
321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
0 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1
337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352
0 1 0 1 1 0 1 1 1 1 0 1 1 1 1 0
353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
1 1 1 1 1 1 1 0 1 0 1 1 0 0 0 0
369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384
1 0 0 0 1 1 0 1 0 0 1 1 1 0 1 0
385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1
401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416
1 0 0 1 1 1 1 1 0 0 1 1 1 0 1 1
417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1
433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448
1 0 0 1 1 1 1 0 1 1 0 0 1 0 1 1
449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464
1 1 0 1 1 0 1 1 0 0 1 1 1 0 1 0
465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 1
481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
1 0 1 1 1 1 0 0 1 0 1 0 0 0 0 1
497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
0 0 1 1 0 0 0 0 1 1 0 0 1 1 1 1
513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
1 0 1 1 0 1 0 0 1 1 0 0 0 0 1 0
529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
0 1 0 1 0 0 0 1 1 0 1 1 1 1 0 1
545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560
1 1 1 0 1 1 0 1 1 1 1 1 0 0 0 1
561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
1 1 1 1 0 1 1 0 1 0 1 0 1 0 0 0
577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592
1 1 1 0 1 0 1 0 1 1 0 1 1 1 0 0
593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608
1 1 1 0 0 0 1 1 0 0 1 1 0 1 0 0
609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
1 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0
625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640
0 1 0 1 1 0 1 1 0 1 0 0 0 1 0 1
641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1
657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672
1 1 1 0 1 1 0 0 0 1 0 0 0 0 1 1
673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
0 0 1 0 1 1 1 0 1 1 0 1 1 0 0 0
689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704
0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720
0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1
721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
1 1 0 1 1 1 1 1 0 0 0 1 1 0 1 1
737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752
0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 1
753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
1 0 1 1 0 1 1 1 0 1 1 1 0 0 0 0
769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784
0 0 0 1 1 1 0 0 0 1 1 1 1 0 0 0
785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
1 0 1 1 0 1 1 1 0 1 1 1 0 0 1 0
801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
0 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1
817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 0
833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848
1 1 1 0 0 0 1 0 1 0 1 0 0 0 1 0
849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864
1 1 1 1 0 1 0 0 1 1 1 1 0 0 0 0
865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880
0 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1
881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896
0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1
897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912
0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 1
913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
0 0 0 1 1 0 1 1 0 0 0 1 0 0 1 1
929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944
0 1 1 1 0 1 0 1 0 1 1 0 1 1 1 1
945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0
961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 0
977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
0 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1
1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
1 0 0 1 1 1 1 0 1 1 0 1 1 1 1 0
1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 1
1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
0 0 0 1 1 0 1 1 1 0 0 1 1 0 0 1
1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
0 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0
1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088
0 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1
1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
1 0 0 0 1 1 1 1 0 1 0 1 1 1 1 0
1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
0 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0
1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
1 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0
1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 0
1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168
0 0 1 0 1 1 1 1 1 0 0 1 1 1 0 0
1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
0 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1
1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0
1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216
1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1
1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 1
1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
0 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0
1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
0 0 1 0 1 0 1 1 0 0 1 0 1 1 0 1
1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
0 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1
1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
0 1 1 0 1 1 0 1 1 0 0 1 1 0 1 1
1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
0 1 1 1 0 0 0 0 0 1 1 0 1 0 1 0
1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0
1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
0 1 0 0 0 0 1 1 0 1 1 0 1 0 0 0
1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 1
1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
1 0 1 1 0 0 1 0 1 1 0 0 0 0 0 1
1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
1 0 0 0 1 1 0 0 1 0 1 1 1 1 0 0
1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
1 0 0 1 1 0 1 0 0 1 0 1 0 0 1 0
1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
1 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0
1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
1 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0
1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456
1 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1
1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472
1 1 1 1 1 0 0 1 0 1 0 1 1 1 0 0
1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488
0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1
1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1
1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520
1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1
1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
1 0 0 1 1 0 0 1 0 0 1 1 1 1 1 0
1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552
1 0 1 1 1 1 1 0 1 0 0 1 1 1 0 0
1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568
0 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0
1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584
0 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1
1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600
1 1 1 1 0 1 0 0 0 0 1 0 1 1 0 1
1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
0 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1
1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632
0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 1
1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648
1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1
1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664
1 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0
1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680
1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0
1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696
1 0 1 1 0 0 1 1 0 0 1 1 1 1 1 1
1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712
1 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0
1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1
1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744
0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0
1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760
0 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1
1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776
0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 1
1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792
1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1
1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808
1 1 1 0 1 0 0 0 1 1 1 0 0 1 0 0
1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824
0 1 1 0 0 1 0 0 1 1 1 1 0 1 1 1
1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840
1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0
1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856
0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 0
1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872
0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 0
1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888
0 1 0 0 1 0 0 0 1 1 0 1 1 1 0 1
1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904
0 0 0 1 1 1 1 1 1 1 0 0 0 1 0 0
1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 0
1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936
1 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0
1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952
1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1
1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968
1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 0
1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
1 1 1 0 1 1 0 1 0 0 0 1 0 1 1 0
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1
$frame
$frame$a
(Intercept) x1 x2 x3
1 1 0.5855288178 0.7094660175 -0.1093033147
2 1 -1.8179559677 0.6300985511 -0.2761841052
3 1 -0.1162478064 1.8173120437 0.3706278643
4 1 0.8168998395 -0.8863575212 -0.3315775899
5 1 0.7796219246 1.4557850825 -0.6443284292
6 1 1.8050975188 -0.4816473637 0.6203798013
7 1 0.8118731786 2.1968335463 2.0491903374
8 1 0.4911882793 -0.3240865787 -1.6620502439
9 1 1.1285108336 -2.3803580614 -1.0602655522
10 1 1.4607294031 -1.4130987779 0.5674032534
11 1 -0.5403860739 1.9476926647 0.0535902704
12 1 0.2779536948 0.6911712729 0.8237953287
13 1 0.1495919806 -1.3425314817 0.5533030755
14 1 -1.8323773062 0.8881394327 1.5934884724
15 1 0.0546155753 -0.7846493732 -1.0493528190
16 1 0.9426008507 0.8262582872 -0.8115404902
17 1 0.6453830742 1.0431435516 -0.3043691127
18 1 1.8670991846 0.6720424690 -0.3079533808
19 1 -0.9639014790 -0.8550825111 1.8869469420
20 1 0.6873321006 -0.5050435175 2.1577198165
21 1 0.2239254075 -1.1562233302 0.4224185278
22 1 -0.5360479985 -0.3116060846 1.5561096426
23 1 -1.2301722469 -1.3240586922 1.2612422744
24 1 -0.5050898063 -0.0521535933 0.6288606251
25 1 1.5448636020 1.3214520172 0.3221515754
26 1 -1.1588210228 -1.8453682894 1.1573252871
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1800 1 -1.143037e+00 1.9002917475
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1820 1 3.700284e-01 0.0806034912
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1823 1 1.180620e+00 1.5151556945
1824 1 1.582217e+00 0.6093844087
1825 1 1.704888e+00 0.3980028120
1826 1 5.648630e-01 -0.0242636963
1827 1 -4.764913e-01 0.0420294559
1828 1 6.199924e-01 1.4681257278
1829 1 -3.674056e-01 -1.5581161430
1830 1 -5.291112e-01 0.7345380436
1831 1 -5.214788e-01 0.5091522708
1832 1 -8.675957e-01 -1.2417965638
1833 1 9.280934e-01 -0.6573551916
1834 1 -3.513386e-01 1.9991006433
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1836 1 5.343666e-01 0.7339604213
1837 1 8.233797e-02 0.4251411363
1838 1 2.119009e-01 -0.8518324261
1839 1 9.254859e-02 1.8774709870
1840 1 -6.365326e-01 1.5379707312
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1842 1 1.362999e+00 0.4398575877
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1844 1 1.253609e-02 -0.1416450733
1845 1 1.972215e-01 -1.1871818361
1846 1 1.564884e+00 0.9718870819
1847 1 -5.352198e-01 2.4116529941
1848 1 1.276402e-01 0.4664958261
1849 1 -2.546610e-01 0.8671421158
1850 1 -1.295310e+00 1.9498100600
1851 1 8.712637e-01 -0.5059742897
1852 1 1.042755e+00 -0.2848301306
1853 1 -6.548728e-01 -1.5371535657
1854 1 4.061649e-01 -0.4419641039
1855 1 -3.701574e-01 0.3595010880
1856 1 -8.494660e-01 0.2668094484
1857 1 -4.235023e-01 0.4601201698
1858 1 3.118541e-01 2.5699594034
1859 1 3.858923e-01 1.2908077867
1860 1 5.128240e-01 0.7792068180
1861 1 -4.541796e-01 0.8808773380
1862 1 2.345021e-01 -0.4485462168
1863 1 1.720489e-01 -2.0859594727
1864 1 -5.309989e-01 -0.3731444918
1865 1 -7.640969e-01 1.4966359941
1866 1 1.065466e-02 -0.5176479737
1867 1 -8.209414e-02 -1.1825741286
1868 1 1.269886e+00 -1.3231069934
1869 1 6.340240e-01 -1.2157261114
1870 1 3.964723e-01 -1.9724188031
1871 1 2.129687e-01 -0.2249344209
1872 1 -2.274793e-02 0.4775947994
1873 1 -1.955873e-01 0.1809316898
1874 1 -2.882144e-01 -1.6730899755
1875 1 -8.519617e-02 -0.6308489260
1876 1 -7.614977e-01 -0.4360083612
1877 1 -3.254867e-01 0.2090606673
1878 1 -2.211325e-01 0.2929710562
1879 1 -1.710826e+00 2.3779783778
1880 1 -3.713471e-01 -1.4813535976
1881 1 -1.191724e+00 -2.4675259417
1882 1 5.749024e-01 -0.5813851051
1883 1 -8.643900e-01 -0.2807471520
1884 1 3.922759e-01 -0.3601650438
1885 1 1.321946e+00 -0.2270587191
1886 1 1.275346e+00 0.0060096421
1887 1 -1.461178e+00 1.4219869387
1888 1 4.263679e-01 -0.6560333749
1889 1 1.069635e-01 0.7154114440
1890 1 3.954492e-01 -1.2810537354
1891 1 -5.094093e-01 0.2116882009
1892 1 6.522543e-01 0.4138620126
1893 1 6.110963e-01 -0.7819688049
1894 1 8.737319e-01 2.1829251895
1895 1 -1.470085e+00 0.5001709711
1896 1 -1.193908e-01 0.7720357885
1897 1 1.441880e+00 2.6635069516
1898 1 9.807212e-01 -0.1918281006
1899 1 -1.549435e-01 -0.0477416512
1900 1 -6.565341e-01 -0.7303512121
1901 1 -2.218050e+00 -0.1248124005
1902 1 1.565099e+00 -1.2543759396
1903 1 -6.645336e-01 -0.0018166585
1904 1 -1.106672e+00 0.0184418854
1905 1 8.962007e-01 -1.5790753894
1906 1 -4.423003e-01 1.7012976526
1907 1 -5.403642e-03 -0.4247147067
1908 1 -1.067551e+00 -0.5569274770
1909 1 -6.025665e-01 -0.3705957323
1910 1 1.091899e+00 -0.2698253087
1911 1 6.321106e-01 -1.0712310012
1912 1 -6.891076e-02 1.5093042332
1913 1 -5.260184e-01 -0.2522409869
1914 1 -9.503961e-01 -1.1194321510
1915 1 1.429524e-01 0.0355176979
1916 1 -1.126099e+00 -0.7397448082
1917 1 2.747486e+00 -0.2091672198
1918 1 -4.562190e-01 -0.2211406219
1919 1 -1.615861e+00 -0.1960416425
1920 1 4.395728e-01 0.0851074619
1921 1 2.996376e-01 -0.1635599861
1922 1 1.142633e+00 1.2442966123
1923 1 -1.601222e+00 -0.8298963397
1924 1 -1.514884e+00 -0.9578456729
1925 1 -8.502370e-01 -0.3434408738
1926 1 -4.536467e-01 -0.9954231651
1927 1 7.029974e-02 0.3539936878
1928 1 -1.713754e-01 -1.8076249912
1929 1 1.063081e+00 -0.5676861874
1930 1 -2.771234e-01 0.0987148898
1931 1 -2.123883e-01 -0.7708787733
1932 1 1.524831e+00 -0.3909979096
1933 1 1.754834e-01 -0.3555536583
1934 1 -1.893939e+00 -0.1499947922
1935 1 -7.669144e-01 1.3458038956
1936 1 -2.528206e-01 1.3359909556
1937 1 8.316751e-01 0.3266016616
1938 1 1.010643e+00 -1.0517406052
1939 1 1.796983e+00 -0.5284336240
1940 1 7.229651e-01 -1.1143800298
1941 1 4.178250e-01 -0.0049648993
1942 1 1.196199e-01 0.4954238593
1943 1 1.825934e+00 1.9632261086
1944 1 -1.567389e+00 0.0977832811
1945 1 2.665720e-01 -1.3769637483
1946 1 -2.851051e-02 -0.1787249146
1947 1 7.168300e-01 2.4089706463
1948 1 9.548940e-02 1.1836679379
1949 1 -1.699918e-01 -0.1629387394
1950 1 7.732266e-01 0.4788108005
1951 1 1.055910e+00 1.4768883954
1952 1 1.301988e+00 1.2483121552
1953 1 -5.629535e-01 1.8736015682
1954 1 -1.046239e+00 -1.4697312412
1955 1 7.296283e-01 0.3759873430
1956 1 -1.134532e+00 -0.7568702574
1957 1 -2.730316e+00 0.7899478570
1958 1 6.522059e-01 -0.7758246883
1959 1 -9.077401e-01 1.7400984741
1960 1 -1.746243e-01 0.7273764157
1961 1 -1.749175e+00 0.0412251647
1962 1 6.351639e-01 -0.7465530475
1963 1 3.928764e-01 -0.4669105152
1964 1 1.469288e-01 0.5619024171
1965 1 -4.416916e-02 0.9213398038
1966 1 5.095332e-01 1.3139610919
1967 1 1.036623e+00 -0.6321055477
1968 1 -1.017870e+00 1.1922587748
1969 1 1.540566e+00 -0.2953318428
1970 1 1.905289e+00 -0.7622792482
1971 1 4.042105e-01 0.5947329044
1972 1 -7.069767e-01 1.5472486383
1973 1 -1.008285e+00 0.6816846166
1974 1 4.863330e-01 -0.0116558350
1975 1 -7.022979e-01 0.7742367691
1976 1 3.093437e-01 0.2707596966
1977 1 -1.078647e+00 -0.1883596530
1978 1 5.902743e-01 0.6246672753
1979 1 6.584944e-01 1.4193192881
1980 1 2.048185e+00 -0.9067758246
1981 1 -1.448530e+00 -0.3987914948
1982 1 1.267581e+00 1.4135953220
1983 1 -6.959804e-01 0.2584286770
1984 1 7.405956e-02 -1.2725183814
1985 1 4.446582e-02 -0.0753325430
1986 1 -4.317039e-01 0.0703880778
1987 1 1.688329e+00 -0.7760323143
1988 1 1.932779e-01 -1.6246009465
1989 1 2.666542e-01 0.8940843318
1990 1 -7.428853e-01 1.4278914745
1991 1 2.081491e-01 1.8593265851
1992 1 -8.529617e-01 -0.3979564585
1993 1 9.235143e-01 0.4129440992
1994 1 1.354395e+00 -0.2755167794
1995 1 5.697907e-01 0.8813467819
1996 1 -2.468826e-02 1.6829380542
1997 1 6.948899e-01 0.6945449748
1998 1 -7.752261e-02 0.5096265680
1999 1 4.085734e-01 0.4243439554
2000 1 -1.587949e+00 0.0181455085
attr(,"assign")
[1] 0 1 2
$calc_lik
function (beta)
{
for (n in names(obj$model$submods)) assign(n, eval(get(n)))
p <- eval(obj$model$boolmod)
log.p <- log(p)
log.p1 <- log(1 - p)
eval(llik)
}
<bytecode: 0x1495300>
<environment: 0x4c9fa38>
$method
[1] "BFGS"
$model.fit
$BFGS
$BFGS$par
X.Intercept. x1 x2 x3 X.Intercept..1 x4 x5
BFGS -1.96264 0.25421 0.6956157 0.9975053 0.006945397 1.70257 -0.1970051
$BFGS$nhatend
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 115.415456 31.274778 63.6456959 100.237354 95.23245 -21.8675831
[2,] 31.274778 117.904100 1.8828406 19.327963 10.65650 -5.9135497
[3,] 63.645696 1.882841 134.5975938 31.183303 39.15290 -0.5354693
[4,] 100.237354 19.327963 31.1833027 145.693121 55.20376 -7.2869532
[5,] 95.232446 10.656496 39.1529009 55.203757 573.52298 37.0939880
[6,] -21.867583 -5.913550 -0.5354693 -7.286953 37.09399 171.9705056
[7,] -2.524958 -7.593851 -2.7941225 0.889493 10.58814 38.7623054
[,7]
[1,] -2.524958
[2,] -7.593851
[3,] -2.794123
[4,] 0.889493
[5,] 10.588142
[6,] 38.762305
[7,] 559.921821
$BFGS$value
[1] -771.6595
$BFGS$iterations
[1] NA
$BFGS$conv
[1] 0
$BFGS$detail
X.Intercept. x1 x2 x3 X.Intercept..1 x4 x5
BFGS -1.96264 0.25421 0.6956157 0.9975053 0.006945397 1.70257 -0.1970051
value fevals gevals niter convcode kkt1 kkt2 xtimes
BFGS 771.6595 58 18 NA 0 TRUE TRUE 0.485
attr(,"class")
[1] "boolfit" "list"
attr(,"class")
[1] "boolean"
>
> ## Induce a convergence warning message.
> (fit3.OR <- boolean(mod.OR, method="BFGS", control = list(maxit = 5)))
$call
boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5, data = Df,
family = list(binomial("probit")))
$link
$link$a
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$link$b
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$model
$model$submods
$model$submods$a
[1] "x1" "x2" "x3"
$model$submods$b
[1] "x4" "x5"
$model$boolmod
expression((a %|% b))
$N
[1] 2000
$k
[1] 7
$coef.labels
[1] "(Intercept)_a" "x1_a" "x2_a" "x3_a"
[5] "(Intercept)_b" "x4_b" "x5_b"
$coef.idx
$coef.idx$a
[1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE
$coef.idx$b
[1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE
$response
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
1 1 1 0 0 0 1 1 1 0 0 1 0 1 0 1
33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
0 1 1 1 1 0 0 1 1 1 1 0 0 1 0 1
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1
65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
1 1 1 1 1 0 0 1 1 1 1 1 0 1 0 0
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0
97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
1 1 0 1 0 0 0 0 0 0 1 1 1 0 1 1
113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
1 0 0 0 0 1 0 1 0 1 1 1 0 0 1 1
129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1
145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
0 1 1 0 0 1 0 1 1 1 0 1 1 1 0 1
161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1
177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
1 1 0 1 1 0 1 1 1 1 1 0 0 0 0 0
193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
1 0 1 1 1 0 0 0 0 1 0 1 1 1 0 1
209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
0 1 0 0 1 0 1 1 1 1 1 0 1 0 1 1
225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0
241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
1 1 1 1 0 0 1 0 1 0 0 0 0 0 1 0
257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
1 0 1 1 0 1 0 1 1 0 1 0 1 0 1 0
273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1
289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304
1 1 0 0 1 1 1 1 0 1 0 0 1 1 1 0
305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320
1 1 1 0 0 1 0 0 0 0 0 0 1 1 0 1
321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
0 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1
337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352
0 1 0 1 1 0 1 1 1 1 0 1 1 1 1 0
353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
1 1 1 1 1 1 1 0 1 0 1 1 0 0 0 0
369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384
1 0 0 0 1 1 0 1 0 0 1 1 1 0 1 0
385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1
401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416
1 0 0 1 1 1 1 1 0 0 1 1 1 0 1 1
417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1
433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448
1 0 0 1 1 1 1 0 1 1 0 0 1 0 1 1
449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464
1 1 0 1 1 0 1 1 0 0 1 1 1 0 1 0
465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 1
481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
1 0 1 1 1 1 0 0 1 0 1 0 0 0 0 1
497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
0 0 1 1 0 0 0 0 1 1 0 0 1 1 1 1
513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
1 0 1 1 0 1 0 0 1 1 0 0 0 0 1 0
529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
0 1 0 1 0 0 0 1 1 0 1 1 1 1 0 1
545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560
1 1 1 0 1 1 0 1 1 1 1 1 0 0 0 1
561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
1 1 1 1 0 1 1 0 1 0 1 0 1 0 0 0
577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592
1 1 1 0 1 0 1 0 1 1 0 1 1 1 0 0
593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608
1 1 1 0 0 0 1 1 0 0 1 1 0 1 0 0
609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
1 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0
625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640
0 1 0 1 1 0 1 1 0 1 0 0 0 1 0 1
641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1
657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672
1 1 1 0 1 1 0 0 0 1 0 0 0 0 1 1
673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
0 0 1 0 1 1 1 0 1 1 0 1 1 0 0 0
689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704
0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720
0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1
721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
1 1 0 1 1 1 1 1 0 0 0 1 1 0 1 1
737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752
0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 1
753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
1 0 1 1 0 1 1 1 0 1 1 1 0 0 0 0
769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784
0 0 0 1 1 1 0 0 0 1 1 1 1 0 0 0
785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
1 0 1 1 0 1 1 1 0 1 1 1 0 0 1 0
801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
0 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1
817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 0
833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848
1 1 1 0 0 0 1 0 1 0 1 0 0 0 1 0
849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864
1 1 1 1 0 1 0 0 1 1 1 1 0 0 0 0
865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880
0 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1
881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896
0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1
897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912
0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 1
913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
0 0 0 1 1 0 1 1 0 0 0 1 0 0 1 1
929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944
0 1 1 1 0 1 0 1 0 1 1 0 1 1 1 1
945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0
961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 0
977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
0 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1
1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
1 0 0 1 1 1 1 0 1 1 0 1 1 1 1 0
1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 1
1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
0 0 0 1 1 0 1 1 1 0 0 1 1 0 0 1
1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
0 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0
1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088
0 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1
1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
1 0 0 0 1 1 1 1 0 1 0 1 1 1 1 0
1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
0 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0
1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
1 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0
1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 0
1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168
0 0 1 0 1 1 1 1 1 0 0 1 1 1 0 0
1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
0 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1
1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0
1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216
1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1
1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 1
1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
0 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0
1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
0 0 1 0 1 0 1 1 0 0 1 0 1 1 0 1
1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
0 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1
1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
0 1 1 0 1 1 0 1 1 0 0 1 1 0 1 1
1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
0 1 1 1 0 0 0 0 0 1 1 0 1 0 1 0
1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0
1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
0 1 0 0 0 0 1 1 0 1 1 0 1 0 0 0
1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 1
1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
1 0 1 1 0 0 1 0 1 1 0 0 0 0 0 1
1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
1 0 0 0 1 1 0 0 1 0 1 1 1 1 0 0
1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
1 0 0 1 1 0 1 0 0 1 0 1 0 0 1 0
1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
1 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0
1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
1 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0
1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456
1 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1
1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472
1 1 1 1 1 0 0 1 0 1 0 1 1 1 0 0
1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488
0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1
1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1
1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520
1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1
1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
1 0 0 1 1 0 0 1 0 0 1 1 1 1 1 0
1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552
1 0 1 1 1 1 1 0 1 0 0 1 1 1 0 0
1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568
0 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0
1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584
0 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1
1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600
1 1 1 1 0 1 0 0 0 0 1 0 1 1 0 1
1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
0 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1
1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632
0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 1
1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648
1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1
1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664
1 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0
1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680
1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0
1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696
1 0 1 1 0 0 1 1 0 0 1 1 1 1 1 1
1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712
1 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0
1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1
1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744
0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0
1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760
0 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1
1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776
0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 1
1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792
1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1
1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808
1 1 1 0 1 0 0 0 1 1 1 0 0 1 0 0
1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824
0 1 1 0 0 1 0 0 1 1 1 1 0 1 1 1
1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840
1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0
1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856
0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 0
1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872
0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 0
1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888
0 1 0 0 1 0 0 0 1 1 0 1 1 1 0 1
1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904
0 0 0 1 1 1 1 1 1 1 0 0 0 1 0 0
1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 0
1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936
1 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0
1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952
1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1
1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968
1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 0
1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
1 1 1 0 1 1 0 1 0 0 0 1 0 1 1 0
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1
$frame
$frame$a
(Intercept) x1 x2 x3
1 1 0.5855288178 0.7094660175 -0.1093033147
2 1 -1.8179559677 0.6300985511 -0.2761841052
3 1 -0.1162478064 1.8173120437 0.3706278643
4 1 0.8168998395 -0.8863575212 -0.3315775899
5 1 0.7796219246 1.4557850825 -0.6443284292
6 1 1.8050975188 -0.4816473637 0.6203798013
7 1 0.8118731786 2.1968335463 2.0491903374
8 1 0.4911882793 -0.3240865787 -1.6620502439
9 1 1.1285108336 -2.3803580614 -1.0602655522
10 1 1.4607294031 -1.4130987779 0.5674032534
11 1 -0.5403860739 1.9476926647 0.0535902704
12 1 0.2779536948 0.6911712729 0.8237953287
13 1 0.1495919806 -1.3425314817 0.5533030755
14 1 -1.8323773062 0.8881394327 1.5934884724
15 1 0.0546155753 -0.7846493732 -1.0493528190
16 1 0.9426008507 0.8262582872 -0.8115404902
17 1 0.6453830742 1.0431435516 -0.3043691127
18 1 1.8670991846 0.6720424690 -0.3079533808
19 1 -0.9639014790 -0.8550825111 1.8869469420
20 1 0.6873321006 -0.5050435175 2.1577198165
21 1 0.2239254075 -1.1562233302 0.4224185278
22 1 -0.5360479985 -0.3116060846 1.5561096426
23 1 -1.2301722469 -1.3240586922 1.2612422744
24 1 -0.5050898063 -0.0521535933 0.6288606251
25 1 1.5448636020 1.3214520172 0.3221515754
26 1 -1.1588210228 -1.8453682894 1.1573252871
27 1 1.6421919908 0.8836548332 0.5248758910
28 1 -1.0479137102 -1.0111225236 0.6689216525
29 1 -1.1402641448 -1.2937152900 -0.5946987691
30 1 0.5401695677 -1.5472919670 0.8496529299
31 1 -1.6193283224 0.5483979230 0.1952821538
32 1 -0.2509466239 1.6993466685 -0.3442987963
33 1 -0.4876385152 0.3031512373 -0.2419740196
34 1 -0.2806491388 0.6330173442 -1.2398183431
35 1 0.1999204838 1.3471927776 0.0360734878
36 1 0.4809501553 2.4835500893 0.4013649875
37 1 -0.9117394242 -0.0490446909 -0.4053874767
38 1 0.0764175190 1.4537473512 0.3741210834
39 1 0.5435221079 -0.5051860029 0.7867957894
40 1 0.7984337703 0.8508604376 -0.4435679681
41 1 -1.4361457062 -0.6292596473 0.2435217656
42 1 0.1052118160 -1.7417130945 0.6452469938
43 1 0.9919506777 -0.8592507612 -0.2815796883
44 1 0.3156128247 0.6602933781 -1.7222024071
45 1 0.8678217426 -2.2900441795 -0.1501902882
46 1 0.6722680417 -0.2093011363 0.0121825061
47 1 0.0784375327 -0.7792610704 0.1665596687
48 1 -0.4678883695 0.7583745568 -0.6417363578
49 1 -0.7000758167 -0.5674015851 -0.2613939259
50 1 0.7711037397 2.7474035422 -0.0839347571
51 1 -0.8086741277 1.0011198474 0.4560525046
52 1 0.6417691636 -0.4150210287 -0.4595756762
53 1 0.7108899984 1.2676017522 -0.1431510558
54 1 -0.1625889099 0.0417091744 0.4830398961
55 1 -0.6346498942 -0.7019630314 0.5768503789
56 1 1.1471271903 0.0147936486 -0.3117392439
57 1 -1.5138640797 0.1642809977 -0.8708652186
58 1 0.3573696852 0.1023930632 -0.6752668252
59 1 -0.4282856231 -0.7139247637 -0.1903840656
60 1 0.1837369095 -2.1503105297 -0.6229665257
61 1 0.5222821685 0.0097937599 -0.4405261962
62 1 0.0382097874 1.1948056324 0.3439583521
63 1 -0.9180582028 -0.0878073286 1.3202937198
64 1 -0.3157324793 -0.5750961042 -1.4063564356
65 1 0.3803156970 0.6051367728 1.0196741368
66 1 0.9331921670 0.4754354212 0.3902816000
67 1 1.4239835987 0.4847305112 0.3492364459
68 1 0.3670449214 -1.5191990495 1.5498033320
69 1 2.0767099333 -0.3076505656 0.9523708852
70 1 -0.1420633784 -1.1830257110 0.5453184095
71 1 0.2929403027 -0.0867079053 -1.4663594955
72 1 -0.3603208006 0.3505937650 0.0282576562
73 1 -0.3758234363 -1.8128337643 0.2886002111
74 1 0.6504302424 0.3102549854 1.6683571689
75 1 -0.1460263920 1.7014318317 0.4713578806
76 1 -0.7789022252 1.1633248478 -1.9645441952
77 1 -0.4772722914 -0.1025805088 0.3686961762
78 1 -0.1505570976 0.9042435562 2.2420361375
79 1 0.7982513947 0.4981721634 0.1195602234
80 1 0.2627041248 0.6407388780 0.3070898308
81 1 0.6279651131 0.0021439509 0.2843777227
82 1 0.8281942395 -0.0848186457 -0.4347199169
83 1 -0.9712292030 -0.1609133622 1.2046870348
84 1 -0.0199197380 0.3280164800 -0.9306286167
85 1 0.5142421022 0.2931683808 -0.5626548305
86 1 -0.4300125693 1.3319083517 -0.7632671561
87 1 -0.8225400839 -0.0476483565 0.1706780107
88 1 0.4906677770 2.2391260841 0.8922123090
89 1 0.3942498996 0.6325214944 0.1025911025
90 1 0.5008706583 -0.9094679509 -2.1972085120
91 1 0.7130781039 1.0282685391 -0.5706327254
92 1 1.2035448160 1.4961826970 -0.1565984322
93 1 -1.2861886466 -0.2725228551 0.1569148369
94 1 -0.8552506726 -1.0057287350 -0.9139523908
95 1 1.2996376499 -0.0293687832 -2.2366218376
96 1 -0.4975791431 -0.6010274043 -0.5121978141
97 1 -0.0240353973 -0.1273903376 -0.1318856204
98 1 0.2109307870 0.7523360895 0.1383795467
99 1 -1.1627837168 0.8546585194 -0.2420958786
100 1 0.2241698326 -0.7368249216 -0.8467107405
101 1 -1.4203238585 -2.4669386469 0.4847158355
102 1 -0.1629454633 0.2204557848 0.8762108117
103 1 -1.9984928459 -1.2347803996 0.8463037177
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1999 1 4.085734e-01 0.4243439554
2000 1 -1.587949e+00 0.0181455085
attr(,"assign")
[1] 0 1 2
$calc_lik
function (beta)
{
for (n in names(obj$model$submods)) assign(n, eval(get(n)))
p <- eval(obj$model$boolmod)
log.p <- log(p)
log.p1 <- log(1 - p)
eval(llik)
}
<bytecode: 0x1495300>
<environment: 0x4c9fa38>
$method
[1] "BFGS"
$model.fit
$BFGS
$BFGS$par
X.Intercept. x1 x2 x3 X.Intercept..1 x4
BFGS -1.55604 0.1957866 0.6223986 0.7455059 -0.04884058 1.311676
x5
BFGS -0.2085441
$BFGS$nhatend
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 152.724870 30.350731 78.5240869 95.7768127 127.11743 -10.971630
[2,] 30.350731 156.269234 -1.5652812 14.0418127 10.66959 -4.423217
[3,] 78.524087 -1.565281 172.8665894 21.9240707 48.76609 3.315847
[4,] 95.776813 14.041813 21.9240707 158.7687197 61.88226 -3.110111
[5,] 127.117432 10.669594 48.7660940 61.8822613 633.64449 49.401769
[6,] -10.971630 -4.423217 3.3158474 -3.1101106 49.40177 268.091052
[7,] 4.493651 -6.401603 -0.9885231 0.5891863 3.17210 51.520332
[,7]
[1,] 4.4936515
[2,] -6.4016028
[3,] -0.9885231
[4,] 0.5891863
[5,] 3.1720999
[6,] 51.5203315
[7,] 611.6443925
$BFGS$value
[1] -793.5796
$BFGS$iterations
[1] NA
$BFGS$conv
[1] 1
$BFGS$detail
X.Intercept. x1 x2 x3 X.Intercept..1 x4
BFGS -1.55604 0.1957866 0.6223986 0.7455059 -0.04884058 1.311676
x5 value fevals gevals niter convcode kkt1 kkt2 xtimes
BFGS -0.2085441 793.5796 19 5 NA 1 FALSE TRUE 0.172
attr(,"class")
[1] "boolfit" "list"
attr(,"class")
[1] "boolean"
>
> ## Not run:
> ##D ## Now estimate model with genoud, a genetic optimizer. This has the
> ##D ## capability of using multiple processors via parallel.
> ##D (fit6.OR <- boolean(mod.OR, method="genoud",
> ##D cluster=c("localhost", "localhost"),
> ##D print.level=2))
> ##D
> ##D ## The default SANN optimizer is also available.
> ##D (fit7.OR <- boolean(mod.OR, method="SANN"))
> ## End(Not run)
>
> ## The fit is stored as "model.fit", within the boolean object.
> str(fit.OR$model.fit)
List of 1
$ nlminb:List of 6
..$ par : num [1, 1:7] -1.96217 0.25416 0.69548 0.99724 0.00689 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr "nlminb"
.. .. ..$ : chr [1:7] "X.Intercept." "x1" "x2" "x3" ...
..$ nhatend : num [1:7, 1:7] 115.5 31.3 63.7 100.2 95.3 ...
..$ value : num -772
..$ iterations: num 42
..$ conv : num 0
..$ detail :Classes ‘optimx’ and 'data.frame': 1 obs. of 15 variables:
.. ..$ X.Intercept. : num -1.96
.. ..$ x1 : num 0.254
.. ..$ x2 : num 0.695
.. ..$ x3 : num 0.997
.. ..$ X.Intercept..1: num 0.00689
.. ..$ x4 : num 1.7
.. ..$ x5 : num -0.197
.. ..$ value : num 772
.. ..$ fevals : num 54
.. ..$ gevals : num 408
.. ..$ niter : num 42
.. ..$ convcode : num 0
.. ..$ kkt1 : logi TRUE
.. ..$ kkt2 : logi TRUE
.. ..$ xtimes : num 0.66
.. ..- attr(*, "details")=List of 5
.. .. ..$ : chr "nlminb"
.. .. ..$ : num [1:7] 1.23e-05 1.32e-04 -4.30e-05 -1.06e-04 -4.30e-04 ...
.. .. ..$ : num [1:7, 1:7] 115.5 31.3 63.7 100.2 95.3 ...
.. .. ..$ : num [1:7] 617 562 246 158 121 ...
.. .. ..$ : chr "none"
.. .. ..- attr(*, "dim")= int [1:2] 1 5
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr "nlminb"
.. .. .. ..$ : chr [1:5] "method" "ngatend" "nhatend" "hev" ...
.. ..- attr(*, "maximize")= logi FALSE
.. ..- attr(*, "npar")= int 7
.. ..- attr(*, "follow.on")= logi FALSE
- attr(*, "class")= chr [1:2] "boolfit" "list"
>
> ## Create a summary object, saving and printing it. Then take a look at
> ## the objects stored in the summary object.
> (smry <- summary(fit.OR))
Length Class Mode
call 6 -none- call
link 2 -none- list
model 2 -none- list
N 1 -none- numeric
k 1 -none- numeric
coef.labels 7 -none- character
coef.idx 2 -none- list
response 2000 -none- numeric
frame 2 -none- list
calc_lik 1 -none- function
method 1 -none- character
model.fit 1 boolfit list
> str(smry)
'summaryDefault' chr [1:12, 1:3] " 6" " 2" " 2" " 1" " 1" " 7" ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:12] "call" "link" "model" "N" ...
..$ : chr [1:3] "Length" "Class" "Mode"
>
> ## Extract log-likelihood and coefficient vector.
> logLik(fit.OR)
Error in UseMethod("logLik") :
no applicable method for 'logLik' applied to an object of class "boolean"
Calls: logLik
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 3.1.6
Check: examples
Result: ERROR
Running examples in ‘boolean3-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: boolean3-package
> ### Title: Modeling Causal Complexity
> ### Aliases: boolean3 boolean3-package
> ### Keywords: package
>
> ### ** Examples
>
> ## Generate some fake data
> require(mvtnorm)
Loading required package: mvtnorm
> set.seed(12345)
> N <- 2000
> Df <- cbind(1, rmvnorm(N, mean=rep(0, 5)))
>
> ## Set coefficients
> beta.a <- c(-2.00, 0.33, 0.66, 1.00)
> beta.b <- c(0.00, 1.50, -0.25)
>
> ## Generate path probabilities following a normal model.
> y.a <- as.vector(pnorm(tcrossprod(beta.a, Df[, 1:4])))
> y.b <- as.vector(pnorm(tcrossprod(beta.b, Df[, c(1, 5, 6)])))
>
> ## AND and OR-model
> or <- function(x, y) { x + y - x * y }
> and <- function(x, y) { x * y }
> y.star.OR <- or(y.a, y.b)
> y.star.AND <- and(y.a, y.b)
>
> ## Observed responses
> y.OR <- rbinom(N, 1, y.star.OR)
> y.AND <- rbinom(N, 1, y.star.AND)
>
> ## Set up data.frame for estimation
> Df <- cbind(1, Df)
> Df <- as.data.frame(Df)
> Df[,1] <- y.OR
> Df[,2] <- y.AND
> names(Df) <- c("y.OR", "y.AND", "x1", "x2", "x3", "x4", "x5")
>
> ## Before estimating, boolean models need to be specified using the
> ## boolprep function.
>
> ## OR model, specified to use a probit link for each causal path. This
> ## matches the data generating process above.
> mod.OR <- boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5,
+ data = Df, family=list(binomial("probit")))
>
> ## IF you really want to, it's also possible to specify a different
> ## link function for each causal path. These should be in the same
> ## order as specified in the model formula.
> mod.OR2 <- boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5,
+ data = Df, family=list(binomial("probit"),
+ binomial("logit")))
>
> ## Fit the prepared model using the nlminb optimizer (the default).
> (fit.OR <- boolean(mod.OR, method="nlminb", control=list(trace=1)))
fn is fn
Looking for method = nlminb
Function has 7 arguments
Analytic gradient not made available.
Analytic Hessian not made available.
Scale check -- log parameter ratio= 2.069957 log bounds ratio= NA
Method: nlminb
0: 2035.0322: 0.555932 0.00473222 0.0518577 0.0496661 0.555932 0.314324 -0.0401190
1: 1125.8726: -0.128002 0.0388608 0.148522 0.127276 -0.0324267 0.721923 -0.0984183
2: 842.16854: -0.894107 0.0450576 0.220895 0.294603 0.0566570 1.33118 -0.124427
3: 801.45277: -1.16971 0.0904748 0.304796 0.364513 -0.181651 1.54390 -0.197683
4: 793.97921: -1.20286 0.147914 0.450047 0.558015 0.0953275 1.78334 -0.178614
5: 780.71673: -1.44471 0.189016 0.538420 0.765386 -0.198236 1.79638 -0.181454
6: 775.40425: -1.51478 0.180762 0.546683 0.747108 -0.0165510 1.79489 -0.229489
7: 774.57448: -1.57276 0.182887 0.545728 0.743597 -0.0714210 1.78130 -0.190647
8: 773.65776: -1.60590 0.198898 0.574778 0.783028 -0.0163173 1.74617 -0.198394
9: 773.45521: -1.67407 0.202747 0.586465 0.808583 -0.0539769 1.72187 -0.223385
10: 773.01515: -1.71678 0.200876 0.598546 0.838030 -0.0208753 1.72345 -0.159004
11: 772.50230: -1.72318 0.206564 0.606078 0.846533 -0.0158345 1.72432 -0.200933
12: 772.18615: -1.78954 0.231404 0.641645 0.890729 -0.0149774 1.74469 -0.180530
13: 771.89407: -1.85032 0.220442 0.644318 0.940353 -0.00765249 1.69664 -0.201119
14: 771.82963: -1.85752 0.227057 0.652232 0.940176 0.00321924 1.70914 -0.192294
15: 771.80804: -1.86904 0.234336 0.658832 0.936483 -0.00922186 1.71128 -0.202710
16: 771.77147: -1.87385 0.245677 0.671660 0.936852 0.000202047 1.70546 -0.194253
17: 771.73610: -1.88837 0.239128 0.666675 0.949032 -0.000940174 1.71251 -0.199984
18: 771.73251: -1.89568 0.239325 0.673717 0.966182 0.00207717 1.70995 -0.190095
19: 771.69796: -1.91188 0.246202 0.678638 0.968550 0.000991727 1.69952 -0.197926
20: 771.68769: -1.91635 0.246152 0.679388 0.968983 0.00465926 1.70560 -0.195221
21: 771.68585: -1.92140 0.245884 0.681944 0.970632 -0.000298807 1.70718 -0.199282
22: 771.67581: -1.92565 0.249540 0.682440 0.975775 0.00323946 1.70555 -0.197016
23: 771.67138: -1.93310 0.248427 0.685083 0.977666 0.00545687 1.70803 -0.197440
24: 771.66787: -1.93884 0.251036 0.686583 0.982438 0.00259472 1.70576 -0.196931
25: 771.66515: -1.94282 0.249059 0.691551 0.986158 0.00554980 1.70248 -0.197777
26: 771.66252: -1.94927 0.253205 0.688497 0.988277 0.00637468 1.70450 -0.196717
27: 771.66179: -1.94943 0.252731 0.690801 0.989858 0.00511422 1.70388 -0.197930
28: 771.66159: -1.95076 0.251950 0.692556 0.990551 0.00589815 1.70452 -0.195798
29: 771.66071: -1.95311 0.252596 0.692511 0.991245 0.00686234 1.70299 -0.197147
30: 771.66048: -1.95352 0.252645 0.692540 0.991439 0.00603419 1.70315 -0.197013
31: 771.66035: -1.95391 0.252809 0.692812 0.992092 0.00645048 1.70345 -0.197051
32: 771.66010: -1.95539 0.253106 0.692932 0.993201 0.00597275 1.70362 -0.197059
33: 771.65989: -1.95682 0.253095 0.693679 0.993688 0.00656203 1.70289 -0.196863
34: 771.65976: -1.95822 0.253576 0.694616 0.994380 0.00638257 1.70290 -0.197323
35: 771.65960: -1.95958 0.253686 0.694374 0.995708 0.00653884 1.70291 -0.197074
36: 771.65960: -1.96080 0.254736 0.695269 0.996154 0.00683515 1.70302 -0.196825
37: 771.65953: -1.96146 0.254391 0.695463 0.996637 0.00680183 1.70275 -0.197052
38: 771.65952: -1.96215 0.254142 0.695673 0.997170 0.00692894 1.70255 -0.196925
39: 771.65951: -1.96220 0.254146 0.695537 0.997255 0.00692672 1.70258 -0.197040
40: 771.65951: -1.96221 0.254149 0.695523 0.997250 0.00689011 1.70258 -0.197017
41: 771.65951: -1.96219 0.254155 0.695486 0.997245 0.00689580 1.70257 -0.197015
42: 771.65951: -1.96217 0.254157 0.695483 0.997240 0.00689316 1.70257 -0.197019
Post processing for method nlminb
Successful convergence!
Compute Hessian approximation at finish of nlminb
Compute gradient approximation at finish of nlminb
Save results from method nlminb
$par
(Intercept) x1 x2 x3 (Intercept) x4
-1.962174116 0.254157295 0.695482511 0.997240458 0.006893159 1.702572353
x5
-0.197019270
$message
[1] "relative convergence (4)"
$convcode
[1] 0
$value
[1] 771.6595
$fevals
function
54
$gevals
gradient
408
$nitns
[1] 42
$kkt1
[1] TRUE
$kkt2
[1] TRUE
$xtimes
user.self
0.409
Assemble the answers
$call
boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5, data = Df,
family = list(binomial("probit")))
$link
$link$a
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$link$b
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$model
$model$submods
$model$submods$a
[1] "x1" "x2" "x3"
$model$submods$b
[1] "x4" "x5"
$model$boolmod
expression((a %|% b))
$N
[1] 2000
$k
[1] 7
$coef.labels
[1] "(Intercept)_a" "x1_a" "x2_a" "x3_a"
[5] "(Intercept)_b" "x4_b" "x5_b"
$coef.idx
$coef.idx$a
[1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE
$coef.idx$b
[1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE
$response
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
1 1 1 0 0 0 1 1 1 0 0 1 0 1 0 1
33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
0 1 1 1 1 0 0 1 1 1 1 0 0 1 0 1
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1
65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
1 1 1 1 1 0 0 1 1 1 1 1 0 1 0 0
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0
97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
1 1 0 1 0 0 0 0 0 0 1 1 1 0 1 1
113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
1 0 0 0 0 1 0 1 0 1 1 1 0 0 1 1
129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1
145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
0 1 1 0 0 1 0 1 1 1 0 1 1 1 0 1
161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1
177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
1 1 0 1 1 0 1 1 1 1 1 0 0 0 0 0
193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
1 0 1 1 1 0 0 0 0 1 0 1 1 1 0 1
209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
0 1 0 0 1 0 1 1 1 1 1 0 1 0 1 1
225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0
241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
1 1 1 1 0 0 1 0 1 0 0 0 0 0 1 0
257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
1 0 1 1 0 1 0 1 1 0 1 0 1 0 1 0
273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1
289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304
1 1 0 0 1 1 1 1 0 1 0 0 1 1 1 0
305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320
1 1 1 0 0 1 0 0 0 0 0 0 1 1 0 1
321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
0 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1
337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352
0 1 0 1 1 0 1 1 1 1 0 1 1 1 1 0
353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
1 1 1 1 1 1 1 0 1 0 1 1 0 0 0 0
369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384
1 0 0 0 1 1 0 1 0 0 1 1 1 0 1 0
385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1
401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416
1 0 0 1 1 1 1 1 0 0 1 1 1 0 1 1
417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1
433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448
1 0 0 1 1 1 1 0 1 1 0 0 1 0 1 1
449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464
1 1 0 1 1 0 1 1 0 0 1 1 1 0 1 0
465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 1
481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
1 0 1 1 1 1 0 0 1 0 1 0 0 0 0 1
497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
0 0 1 1 0 0 0 0 1 1 0 0 1 1 1 1
513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
1 0 1 1 0 1 0 0 1 1 0 0 0 0 1 0
529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
0 1 0 1 0 0 0 1 1 0 1 1 1 1 0 1
545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560
1 1 1 0 1 1 0 1 1 1 1 1 0 0 0 1
561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
1 1 1 1 0 1 1 0 1 0 1 0 1 0 0 0
577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592
1 1 1 0 1 0 1 0 1 1 0 1 1 1 0 0
593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608
1 1 1 0 0 0 1 1 0 0 1 1 0 1 0 0
609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
1 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0
625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640
0 1 0 1 1 0 1 1 0 1 0 0 0 1 0 1
641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1
657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672
1 1 1 0 1 1 0 0 0 1 0 0 0 0 1 1
673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
0 0 1 0 1 1 1 0 1 1 0 1 1 0 0 0
689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704
0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720
0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1
721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
1 1 0 1 1 1 1 1 0 0 0 1 1 0 1 1
737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752
0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 1
753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
1 0 1 1 0 1 1 1 0 1 1 1 0 0 0 0
769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784
0 0 0 1 1 1 0 0 0 1 1 1 1 0 0 0
785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
1 0 1 1 0 1 1 1 0 1 1 1 0 0 1 0
801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
0 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1
817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 0
833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848
1 1 1 0 0 0 1 0 1 0 1 0 0 0 1 0
849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864
1 1 1 1 0 1 0 0 1 1 1 1 0 0 0 0
865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880
0 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1
881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896
0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1
897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912
0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 1
913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
0 0 0 1 1 0 1 1 0 0 0 1 0 0 1 1
929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944
0 1 1 1 0 1 0 1 0 1 1 0 1 1 1 1
945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0
961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 0
977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
0 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1
1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
1 0 0 1 1 1 1 0 1 1 0 1 1 1 1 0
1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 1
1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
0 0 0 1 1 0 1 1 1 0 0 1 1 0 0 1
1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
0 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0
1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088
0 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1
1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
1 0 0 0 1 1 1 1 0 1 0 1 1 1 1 0
1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
0 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0
1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
1 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0
1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 0
1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168
0 0 1 0 1 1 1 1 1 0 0 1 1 1 0 0
1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
0 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1
1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0
1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216
1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1
1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 1
1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
0 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0
1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
0 0 1 0 1 0 1 1 0 0 1 0 1 1 0 1
1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
0 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1
1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
0 1 1 0 1 1 0 1 1 0 0 1 1 0 1 1
1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
0 1 1 1 0 0 0 0 0 1 1 0 1 0 1 0
1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0
1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
0 1 0 0 0 0 1 1 0 1 1 0 1 0 0 0
1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 1
1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
1 0 1 1 0 0 1 0 1 1 0 0 0 0 0 1
1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
1 0 0 0 1 1 0 0 1 0 1 1 1 1 0 0
1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
1 0 0 1 1 0 1 0 0 1 0 1 0 0 1 0
1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
1 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0
1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
1 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0
1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456
1 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1
1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472
1 1 1 1 1 0 0 1 0 1 0 1 1 1 0 0
1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488
0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1
1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1
1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520
1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1
1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
1 0 0 1 1 0 0 1 0 0 1 1 1 1 1 0
1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552
1 0 1 1 1 1 1 0 1 0 0 1 1 1 0 0
1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568
0 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0
1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584
0 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1
1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600
1 1 1 1 0 1 0 0 0 0 1 0 1 1 0 1
1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
0 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1
1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632
0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 1
1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648
1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1
1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664
1 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0
1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680
1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0
1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696
1 0 1 1 0 0 1 1 0 0 1 1 1 1 1 1
1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712
1 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0
1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1
1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744
0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0
1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760
0 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1
1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776
0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 1
1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792
1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1
1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808
1 1 1 0 1 0 0 0 1 1 1 0 0 1 0 0
1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824
0 1 1 0 0 1 0 0 1 1 1 1 0 1 1 1
1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840
1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0
1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856
0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 0
1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872
0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 0
1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888
0 1 0 0 1 0 0 0 1 1 0 1 1 1 0 1
1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904
0 0 0 1 1 1 1 1 1 1 0 0 0 1 0 0
1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 0
1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936
1 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0
1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952
1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1
1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968
1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 0
1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
1 1 1 0 1 1 0 1 0 0 0 1 0 1 1 0
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1
$frame
$frame$a
(Intercept) x1 x2 x3
1 1 0.5855288178 0.7094660175 -0.1093033147
2 1 -1.8179559677 0.6300985511 -0.2761841052
3 1 -0.1162478064 1.8173120437 0.3706278643
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1889 1 1.069635e-01 0.7154114440
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1899 1 -1.549435e-01 -0.0477416512
1900 1 -6.565341e-01 -0.7303512121
1901 1 -2.218050e+00 -0.1248124005
1902 1 1.565099e+00 -1.2543759396
1903 1 -6.645336e-01 -0.0018166585
1904 1 -1.106672e+00 0.0184418854
1905 1 8.962007e-01 -1.5790753894
1906 1 -4.423003e-01 1.7012976526
1907 1 -5.403642e-03 -0.4247147067
1908 1 -1.067551e+00 -0.5569274770
1909 1 -6.025665e-01 -0.3705957323
1910 1 1.091899e+00 -0.2698253087
1911 1 6.321106e-01 -1.0712310012
1912 1 -6.891076e-02 1.5093042332
1913 1 -5.260184e-01 -0.2522409869
1914 1 -9.503961e-01 -1.1194321510
1915 1 1.429524e-01 0.0355176979
1916 1 -1.126099e+00 -0.7397448082
1917 1 2.747486e+00 -0.2091672198
1918 1 -4.562190e-01 -0.2211406219
1919 1 -1.615861e+00 -0.1960416425
1920 1 4.395728e-01 0.0851074619
1921 1 2.996376e-01 -0.1635599861
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1924 1 -1.514884e+00 -0.9578456729
1925 1 -8.502370e-01 -0.3434408738
1926 1 -4.536467e-01 -0.9954231651
1927 1 7.029974e-02 0.3539936878
1928 1 -1.713754e-01 -1.8076249912
1929 1 1.063081e+00 -0.5676861874
1930 1 -2.771234e-01 0.0987148898
1931 1 -2.123883e-01 -0.7708787733
1932 1 1.524831e+00 -0.3909979096
1933 1 1.754834e-01 -0.3555536583
1934 1 -1.893939e+00 -0.1499947922
1935 1 -7.669144e-01 1.3458038956
1936 1 -2.528206e-01 1.3359909556
1937 1 8.316751e-01 0.3266016616
1938 1 1.010643e+00 -1.0517406052
1939 1 1.796983e+00 -0.5284336240
1940 1 7.229651e-01 -1.1143800298
1941 1 4.178250e-01 -0.0049648993
1942 1 1.196199e-01 0.4954238593
1943 1 1.825934e+00 1.9632261086
1944 1 -1.567389e+00 0.0977832811
1945 1 2.665720e-01 -1.3769637483
1946 1 -2.851051e-02 -0.1787249146
1947 1 7.168300e-01 2.4089706463
1948 1 9.548940e-02 1.1836679379
1949 1 -1.699918e-01 -0.1629387394
1950 1 7.732266e-01 0.4788108005
1951 1 1.055910e+00 1.4768883954
1952 1 1.301988e+00 1.2483121552
1953 1 -5.629535e-01 1.8736015682
1954 1 -1.046239e+00 -1.4697312412
1955 1 7.296283e-01 0.3759873430
1956 1 -1.134532e+00 -0.7568702574
1957 1 -2.730316e+00 0.7899478570
1958 1 6.522059e-01 -0.7758246883
1959 1 -9.077401e-01 1.7400984741
1960 1 -1.746243e-01 0.7273764157
1961 1 -1.749175e+00 0.0412251647
1962 1 6.351639e-01 -0.7465530475
1963 1 3.928764e-01 -0.4669105152
1964 1 1.469288e-01 0.5619024171
1965 1 -4.416916e-02 0.9213398038
1966 1 5.095332e-01 1.3139610919
1967 1 1.036623e+00 -0.6321055477
1968 1 -1.017870e+00 1.1922587748
1969 1 1.540566e+00 -0.2953318428
1970 1 1.905289e+00 -0.7622792482
1971 1 4.042105e-01 0.5947329044
1972 1 -7.069767e-01 1.5472486383
1973 1 -1.008285e+00 0.6816846166
1974 1 4.863330e-01 -0.0116558350
1975 1 -7.022979e-01 0.7742367691
1976 1 3.093437e-01 0.2707596966
1977 1 -1.078647e+00 -0.1883596530
1978 1 5.902743e-01 0.6246672753
1979 1 6.584944e-01 1.4193192881
1980 1 2.048185e+00 -0.9067758246
1981 1 -1.448530e+00 -0.3987914948
1982 1 1.267581e+00 1.4135953220
1983 1 -6.959804e-01 0.2584286770
1984 1 7.405956e-02 -1.2725183814
1985 1 4.446582e-02 -0.0753325430
1986 1 -4.317039e-01 0.0703880778
1987 1 1.688329e+00 -0.7760323143
1988 1 1.932779e-01 -1.6246009465
1989 1 2.666542e-01 0.8940843318
1990 1 -7.428853e-01 1.4278914745
1991 1 2.081491e-01 1.8593265851
1992 1 -8.529617e-01 -0.3979564585
1993 1 9.235143e-01 0.4129440992
1994 1 1.354395e+00 -0.2755167794
1995 1 5.697907e-01 0.8813467819
1996 1 -2.468826e-02 1.6829380542
1997 1 6.948899e-01 0.6945449748
1998 1 -7.752261e-02 0.5096265680
1999 1 4.085734e-01 0.4243439554
2000 1 -1.587949e+00 0.0181455085
attr(,"assign")
[1] 0 1 2
$calc_lik
function (beta)
{
for (n in names(obj$model$submods)) assign(n, eval(get(n)))
p <- eval(obj$model$boolmod)
log.p <- log(p)
log.p1 <- log(1 - p)
eval(llik)
}
<bytecode: 0x55f20e31a5d0>
<environment: 0x55f21149e268>
$method
[1] "nlminb"
$model.fit
$nlminb
$nlminb$par
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlminb -1.962174 0.2541573 0.6954825 0.9972405 0.006893159 1.702572
x5
nlminb -0.1970193
$nlminb$nhatend
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 115.457214 31.281924 63.6547716 100.2494647 95.25488 -21.8659765
[2,] 31.281924 117.944232 1.8807743 19.3257198 10.65405 -5.9115098
[3,] 63.654772 1.880774 134.6346070 31.1730783 39.15753 -0.5340214
[4,] 100.249465 19.325720 31.1730783 145.7170857 55.20325 -7.2822566
[5,] 95.254883 10.654048 39.1575254 55.2032529 573.49201 37.1084645
[6,] -21.865976 -5.911510 -0.5340214 -7.2822566 37.10846 171.9634863
[7,] -2.522868 -7.597161 -2.7946852 0.8885202 10.58556 38.7621375
[,7]
[1,] -2.5228681
[2,] -7.5971613
[3,] -2.7946852
[4,] 0.8885202
[5,] 10.5855587
[6,] 38.7621375
[7,] 559.8837337
$nlminb$value
[1] -771.6595
$nlminb$iterations
[1] 42
$nlminb$conv
[1] 0
$nlminb$detail
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlminb -1.962174 0.2541573 0.6954825 0.9972405 0.006893159 1.702572
x5 value fevals gevals niter convcode kkt1 kkt2 xtimes
nlminb -0.1970193 771.6595 54 408 42 0 TRUE TRUE 0.409
attr(,"class")
[1] "boolfit" "list"
attr(,"class")
[1] "boolean"
>
> ## Multiple optimizers can be specified in a single call to boolean.
> ## Here we fit with the nlm and nlminb optimizers.
> (fit1.OR <- boolean(mod.OR, method=c("nlm", "nlminb")))
$call
boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5, data = Df,
family = list(binomial("probit")))
$link
$link$a
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$link$b
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$model
$model$submods
$model$submods$a
[1] "x1" "x2" "x3"
$model$submods$b
[1] "x4" "x5"
$model$boolmod
expression((a %|% b))
$N
[1] 2000
$k
[1] 7
$coef.labels
[1] "(Intercept)_a" "x1_a" "x2_a" "x3_a"
[5] "(Intercept)_b" "x4_b" "x5_b"
$coef.idx
$coef.idx$a
[1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE
$coef.idx$b
[1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE
$response
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
1 1 1 0 0 0 1 1 1 0 0 1 0 1 0 1
33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
0 1 1 1 1 0 0 1 1 1 1 0 0 1 0 1
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1
65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
1 1 1 1 1 0 0 1 1 1 1 1 0 1 0 0
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0
97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
1 1 0 1 0 0 0 0 0 0 1 1 1 0 1 1
113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
1 0 0 0 0 1 0 1 0 1 1 1 0 0 1 1
129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1
145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
0 1 1 0 0 1 0 1 1 1 0 1 1 1 0 1
161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1
177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
1 1 0 1 1 0 1 1 1 1 1 0 0 0 0 0
193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
1 0 1 1 1 0 0 0 0 1 0 1 1 1 0 1
209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
0 1 0 0 1 0 1 1 1 1 1 0 1 0 1 1
225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0
241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
1 1 1 1 0 0 1 0 1 0 0 0 0 0 1 0
257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
1 0 1 1 0 1 0 1 1 0 1 0 1 0 1 0
273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1
289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304
1 1 0 0 1 1 1 1 0 1 0 0 1 1 1 0
305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320
1 1 1 0 0 1 0 0 0 0 0 0 1 1 0 1
321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
0 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1
337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352
0 1 0 1 1 0 1 1 1 1 0 1 1 1 1 0
353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
1 1 1 1 1 1 1 0 1 0 1 1 0 0 0 0
369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384
1 0 0 0 1 1 0 1 0 0 1 1 1 0 1 0
385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1
401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416
1 0 0 1 1 1 1 1 0 0 1 1 1 0 1 1
417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1
433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448
1 0 0 1 1 1 1 0 1 1 0 0 1 0 1 1
449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464
1 1 0 1 1 0 1 1 0 0 1 1 1 0 1 0
465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 1
481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
1 0 1 1 1 1 0 0 1 0 1 0 0 0 0 1
497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
0 0 1 1 0 0 0 0 1 1 0 0 1 1 1 1
513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
1 0 1 1 0 1 0 0 1 1 0 0 0 0 1 0
529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
0 1 0 1 0 0 0 1 1 0 1 1 1 1 0 1
545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560
1 1 1 0 1 1 0 1 1 1 1 1 0 0 0 1
561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
1 1 1 1 0 1 1 0 1 0 1 0 1 0 0 0
577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592
1 1 1 0 1 0 1 0 1 1 0 1 1 1 0 0
593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608
1 1 1 0 0 0 1 1 0 0 1 1 0 1 0 0
609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
1 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0
625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640
0 1 0 1 1 0 1 1 0 1 0 0 0 1 0 1
641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1
657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672
1 1 1 0 1 1 0 0 0 1 0 0 0 0 1 1
673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
0 0 1 0 1 1 1 0 1 1 0 1 1 0 0 0
689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704
0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720
0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1
721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
1 1 0 1 1 1 1 1 0 0 0 1 1 0 1 1
737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752
0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 1
753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
1 0 1 1 0 1 1 1 0 1 1 1 0 0 0 0
769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784
0 0 0 1 1 1 0 0 0 1 1 1 1 0 0 0
785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
1 0 1 1 0 1 1 1 0 1 1 1 0 0 1 0
801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
0 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1
817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 0
833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848
1 1 1 0 0 0 1 0 1 0 1 0 0 0 1 0
849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864
1 1 1 1 0 1 0 0 1 1 1 1 0 0 0 0
865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880
0 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1
881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896
0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1
897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912
0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 1
913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
0 0 0 1 1 0 1 1 0 0 0 1 0 0 1 1
929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944
0 1 1 1 0 1 0 1 0 1 1 0 1 1 1 1
945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0
961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 0
977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
0 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1
1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
1 0 0 1 1 1 1 0 1 1 0 1 1 1 1 0
1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 1
1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
0 0 0 1 1 0 1 1 1 0 0 1 1 0 0 1
1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
0 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0
1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088
0 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1
1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
1 0 0 0 1 1 1 1 0 1 0 1 1 1 1 0
1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
0 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0
1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
1 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0
1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 0
1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168
0 0 1 0 1 1 1 1 1 0 0 1 1 1 0 0
1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
0 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1
1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0
1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216
1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1
1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 1
1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
0 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0
1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
0 0 1 0 1 0 1 1 0 0 1 0 1 1 0 1
1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
0 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1
1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
0 1 1 0 1 1 0 1 1 0 0 1 1 0 1 1
1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
0 1 1 1 0 0 0 0 0 1 1 0 1 0 1 0
1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0
1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
0 1 0 0 0 0 1 1 0 1 1 0 1 0 0 0
1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 1
1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
1 0 1 1 0 0 1 0 1 1 0 0 0 0 0 1
1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
1 0 0 0 1 1 0 0 1 0 1 1 1 1 0 0
1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
1 0 0 1 1 0 1 0 0 1 0 1 0 0 1 0
1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
1 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0
1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
1 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0
1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456
1 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1
1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472
1 1 1 1 1 0 0 1 0 1 0 1 1 1 0 0
1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488
0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1
1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1
1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520
1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1
1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
1 0 0 1 1 0 0 1 0 0 1 1 1 1 1 0
1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552
1 0 1 1 1 1 1 0 1 0 0 1 1 1 0 0
1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568
0 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0
1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584
0 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1
1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600
1 1 1 1 0 1 0 0 0 0 1 0 1 1 0 1
1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
0 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1
1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632
0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 1
1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648
1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1
1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664
1 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0
1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680
1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0
1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696
1 0 1 1 0 0 1 1 0 0 1 1 1 1 1 1
1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712
1 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0
1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1
1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744
0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0
1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760
0 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1
1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776
0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 1
1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792
1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1
1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808
1 1 1 0 1 0 0 0 1 1 1 0 0 1 0 0
1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824
0 1 1 0 0 1 0 0 1 1 1 1 0 1 1 1
1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840
1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0
1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856
0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 0
1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872
0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 0
1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888
0 1 0 0 1 0 0 0 1 1 0 1 1 1 0 1
1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904
0 0 0 1 1 1 1 1 1 1 0 0 0 1 0 0
1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 0
1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936
1 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0
1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952
1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1
1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968
1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 0
1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
1 1 1 0 1 1 0 1 0 0 0 1 0 1 1 0
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1
$frame
$frame$a
(Intercept) x1 x2 x3
1 1 0.5855288178 0.7094660175 -0.1093033147
2 1 -1.8179559677 0.6300985511 -0.2761841052
3 1 -0.1162478064 1.8173120437 0.3706278643
4 1 0.8168998395 -0.8863575212 -0.3315775899
5 1 0.7796219246 1.4557850825 -0.6443284292
6 1 1.8050975188 -0.4816473637 0.6203798013
7 1 0.8118731786 2.1968335463 2.0491903374
8 1 0.4911882793 -0.3240865787 -1.6620502439
9 1 1.1285108336 -2.3803580614 -1.0602655522
10 1 1.4607294031 -1.4130987779 0.5674032534
11 1 -0.5403860739 1.9476926647 0.0535902704
12 1 0.2779536948 0.6911712729 0.8237953287
13 1 0.1495919806 -1.3425314817 0.5533030755
14 1 -1.8323773062 0.8881394327 1.5934884724
15 1 0.0546155753 -0.7846493732 -1.0493528190
16 1 0.9426008507 0.8262582872 -0.8115404902
17 1 0.6453830742 1.0431435516 -0.3043691127
18 1 1.8670991846 0.6720424690 -0.3079533808
19 1 -0.9639014790 -0.8550825111 1.8869469420
20 1 0.6873321006 -0.5050435175 2.1577198165
21 1 0.2239254075 -1.1562233302 0.4224185278
22 1 -0.5360479985 -0.3116060846 1.5561096426
23 1 -1.2301722469 -1.3240586922 1.2612422744
24 1 -0.5050898063 -0.0521535933 0.6288606251
25 1 1.5448636020 1.3214520172 0.3221515754
26 1 -1.1588210228 -1.8453682894 1.1573252871
27 1 1.6421919908 0.8836548332 0.5248758910
28 1 -1.0479137102 -1.0111225236 0.6689216525
29 1 -1.1402641448 -1.2937152900 -0.5946987691
30 1 0.5401695677 -1.5472919670 0.8496529299
31 1 -1.6193283224 0.5483979230 0.1952821538
32 1 -0.2509466239 1.6993466685 -0.3442987963
33 1 -0.4876385152 0.3031512373 -0.2419740196
34 1 -0.2806491388 0.6330173442 -1.2398183431
35 1 0.1999204838 1.3471927776 0.0360734878
36 1 0.4809501553 2.4835500893 0.4013649875
37 1 -0.9117394242 -0.0490446909 -0.4053874767
38 1 0.0764175190 1.4537473512 0.3741210834
39 1 0.5435221079 -0.5051860029 0.7867957894
40 1 0.7984337703 0.8508604376 -0.4435679681
41 1 -1.4361457062 -0.6292596473 0.2435217656
42 1 0.1052118160 -1.7417130945 0.6452469938
43 1 0.9919506777 -0.8592507612 -0.2815796883
44 1 0.3156128247 0.6602933781 -1.7222024071
45 1 0.8678217426 -2.2900441795 -0.1501902882
46 1 0.6722680417 -0.2093011363 0.0121825061
47 1 0.0784375327 -0.7792610704 0.1665596687
48 1 -0.4678883695 0.7583745568 -0.6417363578
49 1 -0.7000758167 -0.5674015851 -0.2613939259
50 1 0.7711037397 2.7474035422 -0.0839347571
51 1 -0.8086741277 1.0011198474 0.4560525046
52 1 0.6417691636 -0.4150210287 -0.4595756762
53 1 0.7108899984 1.2676017522 -0.1431510558
54 1 -0.1625889099 0.0417091744 0.4830398961
55 1 -0.6346498942 -0.7019630314 0.5768503789
56 1 1.1471271903 0.0147936486 -0.3117392439
57 1 -1.5138640797 0.1642809977 -0.8708652186
58 1 0.3573696852 0.1023930632 -0.6752668252
59 1 -0.4282856231 -0.7139247637 -0.1903840656
60 1 0.1837369095 -2.1503105297 -0.6229665257
61 1 0.5222821685 0.0097937599 -0.4405261962
62 1 0.0382097874 1.1948056324 0.3439583521
63 1 -0.9180582028 -0.0878073286 1.3202937198
64 1 -0.3157324793 -0.5750961042 -1.4063564356
65 1 0.3803156970 0.6051367728 1.0196741368
66 1 0.9331921670 0.4754354212 0.3902816000
67 1 1.4239835987 0.4847305112 0.3492364459
68 1 0.3670449214 -1.5191990495 1.5498033320
69 1 2.0767099333 -0.3076505656 0.9523708852
70 1 -0.1420633784 -1.1830257110 0.5453184095
71 1 0.2929403027 -0.0867079053 -1.4663594955
72 1 -0.3603208006 0.3505937650 0.0282576562
73 1 -0.3758234363 -1.8128337643 0.2886002111
74 1 0.6504302424 0.3102549854 1.6683571689
75 1 -0.1460263920 1.7014318317 0.4713578806
76 1 -0.7789022252 1.1633248478 -1.9645441952
77 1 -0.4772722914 -0.1025805088 0.3686961762
78 1 -0.1505570976 0.9042435562 2.2420361375
79 1 0.7982513947 0.4981721634 0.1195602234
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(Intercept) x4 x5
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1734 1 -8.666344e-02 0.0991261760
1735 1 -1.945836e+00 -0.7518397924
1736 1 1.228610e-01 0.4858432846
1737 1 -5.597571e-01 0.8989739752
1738 1 1.738050e-01 -0.3310830127
1739 1 -1.114171e+00 0.4353175344
1740 1 1.542912e+00 1.2937614525
1741 1 -6.141291e-01 -0.3874479475
1742 1 -1.554983e+00 1.3903004617
1743 1 1.252959e+00 -0.0256571166
1744 1 -6.277004e-02 -0.5983056431
1745 1 -8.237522e-01 -0.3565577560
1746 1 4.715598e-01 1.1783449197
1747 1 2.435759e-01 1.3123345264
1748 1 9.583958e-02 -0.4244011442
1749 1 1.400195e+00 -0.5282769995
1750 1 1.366558e+00 -0.4909327140
1751 1 2.341003e+00 -0.0139757233
1752 1 -4.267985e-01 -0.1402644546
1753 1 -4.984207e-01 -1.2036924041
1754 1 -6.721213e-02 -0.0360624334
1755 1 -8.268947e-01 -1.8522687381
1756 1 2.396513e+00 -0.0393937007
1757 1 -6.653453e-01 0.8102485993
1758 1 9.727282e-01 -0.1690086023
1759 1 -3.267450e-01 -0.0304300843
1760 1 4.554780e-03 -0.7651542103
1761 1 -6.108986e-01 1.2079985337
1762 1 -4.492637e-01 0.2893496357
1763 1 3.012417e-02 0.3644379394
1764 1 7.811401e-01 -0.8003054104
1765 1 -6.655344e-01 1.2522781812
1766 1 -4.364759e-01 -2.1767380125
1767 1 -4.497005e-01 -0.6242738875
1768 1 3.094930e-01 0.0482619094
1769 1 7.822935e-01 0.7664588957
1770 1 -1.685372e+00 -1.9434335372
1771 1 -3.847909e-01 -0.1241172515
1772 1 -4.402048e-01 -2.2989330729
1773 1 1.327805e-01 0.0516010918
1774 1 8.549399e-02 -1.0137472547
1775 1 -5.085265e-01 0.1399205875
1776 1 -4.007824e-01 -0.0338663015
1777 1 -6.262117e-01 -0.6866873990
1778 1 -6.671056e-01 -0.4454675787
1779 1 3.517230e-01 -0.7363912757
1780 1 1.819257e-01 1.0295502952
1781 1 -4.341776e-01 0.0425279426
1782 1 9.859413e-01 1.1892380171
1783 1 -8.124890e-01 0.5145974666
1784 1 -1.560004e-01 -1.5456924256
1785 1 9.345811e-02 -0.9926720082
1786 1 -1.183550e+00 0.1897786782
1787 1 5.695106e-01 0.6148896503
1788 1 9.605166e-01 0.7140920204
1789 1 1.586373e+00 -1.6345681822
1790 1 -7.917502e-01 -0.6206343527
1791 1 -6.595907e-01 1.7160863283
1792 1 -7.074627e-01 -1.0769070077
1793 1 2.833266e-01 0.8239320697
1794 1 5.278889e-01 0.7641110487
1795 1 1.309652e+00 0.4882788297
1796 1 -7.804176e-01 -0.9556785998
1797 1 3.465911e-01 -1.7229538653
1798 1 -6.167691e-01 0.8944249848
1799 1 -1.625414e+00 -1.3436603900
1800 1 -1.143037e+00 1.9002917475
1801 1 2.054702e-01 1.0029925229
1802 1 5.353407e-01 -0.7690727847
1803 1 1.015901e+00 0.1155251947
1804 1 -1.191417e+00 -0.1543038812
1805 1 -1.326809e+00 -0.2756281241
1806 1 1.075503e+00 0.8886335444
1807 1 -1.619707e+00 1.7773332474
1808 1 -1.695149e-01 -1.4619763104
1809 1 -6.982581e-01 1.1239117771
1810 1 -6.241596e-01 1.3249132144
1811 1 -4.093872e-01 -0.9922053338
1812 1 -7.187791e-01 -1.9239194595
1813 1 -6.310097e-02 1.5328395713
1814 1 1.334947e+00 0.5161138958
1815 1 -1.113178e+00 0.4901558214
1816 1 -1.064865e+00 -1.5481653098
1817 1 1.260519e+00 -0.0184239077
1818 1 -7.163494e-01 0.9711552717
1819 1 -7.741003e-01 -0.0258881988
1820 1 3.700284e-01 0.0806034912
1821 1 -2.542174e-01 -0.7069376983
1822 1 -3.425698e-01 -0.3410852716
1823 1 1.180620e+00 1.5151556945
1824 1 1.582217e+00 0.6093844087
1825 1 1.704888e+00 0.3980028120
1826 1 5.648630e-01 -0.0242636963
1827 1 -4.764913e-01 0.0420294559
1828 1 6.199924e-01 1.4681257278
1829 1 -3.674056e-01 -1.5581161430
1830 1 -5.291112e-01 0.7345380436
1831 1 -5.214788e-01 0.5091522708
1832 1 -8.675957e-01 -1.2417965638
1833 1 9.280934e-01 -0.6573551916
1834 1 -3.513386e-01 1.9991006433
1835 1 8.122890e-01 -1.2409382629
1836 1 5.343666e-01 0.7339604213
1837 1 8.233797e-02 0.4251411363
1838 1 2.119009e-01 -0.8518324261
1839 1 9.254859e-02 1.8774709870
1840 1 -6.365326e-01 1.5379707312
1841 1 -9.475213e-01 0.2387941811
1842 1 1.362999e+00 0.4398575877
1843 1 -1.493494e+00 -0.1744773007
1844 1 1.253609e-02 -0.1416450733
1845 1 1.972215e-01 -1.1871818361
1846 1 1.564884e+00 0.9718870819
1847 1 -5.352198e-01 2.4116529941
1848 1 1.276402e-01 0.4664958261
1849 1 -2.546610e-01 0.8671421158
1850 1 -1.295310e+00 1.9498100600
1851 1 8.712637e-01 -0.5059742897
1852 1 1.042755e+00 -0.2848301306
1853 1 -6.548728e-01 -1.5371535657
1854 1 4.061649e-01 -0.4419641039
1855 1 -3.701574e-01 0.3595010880
1856 1 -8.494660e-01 0.2668094484
1857 1 -4.235023e-01 0.4601201698
1858 1 3.118541e-01 2.5699594034
1859 1 3.858923e-01 1.2908077867
1860 1 5.128240e-01 0.7792068180
1861 1 -4.541796e-01 0.8808773380
1862 1 2.345021e-01 -0.4485462168
1863 1 1.720489e-01 -2.0859594727
1864 1 -5.309989e-01 -0.3731444918
1865 1 -7.640969e-01 1.4966359941
1866 1 1.065466e-02 -0.5176479737
1867 1 -8.209414e-02 -1.1825741286
1868 1 1.269886e+00 -1.3231069934
1869 1 6.340240e-01 -1.2157261114
1870 1 3.964723e-01 -1.9724188031
1871 1 2.129687e-01 -0.2249344209
1872 1 -2.274793e-02 0.4775947994
1873 1 -1.955873e-01 0.1809316898
1874 1 -2.882144e-01 -1.6730899755
1875 1 -8.519617e-02 -0.6308489260
1876 1 -7.614977e-01 -0.4360083612
1877 1 -3.254867e-01 0.2090606673
1878 1 -2.211325e-01 0.2929710562
1879 1 -1.710826e+00 2.3779783778
1880 1 -3.713471e-01 -1.4813535976
1881 1 -1.191724e+00 -2.4675259417
1882 1 5.749024e-01 -0.5813851051
1883 1 -8.643900e-01 -0.2807471520
1884 1 3.922759e-01 -0.3601650438
1885 1 1.321946e+00 -0.2270587191
1886 1 1.275346e+00 0.0060096421
1887 1 -1.461178e+00 1.4219869387
1888 1 4.263679e-01 -0.6560333749
1889 1 1.069635e-01 0.7154114440
1890 1 3.954492e-01 -1.2810537354
1891 1 -5.094093e-01 0.2116882009
1892 1 6.522543e-01 0.4138620126
1893 1 6.110963e-01 -0.7819688049
1894 1 8.737319e-01 2.1829251895
1895 1 -1.470085e+00 0.5001709711
1896 1 -1.193908e-01 0.7720357885
1897 1 1.441880e+00 2.6635069516
1898 1 9.807212e-01 -0.1918281006
1899 1 -1.549435e-01 -0.0477416512
1900 1 -6.565341e-01 -0.7303512121
1901 1 -2.218050e+00 -0.1248124005
1902 1 1.565099e+00 -1.2543759396
1903 1 -6.645336e-01 -0.0018166585
1904 1 -1.106672e+00 0.0184418854
1905 1 8.962007e-01 -1.5790753894
1906 1 -4.423003e-01 1.7012976526
1907 1 -5.403642e-03 -0.4247147067
1908 1 -1.067551e+00 -0.5569274770
1909 1 -6.025665e-01 -0.3705957323
1910 1 1.091899e+00 -0.2698253087
1911 1 6.321106e-01 -1.0712310012
1912 1 -6.891076e-02 1.5093042332
1913 1 -5.260184e-01 -0.2522409869
1914 1 -9.503961e-01 -1.1194321510
1915 1 1.429524e-01 0.0355176979
1916 1 -1.126099e+00 -0.7397448082
1917 1 2.747486e+00 -0.2091672198
1918 1 -4.562190e-01 -0.2211406219
1919 1 -1.615861e+00 -0.1960416425
1920 1 4.395728e-01 0.0851074619
1921 1 2.996376e-01 -0.1635599861
1922 1 1.142633e+00 1.2442966123
1923 1 -1.601222e+00 -0.8298963397
1924 1 -1.514884e+00 -0.9578456729
1925 1 -8.502370e-01 -0.3434408738
1926 1 -4.536467e-01 -0.9954231651
1927 1 7.029974e-02 0.3539936878
1928 1 -1.713754e-01 -1.8076249912
1929 1 1.063081e+00 -0.5676861874
1930 1 -2.771234e-01 0.0987148898
1931 1 -2.123883e-01 -0.7708787733
1932 1 1.524831e+00 -0.3909979096
1933 1 1.754834e-01 -0.3555536583
1934 1 -1.893939e+00 -0.1499947922
1935 1 -7.669144e-01 1.3458038956
1936 1 -2.528206e-01 1.3359909556
1937 1 8.316751e-01 0.3266016616
1938 1 1.010643e+00 -1.0517406052
1939 1 1.796983e+00 -0.5284336240
1940 1 7.229651e-01 -1.1143800298
1941 1 4.178250e-01 -0.0049648993
1942 1 1.196199e-01 0.4954238593
1943 1 1.825934e+00 1.9632261086
1944 1 -1.567389e+00 0.0977832811
1945 1 2.665720e-01 -1.3769637483
1946 1 -2.851051e-02 -0.1787249146
1947 1 7.168300e-01 2.4089706463
1948 1 9.548940e-02 1.1836679379
1949 1 -1.699918e-01 -0.1629387394
1950 1 7.732266e-01 0.4788108005
1951 1 1.055910e+00 1.4768883954
1952 1 1.301988e+00 1.2483121552
1953 1 -5.629535e-01 1.8736015682
1954 1 -1.046239e+00 -1.4697312412
1955 1 7.296283e-01 0.3759873430
1956 1 -1.134532e+00 -0.7568702574
1957 1 -2.730316e+00 0.7899478570
1958 1 6.522059e-01 -0.7758246883
1959 1 -9.077401e-01 1.7400984741
1960 1 -1.746243e-01 0.7273764157
1961 1 -1.749175e+00 0.0412251647
1962 1 6.351639e-01 -0.7465530475
1963 1 3.928764e-01 -0.4669105152
1964 1 1.469288e-01 0.5619024171
1965 1 -4.416916e-02 0.9213398038
1966 1 5.095332e-01 1.3139610919
1967 1 1.036623e+00 -0.6321055477
1968 1 -1.017870e+00 1.1922587748
1969 1 1.540566e+00 -0.2953318428
1970 1 1.905289e+00 -0.7622792482
1971 1 4.042105e-01 0.5947329044
1972 1 -7.069767e-01 1.5472486383
1973 1 -1.008285e+00 0.6816846166
1974 1 4.863330e-01 -0.0116558350
1975 1 -7.022979e-01 0.7742367691
1976 1 3.093437e-01 0.2707596966
1977 1 -1.078647e+00 -0.1883596530
1978 1 5.902743e-01 0.6246672753
1979 1 6.584944e-01 1.4193192881
1980 1 2.048185e+00 -0.9067758246
1981 1 -1.448530e+00 -0.3987914948
1982 1 1.267581e+00 1.4135953220
1983 1 -6.959804e-01 0.2584286770
1984 1 7.405956e-02 -1.2725183814
1985 1 4.446582e-02 -0.0753325430
1986 1 -4.317039e-01 0.0703880778
1987 1 1.688329e+00 -0.7760323143
1988 1 1.932779e-01 -1.6246009465
1989 1 2.666542e-01 0.8940843318
1990 1 -7.428853e-01 1.4278914745
1991 1 2.081491e-01 1.8593265851
1992 1 -8.529617e-01 -0.3979564585
1993 1 9.235143e-01 0.4129440992
1994 1 1.354395e+00 -0.2755167794
1995 1 5.697907e-01 0.8813467819
1996 1 -2.468826e-02 1.6829380542
1997 1 6.948899e-01 0.6945449748
1998 1 -7.752261e-02 0.5096265680
1999 1 4.085734e-01 0.4243439554
2000 1 -1.587949e+00 0.0181455085
attr(,"assign")
[1] 0 1 2
$calc_lik
function (beta)
{
for (n in names(obj$model$submods)) assign(n, eval(get(n)))
p <- eval(obj$model$boolmod)
log.p <- log(p)
log.p1 <- log(1 - p)
eval(llik)
}
<bytecode: 0x55f20e31a5d0>
<environment: 0x55f21149e268>
$method
[1] "nlm" "nlminb"
$model.fit
$nlm
$nlm$par
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlm -1.962176 0.2541561 0.6954833 0.9972418 0.006893525 1.702574
x5
nlm -0.197018
$nlm$nhatend
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 115.456997 31.281691 63.6547636 100.2494259 95.25477 -21.8660312
[2,] 31.281691 117.943969 1.8807256 19.3256601 10.65402 -5.9115476
[3,] 63.654764 1.880726 134.6344701 31.1731459 39.15750 -0.5340373
[4,] 100.249426 19.325660 31.1731459 145.7169853 55.20325 -7.2822927
[5,] 95.254774 10.654019 39.1575019 55.2032487 573.49187 37.1083998
[6,] -21.866031 -5.911548 -0.5340373 -7.2822927 37.10840 171.9631686
[7,] -2.522996 -7.597149 -2.7947502 0.8884992 10.58570 38.7618385
[,7]
[1,] -2.5229956
[2,] -7.5971495
[3,] -2.7947502
[4,] 0.8884992
[5,] 10.5857040
[6,] 38.7618385
[7,] 559.8836759
$nlm$value
[1] -771.6595
$nlm$iterations
[1] 24
$nlm$conv
[1] 0
$nlm$detail
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlm -1.962176 0.2541561 0.6954833 0.9972418 0.006893525 1.702574
nlminb -1.962174 0.2541573 0.6954825 0.9972405 0.006893159 1.702572
x5 value fevals gevals niter convcode kkt1 kkt2 xtimes
nlm -0.1970180 771.6595 NA NA 24 0 TRUE TRUE 0.186
nlminb -0.1970193 771.6595 54 408 42 0 TRUE TRUE 0.416
$nlminb
$nlminb$par
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlminb -1.962174 0.2541573 0.6954825 0.9972405 0.006893159 1.702572
x5
nlminb -0.1970193
$nlminb$nhatend
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 115.457214 31.281924 63.6547716 100.2494647 95.25488 -21.8659765
[2,] 31.281924 117.944232 1.8807743 19.3257198 10.65405 -5.9115098
[3,] 63.654772 1.880774 134.6346070 31.1730783 39.15753 -0.5340214
[4,] 100.249465 19.325720 31.1730783 145.7170857 55.20325 -7.2822566
[5,] 95.254883 10.654048 39.1575254 55.2032529 573.49201 37.1084645
[6,] -21.865976 -5.911510 -0.5340214 -7.2822566 37.10846 171.9634863
[7,] -2.522868 -7.597161 -2.7946852 0.8885202 10.58556 38.7621375
[,7]
[1,] -2.5228681
[2,] -7.5971613
[3,] -2.7946852
[4,] 0.8885202
[5,] 10.5855587
[6,] 38.7621375
[7,] 559.8837337
$nlminb$value
[1] -771.6595
$nlminb$iterations
[1] 42
$nlminb$conv
[1] 0
$nlminb$detail
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlm -1.962176 0.2541561 0.6954833 0.9972418 0.006893525 1.702574
nlminb -1.962174 0.2541573 0.6954825 0.9972405 0.006893159 1.702572
x5 value fevals gevals niter convcode kkt1 kkt2 xtimes
nlm -0.1970180 771.6595 NA NA 24 0 TRUE TRUE 0.186
nlminb -0.1970193 771.6595 54 408 42 0 TRUE TRUE 0.416
attr(,"class")
[1] "boolfit" "list"
attr(,"class")
[1] "boolean"
>
> ## Re-fit, with BFGS and a higher maximum number of iterations. All
> ## of the options that go along with nlm(), optim(), and genoud() should
> ## be transparently passable through boolean.
> (fit2.OR <- boolean(mod.OR, method="BFGS", control = list(maxit = 500)))
$call
boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5, data = Df,
family = list(binomial("probit")))
$link
$link$a
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$link$b
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$model
$model$submods
$model$submods$a
[1] "x1" "x2" "x3"
$model$submods$b
[1] "x4" "x5"
$model$boolmod
expression((a %|% b))
$N
[1] 2000
$k
[1] 7
$coef.labels
[1] "(Intercept)_a" "x1_a" "x2_a" "x3_a"
[5] "(Intercept)_b" "x4_b" "x5_b"
$coef.idx
$coef.idx$a
[1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE
$coef.idx$b
[1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE
$response
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
1 1 1 0 0 0 1 1 1 0 0 1 0 1 0 1
33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
0 1 1 1 1 0 0 1 1 1 1 0 0 1 0 1
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1
65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
1 1 1 1 1 0 0 1 1 1 1 1 0 1 0 0
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0
97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
1 1 0 1 0 0 0 0 0 0 1 1 1 0 1 1
113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
1 0 0 0 0 1 0 1 0 1 1 1 0 0 1 1
129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1
145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
0 1 1 0 0 1 0 1 1 1 0 1 1 1 0 1
161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1
177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
1 1 0 1 1 0 1 1 1 1 1 0 0 0 0 0
193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
1 0 1 1 1 0 0 0 0 1 0 1 1 1 0 1
209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
0 1 0 0 1 0 1 1 1 1 1 0 1 0 1 1
225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0
241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
1 1 1 1 0 0 1 0 1 0 0 0 0 0 1 0
257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
1 0 1 1 0 1 0 1 1 0 1 0 1 0 1 0
273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1
289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304
1 1 0 0 1 1 1 1 0 1 0 0 1 1 1 0
305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320
1 1 1 0 0 1 0 0 0 0 0 0 1 1 0 1
321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
0 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1
337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352
0 1 0 1 1 0 1 1 1 1 0 1 1 1 1 0
353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
1 1 1 1 1 1 1 0 1 0 1 1 0 0 0 0
369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384
1 0 0 0 1 1 0 1 0 0 1 1 1 0 1 0
385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1
401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416
1 0 0 1 1 1 1 1 0 0 1 1 1 0 1 1
417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1
433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448
1 0 0 1 1 1 1 0 1 1 0 0 1 0 1 1
449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464
1 1 0 1 1 0 1 1 0 0 1 1 1 0 1 0
465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 1
481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
1 0 1 1 1 1 0 0 1 0 1 0 0 0 0 1
497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
0 0 1 1 0 0 0 0 1 1 0 0 1 1 1 1
513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
1 0 1 1 0 1 0 0 1 1 0 0 0 0 1 0
529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
0 1 0 1 0 0 0 1 1 0 1 1 1 1 0 1
545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560
1 1 1 0 1 1 0 1 1 1 1 1 0 0 0 1
561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
1 1 1 1 0 1 1 0 1 0 1 0 1 0 0 0
577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592
1 1 1 0 1 0 1 0 1 1 0 1 1 1 0 0
593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608
1 1 1 0 0 0 1 1 0 0 1 1 0 1 0 0
609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
1 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0
625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640
0 1 0 1 1 0 1 1 0 1 0 0 0 1 0 1
641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1
657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672
1 1 1 0 1 1 0 0 0 1 0 0 0 0 1 1
673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
0 0 1 0 1 1 1 0 1 1 0 1 1 0 0 0
689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704
0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720
0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1
721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
1 1 0 1 1 1 1 1 0 0 0 1 1 0 1 1
737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752
0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 1
753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
1 0 1 1 0 1 1 1 0 1 1 1 0 0 0 0
769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784
0 0 0 1 1 1 0 0 0 1 1 1 1 0 0 0
785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
1 0 1 1 0 1 1 1 0 1 1 1 0 0 1 0
801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
0 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1
817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 0
833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848
1 1 1 0 0 0 1 0 1 0 1 0 0 0 1 0
849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864
1 1 1 1 0 1 0 0 1 1 1 1 0 0 0 0
865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880
0 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1
881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896
0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1
897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912
0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 1
913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
0 0 0 1 1 0 1 1 0 0 0 1 0 0 1 1
929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944
0 1 1 1 0 1 0 1 0 1 1 0 1 1 1 1
945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0
961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 0
977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
0 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1
1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
1 0 0 1 1 1 1 0 1 1 0 1 1 1 1 0
1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 1
1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
0 0 0 1 1 0 1 1 1 0 0 1 1 0 0 1
1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
0 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0
1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088
0 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1
1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
1 0 0 0 1 1 1 1 0 1 0 1 1 1 1 0
1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
0 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0
1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
1 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0
1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 0
1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168
0 0 1 0 1 1 1 1 1 0 0 1 1 1 0 0
1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
0 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1
1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0
1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216
1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1
1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 1
1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
0 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0
1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
0 0 1 0 1 0 1 1 0 0 1 0 1 1 0 1
1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
0 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1
1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
0 1 1 0 1 1 0 1 1 0 0 1 1 0 1 1
1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
0 1 1 1 0 0 0 0 0 1 1 0 1 0 1 0
1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0
1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
0 1 0 0 0 0 1 1 0 1 1 0 1 0 0 0
1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 1
1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
1 0 1 1 0 0 1 0 1 1 0 0 0 0 0 1
1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
1 0 0 0 1 1 0 0 1 0 1 1 1 1 0 0
1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
1 0 0 1 1 0 1 0 0 1 0 1 0 0 1 0
1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
1 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0
1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
1 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0
1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456
1 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1
1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472
1 1 1 1 1 0 0 1 0 1 0 1 1 1 0 0
1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488
0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1
1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1
1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520
1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1
1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
1 0 0 1 1 0 0 1 0 0 1 1 1 1 1 0
1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552
1 0 1 1 1 1 1 0 1 0 0 1 1 1 0 0
1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568
0 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0
1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584
0 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1
1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600
1 1 1 1 0 1 0 0 0 0 1 0 1 1 0 1
1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
0 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1
1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632
0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 1
1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648
1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1
1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664
1 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0
1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680
1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0
1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696
1 0 1 1 0 0 1 1 0 0 1 1 1 1 1 1
1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712
1 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0
1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1
1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744
0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0
1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760
0 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1
1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776
0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 1
1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792
1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1
1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808
1 1 1 0 1 0 0 0 1 1 1 0 0 1 0 0
1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824
0 1 1 0 0 1 0 0 1 1 1 1 0 1 1 1
1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840
1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0
1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856
0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 0
1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872
0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 0
1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888
0 1 0 0 1 0 0 0 1 1 0 1 1 1 0 1
1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904
0 0 0 1 1 1 1 1 1 1 0 0 0 1 0 0
1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 0
1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936
1 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0
1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952
1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1
1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968
1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 0
1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
1 1 1 0 1 1 0 1 0 0 0 1 0 1 1 0
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1
$frame
$frame$a
(Intercept) x1 x2 x3
1 1 0.5855288178 0.7094660175 -0.1093033147
2 1 -1.8179559677 0.6300985511 -0.2761841052
3 1 -0.1162478064 1.8173120437 0.3706278643
4 1 0.8168998395 -0.8863575212 -0.3315775899
5 1 0.7796219246 1.4557850825 -0.6443284292
6 1 1.8050975188 -0.4816473637 0.6203798013
7 1 0.8118731786 2.1968335463 2.0491903374
8 1 0.4911882793 -0.3240865787 -1.6620502439
9 1 1.1285108336 -2.3803580614 -1.0602655522
10 1 1.4607294031 -1.4130987779 0.5674032534
11 1 -0.5403860739 1.9476926647 0.0535902704
12 1 0.2779536948 0.6911712729 0.8237953287
13 1 0.1495919806 -1.3425314817 0.5533030755
14 1 -1.8323773062 0.8881394327 1.5934884724
15 1 0.0546155753 -0.7846493732 -1.0493528190
16 1 0.9426008507 0.8262582872 -0.8115404902
17 1 0.6453830742 1.0431435516 -0.3043691127
18 1 1.8670991846 0.6720424690 -0.3079533808
19 1 -0.9639014790 -0.8550825111 1.8869469420
20 1 0.6873321006 -0.5050435175 2.1577198165
21 1 0.2239254075 -1.1562233302 0.4224185278
22 1 -0.5360479985 -0.3116060846 1.5561096426
23 1 -1.2301722469 -1.3240586922 1.2612422744
24 1 -0.5050898063 -0.0521535933 0.6288606251
25 1 1.5448636020 1.3214520172 0.3221515754
26 1 -1.1588210228 -1.8453682894 1.1573252871
27 1 1.6421919908 0.8836548332 0.5248758910
28 1 -1.0479137102 -1.0111225236 0.6689216525
29 1 -1.1402641448 -1.2937152900 -0.5946987691
30 1 0.5401695677 -1.5472919670 0.8496529299
31 1 -1.6193283224 0.5483979230 0.1952821538
32 1 -0.2509466239 1.6993466685 -0.3442987963
33 1 -0.4876385152 0.3031512373 -0.2419740196
34 1 -0.2806491388 0.6330173442 -1.2398183431
35 1 0.1999204838 1.3471927776 0.0360734878
36 1 0.4809501553 2.4835500893 0.4013649875
37 1 -0.9117394242 -0.0490446909 -0.4053874767
38 1 0.0764175190 1.4537473512 0.3741210834
39 1 0.5435221079 -0.5051860029 0.7867957894
40 1 0.7984337703 0.8508604376 -0.4435679681
41 1 -1.4361457062 -0.6292596473 0.2435217656
42 1 0.1052118160 -1.7417130945 0.6452469938
43 1 0.9919506777 -0.8592507612 -0.2815796883
44 1 0.3156128247 0.6602933781 -1.7222024071
45 1 0.8678217426 -2.2900441795 -0.1501902882
46 1 0.6722680417 -0.2093011363 0.0121825061
47 1 0.0784375327 -0.7792610704 0.1665596687
48 1 -0.4678883695 0.7583745568 -0.6417363578
49 1 -0.7000758167 -0.5674015851 -0.2613939259
50 1 0.7711037397 2.7474035422 -0.0839347571
51 1 -0.8086741277 1.0011198474 0.4560525046
52 1 0.6417691636 -0.4150210287 -0.4595756762
53 1 0.7108899984 1.2676017522 -0.1431510558
54 1 -0.1625889099 0.0417091744 0.4830398961
55 1 -0.6346498942 -0.7019630314 0.5768503789
56 1 1.1471271903 0.0147936486 -0.3117392439
57 1 -1.5138640797 0.1642809977 -0.8708652186
58 1 0.3573696852 0.1023930632 -0.6752668252
59 1 -0.4282856231 -0.7139247637 -0.1903840656
60 1 0.1837369095 -2.1503105297 -0.6229665257
61 1 0.5222821685 0.0097937599 -0.4405261962
62 1 0.0382097874 1.1948056324 0.3439583521
63 1 -0.9180582028 -0.0878073286 1.3202937198
64 1 -0.3157324793 -0.5750961042 -1.4063564356
65 1 0.3803156970 0.6051367728 1.0196741368
66 1 0.9331921670 0.4754354212 0.3902816000
67 1 1.4239835987 0.4847305112 0.3492364459
68 1 0.3670449214 -1.5191990495 1.5498033320
69 1 2.0767099333 -0.3076505656 0.9523708852
70 1 -0.1420633784 -1.1830257110 0.5453184095
71 1 0.2929403027 -0.0867079053 -1.4663594955
72 1 -0.3603208006 0.3505937650 0.0282576562
73 1 -0.3758234363 -1.8128337643 0.2886002111
74 1 0.6504302424 0.3102549854 1.6683571689
75 1 -0.1460263920 1.7014318317 0.4713578806
76 1 -0.7789022252 1.1633248478 -1.9645441952
77 1 -0.4772722914 -0.1025805088 0.3686961762
78 1 -0.1505570976 0.9042435562 2.2420361375
79 1 0.7982513947 0.4981721634 0.1195602234
80 1 0.2627041248 0.6407388780 0.3070898308
81 1 0.6279651131 0.0021439509 0.2843777227
82 1 0.8281942395 -0.0848186457 -0.4347199169
83 1 -0.9712292030 -0.1609133622 1.2046870348
84 1 -0.0199197380 0.3280164800 -0.9306286167
85 1 0.5142421022 0.2931683808 -0.5626548305
86 1 -0.4300125693 1.3319083517 -0.7632671561
87 1 -0.8225400839 -0.0476483565 0.1706780107
88 1 0.4906677770 2.2391260841 0.8922123090
89 1 0.3942498996 0.6325214944 0.1025911025
90 1 0.5008706583 -0.9094679509 -2.1972085120
91 1 0.7130781039 1.0282685391 -0.5706327254
92 1 1.2035448160 1.4961826970 -0.1565984322
93 1 -1.2861886466 -0.2725228551 0.1569148369
94 1 -0.8552506726 -1.0057287350 -0.9139523908
95 1 1.2996376499 -0.0293687832 -2.2366218376
96 1 -0.4975791431 -0.6010274043 -0.5121978141
97 1 -0.0240353973 -0.1273903376 -0.1318856204
98 1 0.2109307870 0.7523360895 0.1383795467
99 1 -1.1627837168 0.8546585194 -0.2420958786
100 1 0.2241698326 -0.7368249216 -0.8467107405
101 1 -1.4203238585 -2.4669386469 0.4847158355
102 1 -0.1629454633 0.2204557848 0.8762108117
103 1 -1.9984928459 -1.2347803996 0.8463037177
104 1 0.7674546604 0.8671015019 -1.2545811490
105 1 0.4130208684 0.5642562609 0.7260659521
106 1 0.2242920505 0.7864091404 -0.4233628372
107 1 -0.1835378759 0.8348719973 -0.5872005112
108 1 0.6454110524 -0.3123199556 1.1854482829
109 1 2.1274630349 -0.1877064794 0.0987578449
110 1 2.0930679948 -0.6922359279 -2.2871981109
111 1 -0.4712225866 -0.0332096774 0.3773670678
112 1 2.1185039306 -1.0874598695 1.2160333866
113 1 0.1128439417 0.0652135748 -0.6306272111
114 1 -0.6816232013 -0.0371627219 -0.2872743152
115 1 0.8366902458 -0.3298962638 0.5348022561
116 1 0.5761647328 1.2362238791 0.4755396380
117 1 0.6800620909 -1.3502015698 0.2451024445
118 1 0.2013818061 -1.5754628329 0.0235387774
119 1 -0.5269106809 -1.3128588906 0.8008613463
120 1 1.6818084980 -0.2263103959 -0.1979290319
121 1 -1.6366290973 0.2115625932 -0.4648317284
122 1 -1.3217017095 0.0766927813 -0.3849908794
123 1 -0.4582940078 0.2288621447 -1.3964076285
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1787 1 5.695106e-01 0.6148896503
1788 1 9.605166e-01 0.7140920204
1789 1 1.586373e+00 -1.6345681822
1790 1 -7.917502e-01 -0.6206343527
1791 1 -6.595907e-01 1.7160863283
1792 1 -7.074627e-01 -1.0769070077
1793 1 2.833266e-01 0.8239320697
1794 1 5.278889e-01 0.7641110487
1795 1 1.309652e+00 0.4882788297
1796 1 -7.804176e-01 -0.9556785998
1797 1 3.465911e-01 -1.7229538653
1798 1 -6.167691e-01 0.8944249848
1799 1 -1.625414e+00 -1.3436603900
1800 1 -1.143037e+00 1.9002917475
1801 1 2.054702e-01 1.0029925229
1802 1 5.353407e-01 -0.7690727847
1803 1 1.015901e+00 0.1155251947
1804 1 -1.191417e+00 -0.1543038812
1805 1 -1.326809e+00 -0.2756281241
1806 1 1.075503e+00 0.8886335444
1807 1 -1.619707e+00 1.7773332474
1808 1 -1.695149e-01 -1.4619763104
1809 1 -6.982581e-01 1.1239117771
1810 1 -6.241596e-01 1.3249132144
1811 1 -4.093872e-01 -0.9922053338
1812 1 -7.187791e-01 -1.9239194595
1813 1 -6.310097e-02 1.5328395713
1814 1 1.334947e+00 0.5161138958
1815 1 -1.113178e+00 0.4901558214
1816 1 -1.064865e+00 -1.5481653098
1817 1 1.260519e+00 -0.0184239077
1818 1 -7.163494e-01 0.9711552717
1819 1 -7.741003e-01 -0.0258881988
1820 1 3.700284e-01 0.0806034912
1821 1 -2.542174e-01 -0.7069376983
1822 1 -3.425698e-01 -0.3410852716
1823 1 1.180620e+00 1.5151556945
1824 1 1.582217e+00 0.6093844087
1825 1 1.704888e+00 0.3980028120
1826 1 5.648630e-01 -0.0242636963
1827 1 -4.764913e-01 0.0420294559
1828 1 6.199924e-01 1.4681257278
1829 1 -3.674056e-01 -1.5581161430
1830 1 -5.291112e-01 0.7345380436
1831 1 -5.214788e-01 0.5091522708
1832 1 -8.675957e-01 -1.2417965638
1833 1 9.280934e-01 -0.6573551916
1834 1 -3.513386e-01 1.9991006433
1835 1 8.122890e-01 -1.2409382629
1836 1 5.343666e-01 0.7339604213
1837 1 8.233797e-02 0.4251411363
1838 1 2.119009e-01 -0.8518324261
1839 1 9.254859e-02 1.8774709870
1840 1 -6.365326e-01 1.5379707312
1841 1 -9.475213e-01 0.2387941811
1842 1 1.362999e+00 0.4398575877
1843 1 -1.493494e+00 -0.1744773007
1844 1 1.253609e-02 -0.1416450733
1845 1 1.972215e-01 -1.1871818361
1846 1 1.564884e+00 0.9718870819
1847 1 -5.352198e-01 2.4116529941
1848 1 1.276402e-01 0.4664958261
1849 1 -2.546610e-01 0.8671421158
1850 1 -1.295310e+00 1.9498100600
1851 1 8.712637e-01 -0.5059742897
1852 1 1.042755e+00 -0.2848301306
1853 1 -6.548728e-01 -1.5371535657
1854 1 4.061649e-01 -0.4419641039
1855 1 -3.701574e-01 0.3595010880
1856 1 -8.494660e-01 0.2668094484
1857 1 -4.235023e-01 0.4601201698
1858 1 3.118541e-01 2.5699594034
1859 1 3.858923e-01 1.2908077867
1860 1 5.128240e-01 0.7792068180
1861 1 -4.541796e-01 0.8808773380
1862 1 2.345021e-01 -0.4485462168
1863 1 1.720489e-01 -2.0859594727
1864 1 -5.309989e-01 -0.3731444918
1865 1 -7.640969e-01 1.4966359941
1866 1 1.065466e-02 -0.5176479737
1867 1 -8.209414e-02 -1.1825741286
1868 1 1.269886e+00 -1.3231069934
1869 1 6.340240e-01 -1.2157261114
1870 1 3.964723e-01 -1.9724188031
1871 1 2.129687e-01 -0.2249344209
1872 1 -2.274793e-02 0.4775947994
1873 1 -1.955873e-01 0.1809316898
1874 1 -2.882144e-01 -1.6730899755
1875 1 -8.519617e-02 -0.6308489260
1876 1 -7.614977e-01 -0.4360083612
1877 1 -3.254867e-01 0.2090606673
1878 1 -2.211325e-01 0.2929710562
1879 1 -1.710826e+00 2.3779783778
1880 1 -3.713471e-01 -1.4813535976
1881 1 -1.191724e+00 -2.4675259417
1882 1 5.749024e-01 -0.5813851051
1883 1 -8.643900e-01 -0.2807471520
1884 1 3.922759e-01 -0.3601650438
1885 1 1.321946e+00 -0.2270587191
1886 1 1.275346e+00 0.0060096421
1887 1 -1.461178e+00 1.4219869387
1888 1 4.263679e-01 -0.6560333749
1889 1 1.069635e-01 0.7154114440
1890 1 3.954492e-01 -1.2810537354
1891 1 -5.094093e-01 0.2116882009
1892 1 6.522543e-01 0.4138620126
1893 1 6.110963e-01 -0.7819688049
1894 1 8.737319e-01 2.1829251895
1895 1 -1.470085e+00 0.5001709711
1896 1 -1.193908e-01 0.7720357885
1897 1 1.441880e+00 2.6635069516
1898 1 9.807212e-01 -0.1918281006
1899 1 -1.549435e-01 -0.0477416512
1900 1 -6.565341e-01 -0.7303512121
1901 1 -2.218050e+00 -0.1248124005
1902 1 1.565099e+00 -1.2543759396
1903 1 -6.645336e-01 -0.0018166585
1904 1 -1.106672e+00 0.0184418854
1905 1 8.962007e-01 -1.5790753894
1906 1 -4.423003e-01 1.7012976526
1907 1 -5.403642e-03 -0.4247147067
1908 1 -1.067551e+00 -0.5569274770
1909 1 -6.025665e-01 -0.3705957323
1910 1 1.091899e+00 -0.2698253087
1911 1 6.321106e-01 -1.0712310012
1912 1 -6.891076e-02 1.5093042332
1913 1 -5.260184e-01 -0.2522409869
1914 1 -9.503961e-01 -1.1194321510
1915 1 1.429524e-01 0.0355176979
1916 1 -1.126099e+00 -0.7397448082
1917 1 2.747486e+00 -0.2091672198
1918 1 -4.562190e-01 -0.2211406219
1919 1 -1.615861e+00 -0.1960416425
1920 1 4.395728e-01 0.0851074619
1921 1 2.996376e-01 -0.1635599861
1922 1 1.142633e+00 1.2442966123
1923 1 -1.601222e+00 -0.8298963397
1924 1 -1.514884e+00 -0.9578456729
1925 1 -8.502370e-01 -0.3434408738
1926 1 -4.536467e-01 -0.9954231651
1927 1 7.029974e-02 0.3539936878
1928 1 -1.713754e-01 -1.8076249912
1929 1 1.063081e+00 -0.5676861874
1930 1 -2.771234e-01 0.0987148898
1931 1 -2.123883e-01 -0.7708787733
1932 1 1.524831e+00 -0.3909979096
1933 1 1.754834e-01 -0.3555536583
1934 1 -1.893939e+00 -0.1499947922
1935 1 -7.669144e-01 1.3458038956
1936 1 -2.528206e-01 1.3359909556
1937 1 8.316751e-01 0.3266016616
1938 1 1.010643e+00 -1.0517406052
1939 1 1.796983e+00 -0.5284336240
1940 1 7.229651e-01 -1.1143800298
1941 1 4.178250e-01 -0.0049648993
1942 1 1.196199e-01 0.4954238593
1943 1 1.825934e+00 1.9632261086
1944 1 -1.567389e+00 0.0977832811
1945 1 2.665720e-01 -1.3769637483
1946 1 -2.851051e-02 -0.1787249146
1947 1 7.168300e-01 2.4089706463
1948 1 9.548940e-02 1.1836679379
1949 1 -1.699918e-01 -0.1629387394
1950 1 7.732266e-01 0.4788108005
1951 1 1.055910e+00 1.4768883954
1952 1 1.301988e+00 1.2483121552
1953 1 -5.629535e-01 1.8736015682
1954 1 -1.046239e+00 -1.4697312412
1955 1 7.296283e-01 0.3759873430
1956 1 -1.134532e+00 -0.7568702574
1957 1 -2.730316e+00 0.7899478570
1958 1 6.522059e-01 -0.7758246883
1959 1 -9.077401e-01 1.7400984741
1960 1 -1.746243e-01 0.7273764157
1961 1 -1.749175e+00 0.0412251647
1962 1 6.351639e-01 -0.7465530475
1963 1 3.928764e-01 -0.4669105152
1964 1 1.469288e-01 0.5619024171
1965 1 -4.416916e-02 0.9213398038
1966 1 5.095332e-01 1.3139610919
1967 1 1.036623e+00 -0.6321055477
1968 1 -1.017870e+00 1.1922587748
1969 1 1.540566e+00 -0.2953318428
1970 1 1.905289e+00 -0.7622792482
1971 1 4.042105e-01 0.5947329044
1972 1 -7.069767e-01 1.5472486383
1973 1 -1.008285e+00 0.6816846166
1974 1 4.863330e-01 -0.0116558350
1975 1 -7.022979e-01 0.7742367691
1976 1 3.093437e-01 0.2707596966
1977 1 -1.078647e+00 -0.1883596530
1978 1 5.902743e-01 0.6246672753
1979 1 6.584944e-01 1.4193192881
1980 1 2.048185e+00 -0.9067758246
1981 1 -1.448530e+00 -0.3987914948
1982 1 1.267581e+00 1.4135953220
1983 1 -6.959804e-01 0.2584286770
1984 1 7.405956e-02 -1.2725183814
1985 1 4.446582e-02 -0.0753325430
1986 1 -4.317039e-01 0.0703880778
1987 1 1.688329e+00 -0.7760323143
1988 1 1.932779e-01 -1.6246009465
1989 1 2.666542e-01 0.8940843318
1990 1 -7.428853e-01 1.4278914745
1991 1 2.081491e-01 1.8593265851
1992 1 -8.529617e-01 -0.3979564585
1993 1 9.235143e-01 0.4129440992
1994 1 1.354395e+00 -0.2755167794
1995 1 5.697907e-01 0.8813467819
1996 1 -2.468826e-02 1.6829380542
1997 1 6.948899e-01 0.6945449748
1998 1 -7.752261e-02 0.5096265680
1999 1 4.085734e-01 0.4243439554
2000 1 -1.587949e+00 0.0181455085
attr(,"assign")
[1] 0 1 2
$calc_lik
function (beta)
{
for (n in names(obj$model$submods)) assign(n, eval(get(n)))
p <- eval(obj$model$boolmod)
log.p <- log(p)
log.p1 <- log(1 - p)
eval(llik)
}
<bytecode: 0x55f20e31a5d0>
<environment: 0x55f21149e268>
$method
[1] "BFGS"
$model.fit
$BFGS
$BFGS$par
X.Intercept. x1 x2 x3 X.Intercept..1 x4 x5
BFGS -1.96264 0.25421 0.6956157 0.9975053 0.006945397 1.70257 -0.1970051
$BFGS$nhatend
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 115.415456 31.274778 63.6456959 100.237354 95.23245 -21.8675831
[2,] 31.274778 117.904100 1.8828406 19.327963 10.65650 -5.9135497
[3,] 63.645696 1.882841 134.5975938 31.183303 39.15290 -0.5354693
[4,] 100.237354 19.327963 31.1833027 145.693121 55.20376 -7.2869532
[5,] 95.232446 10.656496 39.1529009 55.203757 573.52298 37.0939880
[6,] -21.867583 -5.913550 -0.5354693 -7.286953 37.09399 171.9705056
[7,] -2.524958 -7.593851 -2.7941225 0.889493 10.58814 38.7623054
[,7]
[1,] -2.524958
[2,] -7.593851
[3,] -2.794123
[4,] 0.889493
[5,] 10.588142
[6,] 38.762305
[7,] 559.921821
$BFGS$value
[1] -771.6595
$BFGS$iterations
[1] NA
$BFGS$conv
[1] 0
$BFGS$detail
X.Intercept. x1 x2 x3 X.Intercept..1 x4 x5
BFGS -1.96264 0.25421 0.6956157 0.9975053 0.006945397 1.70257 -0.1970051
value fevals gevals niter convcode kkt1 kkt2 xtimes
BFGS 771.6595 58 18 NA 0 TRUE TRUE 0.328
attr(,"class")
[1] "boolfit" "list"
attr(,"class")
[1] "boolean"
>
> ## Induce a convergence warning message.
> (fit3.OR <- boolean(mod.OR, method="BFGS", control = list(maxit = 5)))
$call
boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5, data = Df,
family = list(binomial("probit")))
$link
$link$a
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$link$b
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$model
$model$submods
$model$submods$a
[1] "x1" "x2" "x3"
$model$submods$b
[1] "x4" "x5"
$model$boolmod
expression((a %|% b))
$N
[1] 2000
$k
[1] 7
$coef.labels
[1] "(Intercept)_a" "x1_a" "x2_a" "x3_a"
[5] "(Intercept)_b" "x4_b" "x5_b"
$coef.idx
$coef.idx$a
[1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE
$coef.idx$b
[1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE
$response
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
1 1 1 0 0 0 1 1 1 0 0 1 0 1 0 1
33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
0 1 1 1 1 0 0 1 1 1 1 0 0 1 0 1
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1
65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
1 1 1 1 1 0 0 1 1 1 1 1 0 1 0 0
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0
97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
1 1 0 1 0 0 0 0 0 0 1 1 1 0 1 1
113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
1 0 0 0 0 1 0 1 0 1 1 1 0 0 1 1
129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1
145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
0 1 1 0 0 1 0 1 1 1 0 1 1 1 0 1
161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1
177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
1 1 0 1 1 0 1 1 1 1 1 0 0 0 0 0
193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
1 0 1 1 1 0 0 0 0 1 0 1 1 1 0 1
209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
0 1 0 0 1 0 1 1 1 1 1 0 1 0 1 1
225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0
241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
1 1 1 1 0 0 1 0 1 0 0 0 0 0 1 0
257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
1 0 1 1 0 1 0 1 1 0 1 0 1 0 1 0
273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1
289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304
1 1 0 0 1 1 1 1 0 1 0 0 1 1 1 0
305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320
1 1 1 0 0 1 0 0 0 0 0 0 1 1 0 1
321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
0 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1
337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352
0 1 0 1 1 0 1 1 1 1 0 1 1 1 1 0
353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
1 1 1 1 1 1 1 0 1 0 1 1 0 0 0 0
369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384
1 0 0 0 1 1 0 1 0 0 1 1 1 0 1 0
385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1
401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416
1 0 0 1 1 1 1 1 0 0 1 1 1 0 1 1
417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1
433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448
1 0 0 1 1 1 1 0 1 1 0 0 1 0 1 1
449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464
1 1 0 1 1 0 1 1 0 0 1 1 1 0 1 0
465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 1
481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
1 0 1 1 1 1 0 0 1 0 1 0 0 0 0 1
497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
0 0 1 1 0 0 0 0 1 1 0 0 1 1 1 1
513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
1 0 1 1 0 1 0 0 1 1 0 0 0 0 1 0
529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
0 1 0 1 0 0 0 1 1 0 1 1 1 1 0 1
545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560
1 1 1 0 1 1 0 1 1 1 1 1 0 0 0 1
561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
1 1 1 1 0 1 1 0 1 0 1 0 1 0 0 0
577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592
1 1 1 0 1 0 1 0 1 1 0 1 1 1 0 0
593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608
1 1 1 0 0 0 1 1 0 0 1 1 0 1 0 0
609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
1 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0
625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640
0 1 0 1 1 0 1 1 0 1 0 0 0 1 0 1
641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1
657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672
1 1 1 0 1 1 0 0 0 1 0 0 0 0 1 1
673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
0 0 1 0 1 1 1 0 1 1 0 1 1 0 0 0
689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704
0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720
0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1
721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
1 1 0 1 1 1 1 1 0 0 0 1 1 0 1 1
737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752
0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 1
753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
1 0 1 1 0 1 1 1 0 1 1 1 0 0 0 0
769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784
0 0 0 1 1 1 0 0 0 1 1 1 1 0 0 0
785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
1 0 1 1 0 1 1 1 0 1 1 1 0 0 1 0
801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
0 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1
817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 0
833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848
1 1 1 0 0 0 1 0 1 0 1 0 0 0 1 0
849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864
1 1 1 1 0 1 0 0 1 1 1 1 0 0 0 0
865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880
0 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1
881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896
0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1
897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912
0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 1
913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
0 0 0 1 1 0 1 1 0 0 0 1 0 0 1 1
929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944
0 1 1 1 0 1 0 1 0 1 1 0 1 1 1 1
945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0
961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 0
977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
0 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1
1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
1 0 0 1 1 1 1 0 1 1 0 1 1 1 1 0
1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 1
1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
0 0 0 1 1 0 1 1 1 0 0 1 1 0 0 1
1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
0 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0
1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088
0 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1
1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
1 0 0 0 1 1 1 1 0 1 0 1 1 1 1 0
1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
0 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0
1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
1 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0
1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 0
1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168
0 0 1 0 1 1 1 1 1 0 0 1 1 1 0 0
1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
0 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1
1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0
1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216
1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1
1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 1
1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
0 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0
1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
0 0 1 0 1 0 1 1 0 0 1 0 1 1 0 1
1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
0 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1
1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
0 1 1 0 1 1 0 1 1 0 0 1 1 0 1 1
1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
0 1 1 1 0 0 0 0 0 1 1 0 1 0 1 0
1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0
1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
0 1 0 0 0 0 1 1 0 1 1 0 1 0 0 0
1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 1
1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
1 0 1 1 0 0 1 0 1 1 0 0 0 0 0 1
1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
1 0 0 0 1 1 0 0 1 0 1 1 1 1 0 0
1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
1 0 0 1 1 0 1 0 0 1 0 1 0 0 1 0
1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
1 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0
1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
1 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0
1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456
1 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1
1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472
1 1 1 1 1 0 0 1 0 1 0 1 1 1 0 0
1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488
0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1
1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1
1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520
1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1
1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
1 0 0 1 1 0 0 1 0 0 1 1 1 1 1 0
1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552
1 0 1 1 1 1 1 0 1 0 0 1 1 1 0 0
1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568
0 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0
1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584
0 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1
1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600
1 1 1 1 0 1 0 0 0 0 1 0 1 1 0 1
1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
0 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1
1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632
0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 1
1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648
1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1
1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664
1 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0
1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680
1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0
1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696
1 0 1 1 0 0 1 1 0 0 1 1 1 1 1 1
1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712
1 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0
1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1
1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744
0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0
1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760
0 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1
1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776
0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 1
1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792
1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1
1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808
1 1 1 0 1 0 0 0 1 1 1 0 0 1 0 0
1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824
0 1 1 0 0 1 0 0 1 1 1 1 0 1 1 1
1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840
1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0
1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856
0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 0
1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872
0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 0
1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888
0 1 0 0 1 0 0 0 1 1 0 1 1 1 0 1
1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904
0 0 0 1 1 1 1 1 1 1 0 0 0 1 0 0
1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 0
1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936
1 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0
1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952
1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1
1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968
1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 0
1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
1 1 1 0 1 1 0 1 0 0 0 1 0 1 1 0
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1
$frame
$frame$a
(Intercept) x1 x2 x3
1 1 0.5855288178 0.7094660175 -0.1093033147
2 1 -1.8179559677 0.6300985511 -0.2761841052
3 1 -0.1162478064 1.8173120437 0.3706278643
4 1 0.8168998395 -0.8863575212 -0.3315775899
5 1 0.7796219246 1.4557850825 -0.6443284292
6 1 1.8050975188 -0.4816473637 0.6203798013
7 1 0.8118731786 2.1968335463 2.0491903374
8 1 0.4911882793 -0.3240865787 -1.6620502439
9 1 1.1285108336 -2.3803580614 -1.0602655522
10 1 1.4607294031 -1.4130987779 0.5674032534
11 1 -0.5403860739 1.9476926647 0.0535902704
12 1 0.2779536948 0.6911712729 0.8237953287
13 1 0.1495919806 -1.3425314817 0.5533030755
14 1 -1.8323773062 0.8881394327 1.5934884724
15 1 0.0546155753 -0.7846493732 -1.0493528190
16 1 0.9426008507 0.8262582872 -0.8115404902
17 1 0.6453830742 1.0431435516 -0.3043691127
18 1 1.8670991846 0.6720424690 -0.3079533808
19 1 -0.9639014790 -0.8550825111 1.8869469420
20 1 0.6873321006 -0.5050435175 2.1577198165
21 1 0.2239254075 -1.1562233302 0.4224185278
22 1 -0.5360479985 -0.3116060846 1.5561096426
23 1 -1.2301722469 -1.3240586922 1.2612422744
24 1 -0.5050898063 -0.0521535933 0.6288606251
25 1 1.5448636020 1.3214520172 0.3221515754
26 1 -1.1588210228 -1.8453682894 1.1573252871
27 1 1.6421919908 0.8836548332 0.5248758910
28 1 -1.0479137102 -1.0111225236 0.6689216525
29 1 -1.1402641448 -1.2937152900 -0.5946987691
30 1 0.5401695677 -1.5472919670 0.8496529299
31 1 -1.6193283224 0.5483979230 0.1952821538
32 1 -0.2509466239 1.6993466685 -0.3442987963
33 1 -0.4876385152 0.3031512373 -0.2419740196
34 1 -0.2806491388 0.6330173442 -1.2398183431
35 1 0.1999204838 1.3471927776 0.0360734878
36 1 0.4809501553 2.4835500893 0.4013649875
37 1 -0.9117394242 -0.0490446909 -0.4053874767
38 1 0.0764175190 1.4537473512 0.3741210834
39 1 0.5435221079 -0.5051860029 0.7867957894
40 1 0.7984337703 0.8508604376 -0.4435679681
41 1 -1.4361457062 -0.6292596473 0.2435217656
42 1 0.1052118160 -1.7417130945 0.6452469938
43 1 0.9919506777 -0.8592507612 -0.2815796883
44 1 0.3156128247 0.6602933781 -1.7222024071
45 1 0.8678217426 -2.2900441795 -0.1501902882
46 1 0.6722680417 -0.2093011363 0.0121825061
47 1 0.0784375327 -0.7792610704 0.1665596687
48 1 -0.4678883695 0.7583745568 -0.6417363578
49 1 -0.7000758167 -0.5674015851 -0.2613939259
50 1 0.7711037397 2.7474035422 -0.0839347571
51 1 -0.8086741277 1.0011198474 0.4560525046
52 1 0.6417691636 -0.4150210287 -0.4595756762
53 1 0.7108899984 1.2676017522 -0.1431510558
54 1 -0.1625889099 0.0417091744 0.4830398961
55 1 -0.6346498942 -0.7019630314 0.5768503789
56 1 1.1471271903 0.0147936486 -0.3117392439
57 1 -1.5138640797 0.1642809977 -0.8708652186
58 1 0.3573696852 0.1023930632 -0.6752668252
59 1 -0.4282856231 -0.7139247637 -0.1903840656
60 1 0.1837369095 -2.1503105297 -0.6229665257
61 1 0.5222821685 0.0097937599 -0.4405261962
62 1 0.0382097874 1.1948056324 0.3439583521
63 1 -0.9180582028 -0.0878073286 1.3202937198
64 1 -0.3157324793 -0.5750961042 -1.4063564356
65 1 0.3803156970 0.6051367728 1.0196741368
66 1 0.9331921670 0.4754354212 0.3902816000
67 1 1.4239835987 0.4847305112 0.3492364459
68 1 0.3670449214 -1.5191990495 1.5498033320
69 1 2.0767099333 -0.3076505656 0.9523708852
70 1 -0.1420633784 -1.1830257110 0.5453184095
71 1 0.2929403027 -0.0867079053 -1.4663594955
72 1 -0.3603208006 0.3505937650 0.0282576562
73 1 -0.3758234363 -1.8128337643 0.2886002111
74 1 0.6504302424 0.3102549854 1.6683571689
75 1 -0.1460263920 1.7014318317 0.4713578806
76 1 -0.7789022252 1.1633248478 -1.9645441952
77 1 -0.4772722914 -0.1025805088 0.3686961762
78 1 -0.1505570976 0.9042435562 2.2420361375
79 1 0.7982513947 0.4981721634 0.1195602234
80 1 0.2627041248 0.6407388780 0.3070898308
81 1 0.6279651131 0.0021439509 0.2843777227
82 1 0.8281942395 -0.0848186457 -0.4347199169
83 1 -0.9712292030 -0.1609133622 1.2046870348
84 1 -0.0199197380 0.3280164800 -0.9306286167
85 1 0.5142421022 0.2931683808 -0.5626548305
86 1 -0.4300125693 1.3319083517 -0.7632671561
87 1 -0.8225400839 -0.0476483565 0.1706780107
88 1 0.4906677770 2.2391260841 0.8922123090
89 1 0.3942498996 0.6325214944 0.1025911025
90 1 0.5008706583 -0.9094679509 -2.1972085120
91 1 0.7130781039 1.0282685391 -0.5706327254
92 1 1.2035448160 1.4961826970 -0.1565984322
93 1 -1.2861886466 -0.2725228551 0.1569148369
94 1 -0.8552506726 -1.0057287350 -0.9139523908
95 1 1.2996376499 -0.0293687832 -2.2366218376
96 1 -0.4975791431 -0.6010274043 -0.5121978141
97 1 -0.0240353973 -0.1273903376 -0.1318856204
98 1 0.2109307870 0.7523360895 0.1383795467
99 1 -1.1627837168 0.8546585194 -0.2420958786
100 1 0.2241698326 -0.7368249216 -0.8467107405
101 1 -1.4203238585 -2.4669386469 0.4847158355
102 1 -0.1629454633 0.2204557848 0.8762108117
103 1 -1.9984928459 -1.2347803996 0.8463037177
104 1 0.7674546604 0.8671015019 -1.2545811490
105 1 0.4130208684 0.5642562609 0.7260659521
106 1 0.2242920505 0.7864091404 -0.4233628372
107 1 -0.1835378759 0.8348719973 -0.5872005112
108 1 0.6454110524 -0.3123199556 1.1854482829
109 1 2.1274630349 -0.1877064794 0.0987578449
110 1 2.0930679948 -0.6922359279 -2.2871981109
111 1 -0.4712225866 -0.0332096774 0.3773670678
112 1 2.1185039306 -1.0874598695 1.2160333866
113 1 0.1128439417 0.0652135748 -0.6306272111
114 1 -0.6816232013 -0.0371627219 -0.2872743152
115 1 0.8366902458 -0.3298962638 0.5348022561
116 1 0.5761647328 1.2362238791 0.4755396380
117 1 0.6800620909 -1.3502015698 0.2451024445
118 1 0.2013818061 -1.5754628329 0.0235387774
119 1 -0.5269106809 -1.3128588906 0.8008613463
120 1 1.6818084980 -0.2263103959 -0.1979290319
121 1 -1.6366290973 0.2115625932 -0.4648317284
122 1 -1.3217017095 0.0766927813 -0.3849908794
123 1 -0.4582940078 0.2288621447 -1.3964076285
124 1 0.8028908422 -0.0605252613 0.0893286191
125 1 1.4491694254 0.2055104719 -0.0561199677
126 1 -0.0645002911 0.1324295959 0.0347150846
127 1 -1.0427231529 -0.2136226802 1.7779470769
128 1 -0.6131604214 0.4959067804 0.2456385863
129 1 1.6343509612 0.9754357644 1.0840779376
130 1 1.3355214732 -0.1555622761 0.9266084982
131 1 -0.8186466261 -1.2368228612 2.3172256458
132 1 -1.8112897399 -1.1451476862 1.2808728222
133 1 -1.3686015604 0.5439404116 -1.2380301684
134 1 -0.1533673976 -0.9456886203 0.3124049941
135 1 -0.3843675980 -1.4289357321 0.9634176892
136 1 -1.8070446596 1.3554384433 0.4456801661
137 1 -0.6761129137 1.1360496515 2.1693321275
138 1 1.3026361736 -2.7783255103 0.3413115606
139 1 1.6333026798 -1.8696215131 -0.2500888468
140 1 -2.1379097749 -0.5772610475 0.2364494516
141 1 2.3036270054 2.0208958969 -0.0578785187
142 1 -0.0352204283 0.9242543161 -0.3608283330
143 1 -0.0053473432 -0.5330032870 -0.5154151412
144 1 0.9979542440 -1.1631892607 1.6808938377
145 1 -0.2915456043 0.9726278213 -0.2397067293
146 1 -1.6259750852 -2.1494959449 2.1126279235
147 1 -0.0157344441 -0.1512529191 0.0882313538
148 1 0.3479534670 -1.8995713794 -0.0576824160
149 1 -0.9607436927 -0.8048246106 -0.4959467622
150 1 1.3954864903 3.0936966283 1.9008256312
151 1 0.4617653862 0.0972093387 0.6760255642
152 1 -0.6965953854 -1.2771354355 -0.0924237284
153 1 -1.0308001566 -1.3195143547 -0.0842057018
154 1 0.8633075423 0.9693708778 0.6141290402
155 1 1.8748716168 -0.2375574567 -0.4527795383
156 1 0.8342015841 0.9258648155 0.9431551521
157 1 0.2198254203 -1.0052373893 0.3731011636
158 1 1.0048154415 -0.3738294199 0.9361085041
159 1 0.6292445983 1.0372345783 0.8706170692
160 1 0.2394069412 -1.2561203802 0.2235698835
161 1 -0.8174921146 -0.2492658717 0.4629986479
162 1 1.0764874344 1.1492957868 -1.6221232769
163 1 -0.7405849691 0.2524539178 -0.3369977389
164 1 0.6830537666 0.0783915682 0.5333680907
165 1 0.9401879679 -0.6636770743 -0.8424122326
166 1 0.6504298701 -0.6983808622 -0.4375006119
167 1 0.8969177714 -0.6397434859 -0.0761318178
168 1 -1.9884849273 0.6315952724 -1.9441453638
169 1 0.8878925317 -1.1031640614 -0.3026337057
170 1 0.2574523837 -2.1737384365 0.0261676793
171 1 0.2638040223 0.8894580105 -0.5316916219
172 1 0.8050696694 -0.8247414704 0.6140076455
173 1 -1.0362756757 -2.0581829719 -1.1837015813
174 1 1.7072109072 -0.4014458473 -0.0780482412
175 1 0.2056696111 0.4201750615 1.0637149265
176 1 -1.3884242378 0.3100317494 0.1900542145
177 1 0.9000962909 0.1198709617 0.9415274068
178 1 0.7723769019 0.7435963078 -0.5896524839
179 1 0.1398461034 0.2403041739 0.1199819713
180 1 -0.9971703380 -1.1311023094 0.0152093642
181 1 -0.7848609782 -2.5600524404 0.0728007835
182 1 -0.4878667252 0.9087877914 -1.8524641483
183 1 0.0235512251 1.1834596627 -0.5528332480
184 1 0.0119610482 -0.2068075633 0.7388490471
185 1 -1.8156107912 -0.2577453680 -0.0607272387
186 1 0.3807205787 0.2135097407 -0.9685065319
187 1 -1.7006279206 -1.4676776582 -0.2163486506
188 1 -0.2773096650 -1.7433478148 -1.0185528402
189 1 -0.7414661940 0.1912165321 -1.1669836244
190 1 -0.6886211852 0.4078008873 -1.1214051140
191 1 -1.9837605298 0.9780324681 -0.3259468062
192 1 -0.1935253534 -0.2710168892 -0.2649423855
193 1 0.3250866403 -0.7821450904 0.5210319607
194 1 -0.1318275149 -1.5780766567 -0.7697308985
195 1 0.9021508715 -0.3494070165 0.5899299967
196 1 1.0865618162 0.8349570023 -0.2968655085
197 1 -0.5352410982 -0.8843313187 1.8610330135
198 1 -0.5770577745 -0.3190149090 -0.3140251328
199 1 0.1092041256 -0.2344074001 0.1619446468
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1880 1 -3.713471e-01 -1.4813535976
1881 1 -1.191724e+00 -2.4675259417
1882 1 5.749024e-01 -0.5813851051
1883 1 -8.643900e-01 -0.2807471520
1884 1 3.922759e-01 -0.3601650438
1885 1 1.321946e+00 -0.2270587191
1886 1 1.275346e+00 0.0060096421
1887 1 -1.461178e+00 1.4219869387
1888 1 4.263679e-01 -0.6560333749
1889 1 1.069635e-01 0.7154114440
1890 1 3.954492e-01 -1.2810537354
1891 1 -5.094093e-01 0.2116882009
1892 1 6.522543e-01 0.4138620126
1893 1 6.110963e-01 -0.7819688049
1894 1 8.737319e-01 2.1829251895
1895 1 -1.470085e+00 0.5001709711
1896 1 -1.193908e-01 0.7720357885
1897 1 1.441880e+00 2.6635069516
1898 1 9.807212e-01 -0.1918281006
1899 1 -1.549435e-01 -0.0477416512
1900 1 -6.565341e-01 -0.7303512121
1901 1 -2.218050e+00 -0.1248124005
1902 1 1.565099e+00 -1.2543759396
1903 1 -6.645336e-01 -0.0018166585
1904 1 -1.106672e+00 0.0184418854
1905 1 8.962007e-01 -1.5790753894
1906 1 -4.423003e-01 1.7012976526
1907 1 -5.403642e-03 -0.4247147067
1908 1 -1.067551e+00 -0.5569274770
1909 1 -6.025665e-01 -0.3705957323
1910 1 1.091899e+00 -0.2698253087
1911 1 6.321106e-01 -1.0712310012
1912 1 -6.891076e-02 1.5093042332
1913 1 -5.260184e-01 -0.2522409869
1914 1 -9.503961e-01 -1.1194321510
1915 1 1.429524e-01 0.0355176979
1916 1 -1.126099e+00 -0.7397448082
1917 1 2.747486e+00 -0.2091672198
1918 1 -4.562190e-01 -0.2211406219
1919 1 -1.615861e+00 -0.1960416425
1920 1 4.395728e-01 0.0851074619
1921 1 2.996376e-01 -0.1635599861
1922 1 1.142633e+00 1.2442966123
1923 1 -1.601222e+00 -0.8298963397
1924 1 -1.514884e+00 -0.9578456729
1925 1 -8.502370e-01 -0.3434408738
1926 1 -4.536467e-01 -0.9954231651
1927 1 7.029974e-02 0.3539936878
1928 1 -1.713754e-01 -1.8076249912
1929 1 1.063081e+00 -0.5676861874
1930 1 -2.771234e-01 0.0987148898
1931 1 -2.123883e-01 -0.7708787733
1932 1 1.524831e+00 -0.3909979096
1933 1 1.754834e-01 -0.3555536583
1934 1 -1.893939e+00 -0.1499947922
1935 1 -7.669144e-01 1.3458038956
1936 1 -2.528206e-01 1.3359909556
1937 1 8.316751e-01 0.3266016616
1938 1 1.010643e+00 -1.0517406052
1939 1 1.796983e+00 -0.5284336240
1940 1 7.229651e-01 -1.1143800298
1941 1 4.178250e-01 -0.0049648993
1942 1 1.196199e-01 0.4954238593
1943 1 1.825934e+00 1.9632261086
1944 1 -1.567389e+00 0.0977832811
1945 1 2.665720e-01 -1.3769637483
1946 1 -2.851051e-02 -0.1787249146
1947 1 7.168300e-01 2.4089706463
1948 1 9.548940e-02 1.1836679379
1949 1 -1.699918e-01 -0.1629387394
1950 1 7.732266e-01 0.4788108005
1951 1 1.055910e+00 1.4768883954
1952 1 1.301988e+00 1.2483121552
1953 1 -5.629535e-01 1.8736015682
1954 1 -1.046239e+00 -1.4697312412
1955 1 7.296283e-01 0.3759873430
1956 1 -1.134532e+00 -0.7568702574
1957 1 -2.730316e+00 0.7899478570
1958 1 6.522059e-01 -0.7758246883
1959 1 -9.077401e-01 1.7400984741
1960 1 -1.746243e-01 0.7273764157
1961 1 -1.749175e+00 0.0412251647
1962 1 6.351639e-01 -0.7465530475
1963 1 3.928764e-01 -0.4669105152
1964 1 1.469288e-01 0.5619024171
1965 1 -4.416916e-02 0.9213398038
1966 1 5.095332e-01 1.3139610919
1967 1 1.036623e+00 -0.6321055477
1968 1 -1.017870e+00 1.1922587748
1969 1 1.540566e+00 -0.2953318428
1970 1 1.905289e+00 -0.7622792482
1971 1 4.042105e-01 0.5947329044
1972 1 -7.069767e-01 1.5472486383
1973 1 -1.008285e+00 0.6816846166
1974 1 4.863330e-01 -0.0116558350
1975 1 -7.022979e-01 0.7742367691
1976 1 3.093437e-01 0.2707596966
1977 1 -1.078647e+00 -0.1883596530
1978 1 5.902743e-01 0.6246672753
1979 1 6.584944e-01 1.4193192881
1980 1 2.048185e+00 -0.9067758246
1981 1 -1.448530e+00 -0.3987914948
1982 1 1.267581e+00 1.4135953220
1983 1 -6.959804e-01 0.2584286770
1984 1 7.405956e-02 -1.2725183814
1985 1 4.446582e-02 -0.0753325430
1986 1 -4.317039e-01 0.0703880778
1987 1 1.688329e+00 -0.7760323143
1988 1 1.932779e-01 -1.6246009465
1989 1 2.666542e-01 0.8940843318
1990 1 -7.428853e-01 1.4278914745
1991 1 2.081491e-01 1.8593265851
1992 1 -8.529617e-01 -0.3979564585
1993 1 9.235143e-01 0.4129440992
1994 1 1.354395e+00 -0.2755167794
1995 1 5.697907e-01 0.8813467819
1996 1 -2.468826e-02 1.6829380542
1997 1 6.948899e-01 0.6945449748
1998 1 -7.752261e-02 0.5096265680
1999 1 4.085734e-01 0.4243439554
2000 1 -1.587949e+00 0.0181455085
attr(,"assign")
[1] 0 1 2
$calc_lik
function (beta)
{
for (n in names(obj$model$submods)) assign(n, eval(get(n)))
p <- eval(obj$model$boolmod)
log.p <- log(p)
log.p1 <- log(1 - p)
eval(llik)
}
<bytecode: 0x55f20e31a5d0>
<environment: 0x55f21149e268>
$method
[1] "BFGS"
$model.fit
$BFGS
$BFGS$par
X.Intercept. x1 x2 x3 X.Intercept..1 x4
BFGS -1.55604 0.1957866 0.6223986 0.7455059 -0.04884058 1.311676
x5
BFGS -0.2085441
$BFGS$nhatend
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 152.724870 30.350731 78.5240869 95.7768127 127.11743 -10.971630
[2,] 30.350731 156.269234 -1.5652812 14.0418127 10.66959 -4.423217
[3,] 78.524087 -1.565281 172.8665894 21.9240707 48.76609 3.315847
[4,] 95.776813 14.041813 21.9240707 158.7687197 61.88226 -3.110111
[5,] 127.117432 10.669594 48.7660940 61.8822613 633.64449 49.401769
[6,] -10.971630 -4.423217 3.3158474 -3.1101106 49.40177 268.091052
[7,] 4.493651 -6.401603 -0.9885231 0.5891863 3.17210 51.520332
[,7]
[1,] 4.4936515
[2,] -6.4016028
[3,] -0.9885231
[4,] 0.5891863
[5,] 3.1720999
[6,] 51.5203315
[7,] 611.6443925
$BFGS$value
[1] -793.5796
$BFGS$iterations
[1] NA
$BFGS$conv
[1] 1
$BFGS$detail
X.Intercept. x1 x2 x3 X.Intercept..1 x4
BFGS -1.55604 0.1957866 0.6223986 0.7455059 -0.04884058 1.311676
x5 value fevals gevals niter convcode kkt1 kkt2 xtimes
BFGS -0.2085441 793.5796 19 5 NA 1 FALSE TRUE 0.121
attr(,"class")
[1] "boolfit" "list"
attr(,"class")
[1] "boolean"
>
> ## Not run:
> ##D ## Now estimate model with genoud, a genetic optimizer. This has the
> ##D ## capability of using multiple processors via parallel.
> ##D (fit6.OR <- boolean(mod.OR, method="genoud",
> ##D cluster=c("localhost", "localhost"),
> ##D print.level=2))
> ##D
> ##D ## The default SANN optimizer is also available.
> ##D (fit7.OR <- boolean(mod.OR, method="SANN"))
> ## End(Not run)
>
> ## The fit is stored as "model.fit", within the boolean object.
> str(fit.OR$model.fit)
List of 1
$ nlminb:List of 6
..$ par : num [1, 1:7] -1.96217 0.25416 0.69548 0.99724 0.00689 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr "nlminb"
.. .. ..$ : chr [1:7] "X.Intercept." "x1" "x2" "x3" ...
..$ nhatend : num [1:7, 1:7] 115.5 31.3 63.7 100.2 95.3 ...
..$ value : num -772
..$ iterations: num 42
..$ conv : num 0
..$ detail :Classes ‘optimx’ and 'data.frame': 1 obs. of 15 variables:
.. ..$ X.Intercept. : num -1.96
.. ..$ x1 : num 0.254
.. ..$ x2 : num 0.695
.. ..$ x3 : num 0.997
.. ..$ X.Intercept..1: num 0.00689
.. ..$ x4 : num 1.7
.. ..$ x5 : num -0.197
.. ..$ value : num 772
.. ..$ fevals : num 54
.. ..$ gevals : num 408
.. ..$ niter : num 42
.. ..$ convcode : num 0
.. ..$ kkt1 : logi TRUE
.. ..$ kkt2 : logi TRUE
.. ..$ xtimes : num 0.409
.. ..- attr(*, "details")=List of 5
.. .. ..$ : chr "nlminb"
.. .. ..$ : num [1:7] 1.23e-05 1.32e-04 -4.30e-05 -1.06e-04 -4.30e-04 ...
.. .. ..$ : num [1:7, 1:7] 115.5 31.3 63.7 100.2 95.3 ...
.. .. ..$ : num [1:7] 617 562 246 158 121 ...
.. .. ..$ : chr "none"
.. .. ..- attr(*, "dim")= int [1:2] 1 5
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr "nlminb"
.. .. .. ..$ : chr [1:5] "method" "ngatend" "nhatend" "hev" ...
.. ..- attr(*, "maximize")= logi FALSE
.. ..- attr(*, "npar")= int 7
.. ..- attr(*, "follow.on")= logi FALSE
- attr(*, "class")= chr [1:2] "boolfit" "list"
>
> ## Create a summary object, saving and printing it. Then take a look at
> ## the objects stored in the summary object.
> (smry <- summary(fit.OR))
Length Class Mode
call 6 -none- call
link 2 -none- list
model 2 -none- list
N 1 -none- numeric
k 1 -none- numeric
coef.labels 7 -none- character
coef.idx 2 -none- list
response 2000 -none- numeric
frame 2 -none- list
calc_lik 1 -none- function
method 1 -none- character
model.fit 1 boolfit list
> str(smry)
'summaryDefault' chr [1:12, 1:3] " 6" " 2" " 2" " 1" " 1" " 7" ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:12] "call" "link" "model" "N" ...
..$ : chr [1:3] "Length" "Class" "Mode"
>
> ## Extract log-likelihood and coefficient vector.
> logLik(fit.OR)
Error in UseMethod("logLik") :
no applicable method for 'logLik' applied to an object of class "boolean"
Calls: logLik
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 3.1.6
Check: examples
Result: ERROR
Running examples in ‘boolean3-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: boolean3-package
> ### Title: Modeling Causal Complexity
> ### Aliases: boolean3 boolean3-package
> ### Keywords: package
>
> ### ** Examples
>
> ## Generate some fake data
> require(mvtnorm)
Loading required package: mvtnorm
> set.seed(12345)
> N <- 2000
> Df <- cbind(1, rmvnorm(N, mean=rep(0, 5)))
>
> ## Set coefficients
> beta.a <- c(-2.00, 0.33, 0.66, 1.00)
> beta.b <- c(0.00, 1.50, -0.25)
>
> ## Generate path probabilities following a normal model.
> y.a <- as.vector(pnorm(tcrossprod(beta.a, Df[, 1:4])))
> y.b <- as.vector(pnorm(tcrossprod(beta.b, Df[, c(1, 5, 6)])))
>
> ## AND and OR-model
> or <- function(x, y) { x + y - x * y }
> and <- function(x, y) { x * y }
> y.star.OR <- or(y.a, y.b)
> y.star.AND <- and(y.a, y.b)
>
> ## Observed responses
> y.OR <- rbinom(N, 1, y.star.OR)
> y.AND <- rbinom(N, 1, y.star.AND)
>
> ## Set up data.frame for estimation
> Df <- cbind(1, Df)
> Df <- as.data.frame(Df)
> Df[,1] <- y.OR
> Df[,2] <- y.AND
> names(Df) <- c("y.OR", "y.AND", "x1", "x2", "x3", "x4", "x5")
>
> ## Before estimating, boolean models need to be specified using the
> ## boolprep function.
>
> ## OR model, specified to use a probit link for each causal path. This
> ## matches the data generating process above.
> mod.OR <- boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5,
+ data = Df, family=list(binomial("probit")))
>
> ## IF you really want to, it's also possible to specify a different
> ## link function for each causal path. These should be in the same
> ## order as specified in the model formula.
> mod.OR2 <- boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5,
+ data = Df, family=list(binomial("probit"),
+ binomial("logit")))
>
> ## Fit the prepared model using the nlminb optimizer (the default).
> (fit.OR <- boolean(mod.OR, method="nlminb", control=list(trace=1)))
fn is fn
Looking for method = nlminb
Function has 7 arguments
Analytic gradient not made available.
Analytic Hessian not made available.
Scale check -- log parameter ratio= 2.069957 log bounds ratio= NA
Method: nlminb
0: 2035.0322: 0.555932 0.00473222 0.0518577 0.0496661 0.555932 0.314324 -0.0401190
1: 1125.8726: -0.128002 0.0388608 0.148522 0.127276 -0.0324267 0.721923 -0.0984183
2: 842.16854: -0.894107 0.0450576 0.220895 0.294603 0.0566570 1.33118 -0.124427
3: 801.45277: -1.16971 0.0904748 0.304796 0.364513 -0.181651 1.54390 -0.197683
4: 793.97921: -1.20286 0.147914 0.450047 0.558015 0.0953275 1.78334 -0.178614
5: 780.71673: -1.44471 0.189016 0.538420 0.765386 -0.198236 1.79638 -0.181454
6: 775.40425: -1.51478 0.180762 0.546683 0.747108 -0.0165510 1.79489 -0.229489
7: 774.57448: -1.57276 0.182887 0.545728 0.743597 -0.0714210 1.78130 -0.190647
8: 773.65776: -1.60590 0.198898 0.574778 0.783028 -0.0163173 1.74617 -0.198394
9: 773.45521: -1.67407 0.202747 0.586465 0.808583 -0.0539769 1.72187 -0.223385
10: 773.01515: -1.71678 0.200876 0.598546 0.838030 -0.0208753 1.72345 -0.159004
11: 772.50230: -1.72318 0.206564 0.606078 0.846533 -0.0158345 1.72432 -0.200933
12: 772.18615: -1.78954 0.231404 0.641645 0.890729 -0.0149774 1.74469 -0.180530
13: 771.89407: -1.85032 0.220442 0.644318 0.940353 -0.00765249 1.69664 -0.201119
14: 771.82963: -1.85752 0.227057 0.652232 0.940176 0.00321924 1.70914 -0.192294
15: 771.80804: -1.86904 0.234336 0.658832 0.936483 -0.00922186 1.71128 -0.202710
16: 771.77147: -1.87385 0.245677 0.671660 0.936852 0.000202047 1.70546 -0.194253
17: 771.73610: -1.88837 0.239128 0.666675 0.949032 -0.000940174 1.71251 -0.199984
18: 771.73251: -1.89568 0.239325 0.673717 0.966182 0.00207717 1.70995 -0.190095
19: 771.69796: -1.91188 0.246202 0.678638 0.968550 0.000991727 1.69952 -0.197926
20: 771.68769: -1.91635 0.246152 0.679388 0.968983 0.00465926 1.70560 -0.195221
21: 771.68585: -1.92140 0.245884 0.681944 0.970632 -0.000298807 1.70718 -0.199282
22: 771.67581: -1.92565 0.249540 0.682440 0.975775 0.00323946 1.70555 -0.197016
23: 771.67138: -1.93310 0.248427 0.685083 0.977666 0.00545687 1.70803 -0.197440
24: 771.66787: -1.93884 0.251036 0.686583 0.982438 0.00259472 1.70576 -0.196931
25: 771.66515: -1.94282 0.249059 0.691551 0.986158 0.00554980 1.70248 -0.197777
26: 771.66252: -1.94927 0.253205 0.688497 0.988277 0.00637468 1.70450 -0.196717
27: 771.66179: -1.94943 0.252731 0.690801 0.989858 0.00511422 1.70388 -0.197930
28: 771.66159: -1.95076 0.251950 0.692556 0.990551 0.00589815 1.70452 -0.195798
29: 771.66071: -1.95311 0.252596 0.692511 0.991245 0.00686234 1.70299 -0.197147
30: 771.66048: -1.95352 0.252645 0.692540 0.991439 0.00603419 1.70315 -0.197013
31: 771.66035: -1.95391 0.252809 0.692812 0.992092 0.00645048 1.70345 -0.197051
32: 771.66010: -1.95539 0.253106 0.692932 0.993201 0.00597275 1.70362 -0.197059
33: 771.65989: -1.95682 0.253095 0.693679 0.993688 0.00656203 1.70289 -0.196863
34: 771.65976: -1.95822 0.253576 0.694616 0.994380 0.00638257 1.70290 -0.197323
35: 771.65960: -1.95958 0.253686 0.694374 0.995708 0.00653884 1.70291 -0.197074
36: 771.65960: -1.96080 0.254736 0.695269 0.996154 0.00683515 1.70302 -0.196825
37: 771.65953: -1.96146 0.254391 0.695463 0.996637 0.00680183 1.70275 -0.197052
38: 771.65952: -1.96215 0.254142 0.695673 0.997170 0.00692894 1.70255 -0.196925
39: 771.65951: -1.96220 0.254146 0.695537 0.997255 0.00692672 1.70258 -0.197040
40: 771.65951: -1.96221 0.254149 0.695523 0.997250 0.00689011 1.70258 -0.197017
41: 771.65951: -1.96219 0.254155 0.695486 0.997245 0.00689580 1.70257 -0.197015
42: 771.65951: -1.96217 0.254157 0.695483 0.997240 0.00689316 1.70257 -0.197019
Post processing for method nlminb
Successful convergence!
Compute Hessian approximation at finish of nlminb
Compute gradient approximation at finish of nlminb
Save results from method nlminb
$par
(Intercept) x1 x2 x3 (Intercept) x4
-1.962174116 0.254157295 0.695482511 0.997240458 0.006893159 1.702572353
x5
-0.197019270
$message
[1] "relative convergence (4)"
$convcode
[1] 0
$value
[1] 771.6595
$fevals
function
54
$gevals
gradient
408
$nitns
[1] 42
$kkt1
[1] TRUE
$kkt2
[1] TRUE
$xtimes
user.self
0.563
Assemble the answers
$call
boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5, data = Df,
family = list(binomial("probit")))
$link
$link$a
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$link$b
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$model
$model$submods
$model$submods$a
[1] "x1" "x2" "x3"
$model$submods$b
[1] "x4" "x5"
$model$boolmod
expression((a %|% b))
$N
[1] 2000
$k
[1] 7
$coef.labels
[1] "(Intercept)_a" "x1_a" "x2_a" "x3_a"
[5] "(Intercept)_b" "x4_b" "x5_b"
$coef.idx
$coef.idx$a
[1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE
$coef.idx$b
[1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE
$response
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
1 1 1 0 0 0 1 1 1 0 0 1 0 1 0 1
33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
0 1 1 1 1 0 0 1 1 1 1 0 0 1 0 1
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1
65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
1 1 1 1 1 0 0 1 1 1 1 1 0 1 0 0
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0
97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
1 1 0 1 0 0 0 0 0 0 1 1 1 0 1 1
113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
1 0 0 0 0 1 0 1 0 1 1 1 0 0 1 1
129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1
145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
0 1 1 0 0 1 0 1 1 1 0 1 1 1 0 1
161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1
177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
1 1 0 1 1 0 1 1 1 1 1 0 0 0 0 0
193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
1 0 1 1 1 0 0 0 0 1 0 1 1 1 0 1
209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
0 1 0 0 1 0 1 1 1 1 1 0 1 0 1 1
225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0
241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
1 1 1 1 0 0 1 0 1 0 0 0 0 0 1 0
257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
1 0 1 1 0 1 0 1 1 0 1 0 1 0 1 0
273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1
289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304
1 1 0 0 1 1 1 1 0 1 0 0 1 1 1 0
305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320
1 1 1 0 0 1 0 0 0 0 0 0 1 1 0 1
321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
0 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1
337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352
0 1 0 1 1 0 1 1 1 1 0 1 1 1 1 0
353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
1 1 1 1 1 1 1 0 1 0 1 1 0 0 0 0
369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384
1 0 0 0 1 1 0 1 0 0 1 1 1 0 1 0
385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1
401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416
1 0 0 1 1 1 1 1 0 0 1 1 1 0 1 1
417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1
433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448
1 0 0 1 1 1 1 0 1 1 0 0 1 0 1 1
449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464
1 1 0 1 1 0 1 1 0 0 1 1 1 0 1 0
465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 1
481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
1 0 1 1 1 1 0 0 1 0 1 0 0 0 0 1
497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
0 0 1 1 0 0 0 0 1 1 0 0 1 1 1 1
513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
1 0 1 1 0 1 0 0 1 1 0 0 0 0 1 0
529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
0 1 0 1 0 0 0 1 1 0 1 1 1 1 0 1
545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560
1 1 1 0 1 1 0 1 1 1 1 1 0 0 0 1
561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
1 1 1 1 0 1 1 0 1 0 1 0 1 0 0 0
577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592
1 1 1 0 1 0 1 0 1 1 0 1 1 1 0 0
593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608
1 1 1 0 0 0 1 1 0 0 1 1 0 1 0 0
609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
1 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0
625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640
0 1 0 1 1 0 1 1 0 1 0 0 0 1 0 1
641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1
657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672
1 1 1 0 1 1 0 0 0 1 0 0 0 0 1 1
673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
0 0 1 0 1 1 1 0 1 1 0 1 1 0 0 0
689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704
0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720
0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1
721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
1 1 0 1 1 1 1 1 0 0 0 1 1 0 1 1
737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752
0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 1
753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
1 0 1 1 0 1 1 1 0 1 1 1 0 0 0 0
769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784
0 0 0 1 1 1 0 0 0 1 1 1 1 0 0 0
785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
1 0 1 1 0 1 1 1 0 1 1 1 0 0 1 0
801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
0 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1
817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 0
833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848
1 1 1 0 0 0 1 0 1 0 1 0 0 0 1 0
849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864
1 1 1 1 0 1 0 0 1 1 1 1 0 0 0 0
865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880
0 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1
881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896
0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1
897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912
0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 1
913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
0 0 0 1 1 0 1 1 0 0 0 1 0 0 1 1
929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944
0 1 1 1 0 1 0 1 0 1 1 0 1 1 1 1
945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0
961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 0
977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
0 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1
1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
1 0 0 1 1 1 1 0 1 1 0 1 1 1 1 0
1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 1
1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
0 0 0 1 1 0 1 1 1 0 0 1 1 0 0 1
1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
0 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0
1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088
0 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1
1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
1 0 0 0 1 1 1 1 0 1 0 1 1 1 1 0
1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
0 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0
1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
1 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0
1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 0
1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168
0 0 1 0 1 1 1 1 1 0 0 1 1 1 0 0
1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
0 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1
1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0
1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216
1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1
1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 1
1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
0 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0
1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
0 0 1 0 1 0 1 1 0 0 1 0 1 1 0 1
1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
0 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1
1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
0 1 1 0 1 1 0 1 1 0 0 1 1 0 1 1
1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
0 1 1 1 0 0 0 0 0 1 1 0 1 0 1 0
1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0
1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
0 1 0 0 0 0 1 1 0 1 1 0 1 0 0 0
1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 1
1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
1 0 1 1 0 0 1 0 1 1 0 0 0 0 0 1
1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
1 0 0 0 1 1 0 0 1 0 1 1 1 1 0 0
1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
1 0 0 1 1 0 1 0 0 1 0 1 0 0 1 0
1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
1 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0
1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
1 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0
1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456
1 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1
1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472
1 1 1 1 1 0 0 1 0 1 0 1 1 1 0 0
1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488
0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1
1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1
1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520
1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1
1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
1 0 0 1 1 0 0 1 0 0 1 1 1 1 1 0
1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552
1 0 1 1 1 1 1 0 1 0 0 1 1 1 0 0
1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568
0 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0
1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584
0 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1
1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600
1 1 1 1 0 1 0 0 0 0 1 0 1 1 0 1
1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
0 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1
1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632
0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 1
1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648
1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1
1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664
1 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0
1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680
1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0
1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696
1 0 1 1 0 0 1 1 0 0 1 1 1 1 1 1
1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712
1 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0
1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1
1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744
0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0
1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760
0 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1
1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776
0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 1
1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792
1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1
1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808
1 1 1 0 1 0 0 0 1 1 1 0 0 1 0 0
1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824
0 1 1 0 0 1 0 0 1 1 1 1 0 1 1 1
1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840
1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0
1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856
0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 0
1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872
0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 0
1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888
0 1 0 0 1 0 0 0 1 1 0 1 1 1 0 1
1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904
0 0 0 1 1 1 1 1 1 1 0 0 0 1 0 0
1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 0
1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936
1 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0
1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952
1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1
1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968
1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 0
1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
1 1 1 0 1 1 0 1 0 0 0 1 0 1 1 0
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1
$frame
$frame$a
(Intercept) x1 x2 x3
1 1 0.5855288178 0.7094660175 -0.1093033147
2 1 -1.8179559677 0.6300985511 -0.2761841052
3 1 -0.1162478064 1.8173120437 0.3706278643
4 1 0.8168998395 -0.8863575212 -0.3315775899
5 1 0.7796219246 1.4557850825 -0.6443284292
6 1 1.8050975188 -0.4816473637 0.6203798013
7 1 0.8118731786 2.1968335463 2.0491903374
8 1 0.4911882793 -0.3240865787 -1.6620502439
9 1 1.1285108336 -2.3803580614 -1.0602655522
10 1 1.4607294031 -1.4130987779 0.5674032534
11 1 -0.5403860739 1.9476926647 0.0535902704
12 1 0.2779536948 0.6911712729 0.8237953287
13 1 0.1495919806 -1.3425314817 0.5533030755
14 1 -1.8323773062 0.8881394327 1.5934884724
15 1 0.0546155753 -0.7846493732 -1.0493528190
16 1 0.9426008507 0.8262582872 -0.8115404902
17 1 0.6453830742 1.0431435516 -0.3043691127
18 1 1.8670991846 0.6720424690 -0.3079533808
19 1 -0.9639014790 -0.8550825111 1.8869469420
20 1 0.6873321006 -0.5050435175 2.1577198165
21 1 0.2239254075 -1.1562233302 0.4224185278
22 1 -0.5360479985 -0.3116060846 1.5561096426
23 1 -1.2301722469 -1.3240586922 1.2612422744
24 1 -0.5050898063 -0.0521535933 0.6288606251
25 1 1.5448636020 1.3214520172 0.3221515754
26 1 -1.1588210228 -1.8453682894 1.1573252871
27 1 1.6421919908 0.8836548332 0.5248758910
28 1 -1.0479137102 -1.0111225236 0.6689216525
29 1 -1.1402641448 -1.2937152900 -0.5946987691
30 1 0.5401695677 -1.5472919670 0.8496529299
31 1 -1.6193283224 0.5483979230 0.1952821538
32 1 -0.2509466239 1.6993466685 -0.3442987963
33 1 -0.4876385152 0.3031512373 -0.2419740196
34 1 -0.2806491388 0.6330173442 -1.2398183431
35 1 0.1999204838 1.3471927776 0.0360734878
36 1 0.4809501553 2.4835500893 0.4013649875
37 1 -0.9117394242 -0.0490446909 -0.4053874767
38 1 0.0764175190 1.4537473512 0.3741210834
39 1 0.5435221079 -0.5051860029 0.7867957894
40 1 0.7984337703 0.8508604376 -0.4435679681
41 1 -1.4361457062 -0.6292596473 0.2435217656
42 1 0.1052118160 -1.7417130945 0.6452469938
43 1 0.9919506777 -0.8592507612 -0.2815796883
44 1 0.3156128247 0.6602933781 -1.7222024071
45 1 0.8678217426 -2.2900441795 -0.1501902882
46 1 0.6722680417 -0.2093011363 0.0121825061
47 1 0.0784375327 -0.7792610704 0.1665596687
48 1 -0.4678883695 0.7583745568 -0.6417363578
49 1 -0.7000758167 -0.5674015851 -0.2613939259
50 1 0.7711037397 2.7474035422 -0.0839347571
51 1 -0.8086741277 1.0011198474 0.4560525046
52 1 0.6417691636 -0.4150210287 -0.4595756762
53 1 0.7108899984 1.2676017522 -0.1431510558
54 1 -0.1625889099 0.0417091744 0.4830398961
55 1 -0.6346498942 -0.7019630314 0.5768503789
56 1 1.1471271903 0.0147936486 -0.3117392439
57 1 -1.5138640797 0.1642809977 -0.8708652186
58 1 0.3573696852 0.1023930632 -0.6752668252
59 1 -0.4282856231 -0.7139247637 -0.1903840656
60 1 0.1837369095 -2.1503105297 -0.6229665257
61 1 0.5222821685 0.0097937599 -0.4405261962
62 1 0.0382097874 1.1948056324 0.3439583521
63 1 -0.9180582028 -0.0878073286 1.3202937198
64 1 -0.3157324793 -0.5750961042 -1.4063564356
65 1 0.3803156970 0.6051367728 1.0196741368
66 1 0.9331921670 0.4754354212 0.3902816000
67 1 1.4239835987 0.4847305112 0.3492364459
68 1 0.3670449214 -1.5191990495 1.5498033320
69 1 2.0767099333 -0.3076505656 0.9523708852
70 1 -0.1420633784 -1.1830257110 0.5453184095
71 1 0.2929403027 -0.0867079053 -1.4663594955
72 1 -0.3603208006 0.3505937650 0.0282576562
73 1 -0.3758234363 -1.8128337643 0.2886002111
74 1 0.6504302424 0.3102549854 1.6683571689
75 1 -0.1460263920 1.7014318317 0.4713578806
76 1 -0.7789022252 1.1633248478 -1.9645441952
77 1 -0.4772722914 -0.1025805088 0.3686961762
78 1 -0.1505570976 0.9042435562 2.2420361375
79 1 0.7982513947 0.4981721634 0.1195602234
80 1 0.2627041248 0.6407388780 0.3070898308
81 1 0.6279651131 0.0021439509 0.2843777227
82 1 0.8281942395 -0.0848186457 -0.4347199169
83 1 -0.9712292030 -0.1609133622 1.2046870348
84 1 -0.0199197380 0.3280164800 -0.9306286167
85 1 0.5142421022 0.2931683808 -0.5626548305
86 1 -0.4300125693 1.3319083517 -0.7632671561
87 1 -0.8225400839 -0.0476483565 0.1706780107
88 1 0.4906677770 2.2391260841 0.8922123090
89 1 0.3942498996 0.6325214944 0.1025911025
90 1 0.5008706583 -0.9094679509 -2.1972085120
91 1 0.7130781039 1.0282685391 -0.5706327254
92 1 1.2035448160 1.4961826970 -0.1565984322
93 1 -1.2861886466 -0.2725228551 0.1569148369
94 1 -0.8552506726 -1.0057287350 -0.9139523908
95 1 1.2996376499 -0.0293687832 -2.2366218376
96 1 -0.4975791431 -0.6010274043 -0.5121978141
97 1 -0.0240353973 -0.1273903376 -0.1318856204
98 1 0.2109307870 0.7523360895 0.1383795467
99 1 -1.1627837168 0.8546585194 -0.2420958786
100 1 0.2241698326 -0.7368249216 -0.8467107405
101 1 -1.4203238585 -2.4669386469 0.4847158355
102 1 -0.1629454633 0.2204557848 0.8762108117
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1762 1 -4.492637e-01 0.2893496357
1763 1 3.012417e-02 0.3644379394
1764 1 7.811401e-01 -0.8003054104
1765 1 -6.655344e-01 1.2522781812
1766 1 -4.364759e-01 -2.1767380125
1767 1 -4.497005e-01 -0.6242738875
1768 1 3.094930e-01 0.0482619094
1769 1 7.822935e-01 0.7664588957
1770 1 -1.685372e+00 -1.9434335372
1771 1 -3.847909e-01 -0.1241172515
1772 1 -4.402048e-01 -2.2989330729
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1774 1 8.549399e-02 -1.0137472547
1775 1 -5.085265e-01 0.1399205875
1776 1 -4.007824e-01 -0.0338663015
1777 1 -6.262117e-01 -0.6866873990
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1779 1 3.517230e-01 -0.7363912757
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1781 1 -4.341776e-01 0.0425279426
1782 1 9.859413e-01 1.1892380171
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1785 1 9.345811e-02 -0.9926720082
1786 1 -1.183550e+00 0.1897786782
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1788 1 9.605166e-01 0.7140920204
1789 1 1.586373e+00 -1.6345681822
1790 1 -7.917502e-01 -0.6206343527
1791 1 -6.595907e-01 1.7160863283
1792 1 -7.074627e-01 -1.0769070077
1793 1 2.833266e-01 0.8239320697
1794 1 5.278889e-01 0.7641110487
1795 1 1.309652e+00 0.4882788297
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1797 1 3.465911e-01 -1.7229538653
1798 1 -6.167691e-01 0.8944249848
1799 1 -1.625414e+00 -1.3436603900
1800 1 -1.143037e+00 1.9002917475
1801 1 2.054702e-01 1.0029925229
1802 1 5.353407e-01 -0.7690727847
1803 1 1.015901e+00 0.1155251947
1804 1 -1.191417e+00 -0.1543038812
1805 1 -1.326809e+00 -0.2756281241
1806 1 1.075503e+00 0.8886335444
1807 1 -1.619707e+00 1.7773332474
1808 1 -1.695149e-01 -1.4619763104
1809 1 -6.982581e-01 1.1239117771
1810 1 -6.241596e-01 1.3249132144
1811 1 -4.093872e-01 -0.9922053338
1812 1 -7.187791e-01 -1.9239194595
1813 1 -6.310097e-02 1.5328395713
1814 1 1.334947e+00 0.5161138958
1815 1 -1.113178e+00 0.4901558214
1816 1 -1.064865e+00 -1.5481653098
1817 1 1.260519e+00 -0.0184239077
1818 1 -7.163494e-01 0.9711552717
1819 1 -7.741003e-01 -0.0258881988
1820 1 3.700284e-01 0.0806034912
1821 1 -2.542174e-01 -0.7069376983
1822 1 -3.425698e-01 -0.3410852716
1823 1 1.180620e+00 1.5151556945
1824 1 1.582217e+00 0.6093844087
1825 1 1.704888e+00 0.3980028120
1826 1 5.648630e-01 -0.0242636963
1827 1 -4.764913e-01 0.0420294559
1828 1 6.199924e-01 1.4681257278
1829 1 -3.674056e-01 -1.5581161430
1830 1 -5.291112e-01 0.7345380436
1831 1 -5.214788e-01 0.5091522708
1832 1 -8.675957e-01 -1.2417965638
1833 1 9.280934e-01 -0.6573551916
1834 1 -3.513386e-01 1.9991006433
1835 1 8.122890e-01 -1.2409382629
1836 1 5.343666e-01 0.7339604213
1837 1 8.233797e-02 0.4251411363
1838 1 2.119009e-01 -0.8518324261
1839 1 9.254859e-02 1.8774709870
1840 1 -6.365326e-01 1.5379707312
1841 1 -9.475213e-01 0.2387941811
1842 1 1.362999e+00 0.4398575877
1843 1 -1.493494e+00 -0.1744773007
1844 1 1.253609e-02 -0.1416450733
1845 1 1.972215e-01 -1.1871818361
1846 1 1.564884e+00 0.9718870819
1847 1 -5.352198e-01 2.4116529941
1848 1 1.276402e-01 0.4664958261
1849 1 -2.546610e-01 0.8671421158
1850 1 -1.295310e+00 1.9498100600
1851 1 8.712637e-01 -0.5059742897
1852 1 1.042755e+00 -0.2848301306
1853 1 -6.548728e-01 -1.5371535657
1854 1 4.061649e-01 -0.4419641039
1855 1 -3.701574e-01 0.3595010880
1856 1 -8.494660e-01 0.2668094484
1857 1 -4.235023e-01 0.4601201698
1858 1 3.118541e-01 2.5699594034
1859 1 3.858923e-01 1.2908077867
1860 1 5.128240e-01 0.7792068180
1861 1 -4.541796e-01 0.8808773380
1862 1 2.345021e-01 -0.4485462168
1863 1 1.720489e-01 -2.0859594727
1864 1 -5.309989e-01 -0.3731444918
1865 1 -7.640969e-01 1.4966359941
1866 1 1.065466e-02 -0.5176479737
1867 1 -8.209414e-02 -1.1825741286
1868 1 1.269886e+00 -1.3231069934
1869 1 6.340240e-01 -1.2157261114
1870 1 3.964723e-01 -1.9724188031
1871 1 2.129687e-01 -0.2249344209
1872 1 -2.274793e-02 0.4775947994
1873 1 -1.955873e-01 0.1809316898
1874 1 -2.882144e-01 -1.6730899755
1875 1 -8.519617e-02 -0.6308489260
1876 1 -7.614977e-01 -0.4360083612
1877 1 -3.254867e-01 0.2090606673
1878 1 -2.211325e-01 0.2929710562
1879 1 -1.710826e+00 2.3779783778
1880 1 -3.713471e-01 -1.4813535976
1881 1 -1.191724e+00 -2.4675259417
1882 1 5.749024e-01 -0.5813851051
1883 1 -8.643900e-01 -0.2807471520
1884 1 3.922759e-01 -0.3601650438
1885 1 1.321946e+00 -0.2270587191
1886 1 1.275346e+00 0.0060096421
1887 1 -1.461178e+00 1.4219869387
1888 1 4.263679e-01 -0.6560333749
1889 1 1.069635e-01 0.7154114440
1890 1 3.954492e-01 -1.2810537354
1891 1 -5.094093e-01 0.2116882009
1892 1 6.522543e-01 0.4138620126
1893 1 6.110963e-01 -0.7819688049
1894 1 8.737319e-01 2.1829251895
1895 1 -1.470085e+00 0.5001709711
1896 1 -1.193908e-01 0.7720357885
1897 1 1.441880e+00 2.6635069516
1898 1 9.807212e-01 -0.1918281006
1899 1 -1.549435e-01 -0.0477416512
1900 1 -6.565341e-01 -0.7303512121
1901 1 -2.218050e+00 -0.1248124005
1902 1 1.565099e+00 -1.2543759396
1903 1 -6.645336e-01 -0.0018166585
1904 1 -1.106672e+00 0.0184418854
1905 1 8.962007e-01 -1.5790753894
1906 1 -4.423003e-01 1.7012976526
1907 1 -5.403642e-03 -0.4247147067
1908 1 -1.067551e+00 -0.5569274770
1909 1 -6.025665e-01 -0.3705957323
1910 1 1.091899e+00 -0.2698253087
1911 1 6.321106e-01 -1.0712310012
1912 1 -6.891076e-02 1.5093042332
1913 1 -5.260184e-01 -0.2522409869
1914 1 -9.503961e-01 -1.1194321510
1915 1 1.429524e-01 0.0355176979
1916 1 -1.126099e+00 -0.7397448082
1917 1 2.747486e+00 -0.2091672198
1918 1 -4.562190e-01 -0.2211406219
1919 1 -1.615861e+00 -0.1960416425
1920 1 4.395728e-01 0.0851074619
1921 1 2.996376e-01 -0.1635599861
1922 1 1.142633e+00 1.2442966123
1923 1 -1.601222e+00 -0.8298963397
1924 1 -1.514884e+00 -0.9578456729
1925 1 -8.502370e-01 -0.3434408738
1926 1 -4.536467e-01 -0.9954231651
1927 1 7.029974e-02 0.3539936878
1928 1 -1.713754e-01 -1.8076249912
1929 1 1.063081e+00 -0.5676861874
1930 1 -2.771234e-01 0.0987148898
1931 1 -2.123883e-01 -0.7708787733
1932 1 1.524831e+00 -0.3909979096
1933 1 1.754834e-01 -0.3555536583
1934 1 -1.893939e+00 -0.1499947922
1935 1 -7.669144e-01 1.3458038956
1936 1 -2.528206e-01 1.3359909556
1937 1 8.316751e-01 0.3266016616
1938 1 1.010643e+00 -1.0517406052
1939 1 1.796983e+00 -0.5284336240
1940 1 7.229651e-01 -1.1143800298
1941 1 4.178250e-01 -0.0049648993
1942 1 1.196199e-01 0.4954238593
1943 1 1.825934e+00 1.9632261086
1944 1 -1.567389e+00 0.0977832811
1945 1 2.665720e-01 -1.3769637483
1946 1 -2.851051e-02 -0.1787249146
1947 1 7.168300e-01 2.4089706463
1948 1 9.548940e-02 1.1836679379
1949 1 -1.699918e-01 -0.1629387394
1950 1 7.732266e-01 0.4788108005
1951 1 1.055910e+00 1.4768883954
1952 1 1.301988e+00 1.2483121552
1953 1 -5.629535e-01 1.8736015682
1954 1 -1.046239e+00 -1.4697312412
1955 1 7.296283e-01 0.3759873430
1956 1 -1.134532e+00 -0.7568702574
1957 1 -2.730316e+00 0.7899478570
1958 1 6.522059e-01 -0.7758246883
1959 1 -9.077401e-01 1.7400984741
1960 1 -1.746243e-01 0.7273764157
1961 1 -1.749175e+00 0.0412251647
1962 1 6.351639e-01 -0.7465530475
1963 1 3.928764e-01 -0.4669105152
1964 1 1.469288e-01 0.5619024171
1965 1 -4.416916e-02 0.9213398038
1966 1 5.095332e-01 1.3139610919
1967 1 1.036623e+00 -0.6321055477
1968 1 -1.017870e+00 1.1922587748
1969 1 1.540566e+00 -0.2953318428
1970 1 1.905289e+00 -0.7622792482
1971 1 4.042105e-01 0.5947329044
1972 1 -7.069767e-01 1.5472486383
1973 1 -1.008285e+00 0.6816846166
1974 1 4.863330e-01 -0.0116558350
1975 1 -7.022979e-01 0.7742367691
1976 1 3.093437e-01 0.2707596966
1977 1 -1.078647e+00 -0.1883596530
1978 1 5.902743e-01 0.6246672753
1979 1 6.584944e-01 1.4193192881
1980 1 2.048185e+00 -0.9067758246
1981 1 -1.448530e+00 -0.3987914948
1982 1 1.267581e+00 1.4135953220
1983 1 -6.959804e-01 0.2584286770
1984 1 7.405956e-02 -1.2725183814
1985 1 4.446582e-02 -0.0753325430
1986 1 -4.317039e-01 0.0703880778
1987 1 1.688329e+00 -0.7760323143
1988 1 1.932779e-01 -1.6246009465
1989 1 2.666542e-01 0.8940843318
1990 1 -7.428853e-01 1.4278914745
1991 1 2.081491e-01 1.8593265851
1992 1 -8.529617e-01 -0.3979564585
1993 1 9.235143e-01 0.4129440992
1994 1 1.354395e+00 -0.2755167794
1995 1 5.697907e-01 0.8813467819
1996 1 -2.468826e-02 1.6829380542
1997 1 6.948899e-01 0.6945449748
1998 1 -7.752261e-02 0.5096265680
1999 1 4.085734e-01 0.4243439554
2000 1 -1.587949e+00 0.0181455085
attr(,"assign")
[1] 0 1 2
$calc_lik
function (beta)
{
for (n in names(obj$model$submods)) assign(n, eval(get(n)))
p <- eval(obj$model$boolmod)
log.p <- log(p)
log.p1 <- log(1 - p)
eval(llik)
}
<bytecode: 0x555bc6d72188>
<environment: 0x555bc68fb960>
$method
[1] "nlminb"
$model.fit
$nlminb
$nlminb$par
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlminb -1.962174 0.2541573 0.6954825 0.9972405 0.006893159 1.702572
x5
nlminb -0.1970193
$nlminb$nhatend
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 115.457214 31.281924 63.6547716 100.2494647 95.25488 -21.8659765
[2,] 31.281924 117.944232 1.8807743 19.3257198 10.65405 -5.9115098
[3,] 63.654772 1.880774 134.6346070 31.1730783 39.15753 -0.5340214
[4,] 100.249465 19.325720 31.1730783 145.7170857 55.20325 -7.2822566
[5,] 95.254883 10.654048 39.1575254 55.2032529 573.49201 37.1084645
[6,] -21.865976 -5.911510 -0.5340214 -7.2822566 37.10846 171.9634863
[7,] -2.522868 -7.597161 -2.7946852 0.8885202 10.58556 38.7621375
[,7]
[1,] -2.5228681
[2,] -7.5971613
[3,] -2.7946852
[4,] 0.8885202
[5,] 10.5855587
[6,] 38.7621375
[7,] 559.8837337
$nlminb$value
[1] -771.6595
$nlminb$iterations
[1] 42
$nlminb$conv
[1] 0
$nlminb$detail
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlminb -1.962174 0.2541573 0.6954825 0.9972405 0.006893159 1.702572
x5 value fevals gevals niter convcode kkt1 kkt2 xtimes
nlminb -0.1970193 771.6595 54 408 42 0 TRUE TRUE 0.563
attr(,"class")
[1] "boolfit" "list"
attr(,"class")
[1] "boolean"
>
> ## Multiple optimizers can be specified in a single call to boolean.
> ## Here we fit with the nlm and nlminb optimizers.
> (fit1.OR <- boolean(mod.OR, method=c("nlm", "nlminb")))
$call
boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5, data = Df,
family = list(binomial("probit")))
$link
$link$a
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$link$b
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$model
$model$submods
$model$submods$a
[1] "x1" "x2" "x3"
$model$submods$b
[1] "x4" "x5"
$model$boolmod
expression((a %|% b))
$N
[1] 2000
$k
[1] 7
$coef.labels
[1] "(Intercept)_a" "x1_a" "x2_a" "x3_a"
[5] "(Intercept)_b" "x4_b" "x5_b"
$coef.idx
$coef.idx$a
[1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE
$coef.idx$b
[1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE
$response
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
1 1 1 0 0 0 1 1 1 0 0 1 0 1 0 1
33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
0 1 1 1 1 0 0 1 1 1 1 0 0 1 0 1
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1
65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
1 1 1 1 1 0 0 1 1 1 1 1 0 1 0 0
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0
97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
1 1 0 1 0 0 0 0 0 0 1 1 1 0 1 1
113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
1 0 0 0 0 1 0 1 0 1 1 1 0 0 1 1
129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1
145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
0 1 1 0 0 1 0 1 1 1 0 1 1 1 0 1
161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1
177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
1 1 0 1 1 0 1 1 1 1 1 0 0 0 0 0
193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
1 0 1 1 1 0 0 0 0 1 0 1 1 1 0 1
209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
0 1 0 0 1 0 1 1 1 1 1 0 1 0 1 1
225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0
241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
1 1 1 1 0 0 1 0 1 0 0 0 0 0 1 0
257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
1 0 1 1 0 1 0 1 1 0 1 0 1 0 1 0
273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1
289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304
1 1 0 0 1 1 1 1 0 1 0 0 1 1 1 0
305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320
1 1 1 0 0 1 0 0 0 0 0 0 1 1 0 1
321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
0 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1
337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352
0 1 0 1 1 0 1 1 1 1 0 1 1 1 1 0
353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
1 1 1 1 1 1 1 0 1 0 1 1 0 0 0 0
369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384
1 0 0 0 1 1 0 1 0 0 1 1 1 0 1 0
385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1
401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416
1 0 0 1 1 1 1 1 0 0 1 1 1 0 1 1
417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1
433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448
1 0 0 1 1 1 1 0 1 1 0 0 1 0 1 1
449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464
1 1 0 1 1 0 1 1 0 0 1 1 1 0 1 0
465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 1
481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
1 0 1 1 1 1 0 0 1 0 1 0 0 0 0 1
497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
0 0 1 1 0 0 0 0 1 1 0 0 1 1 1 1
513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
1 0 1 1 0 1 0 0 1 1 0 0 0 0 1 0
529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
0 1 0 1 0 0 0 1 1 0 1 1 1 1 0 1
545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560
1 1 1 0 1 1 0 1 1 1 1 1 0 0 0 1
561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
1 1 1 1 0 1 1 0 1 0 1 0 1 0 0 0
577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592
1 1 1 0 1 0 1 0 1 1 0 1 1 1 0 0
593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608
1 1 1 0 0 0 1 1 0 0 1 1 0 1 0 0
609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
1 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0
625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640
0 1 0 1 1 0 1 1 0 1 0 0 0 1 0 1
641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1
657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672
1 1 1 0 1 1 0 0 0 1 0 0 0 0 1 1
673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
0 0 1 0 1 1 1 0 1 1 0 1 1 0 0 0
689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704
0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720
0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1
721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
1 1 0 1 1 1 1 1 0 0 0 1 1 0 1 1
737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752
0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 1
753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
1 0 1 1 0 1 1 1 0 1 1 1 0 0 0 0
769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784
0 0 0 1 1 1 0 0 0 1 1 1 1 0 0 0
785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
1 0 1 1 0 1 1 1 0 1 1 1 0 0 1 0
801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
0 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1
817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 0
833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848
1 1 1 0 0 0 1 0 1 0 1 0 0 0 1 0
849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864
1 1 1 1 0 1 0 0 1 1 1 1 0 0 0 0
865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880
0 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1
881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896
0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1
897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912
0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 1
913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
0 0 0 1 1 0 1 1 0 0 0 1 0 0 1 1
929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944
0 1 1 1 0 1 0 1 0 1 1 0 1 1 1 1
945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0
961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 0
977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
0 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1
1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
1 0 0 1 1 1 1 0 1 1 0 1 1 1 1 0
1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 1
1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
0 0 0 1 1 0 1 1 1 0 0 1 1 0 0 1
1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
0 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0
1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088
0 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1
1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
1 0 0 0 1 1 1 1 0 1 0 1 1 1 1 0
1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
0 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0
1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
1 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0
1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 0
1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168
0 0 1 0 1 1 1 1 1 0 0 1 1 1 0 0
1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
0 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1
1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0
1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216
1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1
1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 1
1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
0 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0
1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
0 0 1 0 1 0 1 1 0 0 1 0 1 1 0 1
1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
0 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1
1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
0 1 1 0 1 1 0 1 1 0 0 1 1 0 1 1
1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
0 1 1 1 0 0 0 0 0 1 1 0 1 0 1 0
1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0
1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
0 1 0 0 0 0 1 1 0 1 1 0 1 0 0 0
1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 1
1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
1 0 1 1 0 0 1 0 1 1 0 0 0 0 0 1
1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
1 0 0 0 1 1 0 0 1 0 1 1 1 1 0 0
1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
1 0 0 1 1 0 1 0 0 1 0 1 0 0 1 0
1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
1 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0
1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
1 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0
1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456
1 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1
1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472
1 1 1 1 1 0 0 1 0 1 0 1 1 1 0 0
1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488
0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1
1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1
1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520
1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1
1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
1 0 0 1 1 0 0 1 0 0 1 1 1 1 1 0
1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552
1 0 1 1 1 1 1 0 1 0 0 1 1 1 0 0
1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568
0 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0
1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584
0 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1
1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600
1 1 1 1 0 1 0 0 0 0 1 0 1 1 0 1
1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
0 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1
1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632
0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 1
1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648
1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1
1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664
1 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0
1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680
1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0
1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696
1 0 1 1 0 0 1 1 0 0 1 1 1 1 1 1
1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712
1 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0
1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1
1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744
0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0
1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760
0 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1
1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776
0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 1
1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792
1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1
1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808
1 1 1 0 1 0 0 0 1 1 1 0 0 1 0 0
1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824
0 1 1 0 0 1 0 0 1 1 1 1 0 1 1 1
1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840
1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0
1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856
0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 0
1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872
0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 0
1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888
0 1 0 0 1 0 0 0 1 1 0 1 1 1 0 1
1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904
0 0 0 1 1 1 1 1 1 1 0 0 0 1 0 0
1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 0
1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936
1 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0
1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952
1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1
1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968
1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 0
1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
1 1 1 0 1 1 0 1 0 0 0 1 0 1 1 0
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1
$frame
$frame$a
(Intercept) x1 x2 x3
1 1 0.5855288178 0.7094660175 -0.1093033147
2 1 -1.8179559677 0.6300985511 -0.2761841052
3 1 -0.1162478064 1.8173120437 0.3706278643
4 1 0.8168998395 -0.8863575212 -0.3315775899
5 1 0.7796219246 1.4557850825 -0.6443284292
6 1 1.8050975188 -0.4816473637 0.6203798013
7 1 0.8118731786 2.1968335463 2.0491903374
8 1 0.4911882793 -0.3240865787 -1.6620502439
9 1 1.1285108336 -2.3803580614 -1.0602655522
10 1 1.4607294031 -1.4130987779 0.5674032534
11 1 -0.5403860739 1.9476926647 0.0535902704
12 1 0.2779536948 0.6911712729 0.8237953287
13 1 0.1495919806 -1.3425314817 0.5533030755
14 1 -1.8323773062 0.8881394327 1.5934884724
15 1 0.0546155753 -0.7846493732 -1.0493528190
16 1 0.9426008507 0.8262582872 -0.8115404902
17 1 0.6453830742 1.0431435516 -0.3043691127
18 1 1.8670991846 0.6720424690 -0.3079533808
19 1 -0.9639014790 -0.8550825111 1.8869469420
20 1 0.6873321006 -0.5050435175 2.1577198165
21 1 0.2239254075 -1.1562233302 0.4224185278
22 1 -0.5360479985 -0.3116060846 1.5561096426
23 1 -1.2301722469 -1.3240586922 1.2612422744
24 1 -0.5050898063 -0.0521535933 0.6288606251
25 1 1.5448636020 1.3214520172 0.3221515754
26 1 -1.1588210228 -1.8453682894 1.1573252871
27 1 1.6421919908 0.8836548332 0.5248758910
28 1 -1.0479137102 -1.0111225236 0.6689216525
29 1 -1.1402641448 -1.2937152900 -0.5946987691
30 1 0.5401695677 -1.5472919670 0.8496529299
31 1 -1.6193283224 0.5483979230 0.1952821538
32 1 -0.2509466239 1.6993466685 -0.3442987963
33 1 -0.4876385152 0.3031512373 -0.2419740196
34 1 -0.2806491388 0.6330173442 -1.2398183431
35 1 0.1999204838 1.3471927776 0.0360734878
36 1 0.4809501553 2.4835500893 0.4013649875
37 1 -0.9117394242 -0.0490446909 -0.4053874767
38 1 0.0764175190 1.4537473512 0.3741210834
39 1 0.5435221079 -0.5051860029 0.7867957894
40 1 0.7984337703 0.8508604376 -0.4435679681
41 1 -1.4361457062 -0.6292596473 0.2435217656
42 1 0.1052118160 -1.7417130945 0.6452469938
43 1 0.9919506777 -0.8592507612 -0.2815796883
44 1 0.3156128247 0.6602933781 -1.7222024071
45 1 0.8678217426 -2.2900441795 -0.1501902882
46 1 0.6722680417 -0.2093011363 0.0121825061
47 1 0.0784375327 -0.7792610704 0.1665596687
48 1 -0.4678883695 0.7583745568 -0.6417363578
49 1 -0.7000758167 -0.5674015851 -0.2613939259
50 1 0.7711037397 2.7474035422 -0.0839347571
51 1 -0.8086741277 1.0011198474 0.4560525046
52 1 0.6417691636 -0.4150210287 -0.4595756762
53 1 0.7108899984 1.2676017522 -0.1431510558
54 1 -0.1625889099 0.0417091744 0.4830398961
55 1 -0.6346498942 -0.7019630314 0.5768503789
56 1 1.1471271903 0.0147936486 -0.3117392439
57 1 -1.5138640797 0.1642809977 -0.8708652186
58 1 0.3573696852 0.1023930632 -0.6752668252
59 1 -0.4282856231 -0.7139247637 -0.1903840656
60 1 0.1837369095 -2.1503105297 -0.6229665257
61 1 0.5222821685 0.0097937599 -0.4405261962
62 1 0.0382097874 1.1948056324 0.3439583521
63 1 -0.9180582028 -0.0878073286 1.3202937198
64 1 -0.3157324793 -0.5750961042 -1.4063564356
65 1 0.3803156970 0.6051367728 1.0196741368
66 1 0.9331921670 0.4754354212 0.3902816000
67 1 1.4239835987 0.4847305112 0.3492364459
68 1 0.3670449214 -1.5191990495 1.5498033320
69 1 2.0767099333 -0.3076505656 0.9523708852
70 1 -0.1420633784 -1.1830257110 0.5453184095
71 1 0.2929403027 -0.0867079053 -1.4663594955
72 1 -0.3603208006 0.3505937650 0.0282576562
73 1 -0.3758234363 -1.8128337643 0.2886002111
74 1 0.6504302424 0.3102549854 1.6683571689
75 1 -0.1460263920 1.7014318317 0.4713578806
76 1 -0.7789022252 1.1633248478 -1.9645441952
77 1 -0.4772722914 -0.1025805088 0.3686961762
78 1 -0.1505570976 0.9042435562 2.2420361375
79 1 0.7982513947 0.4981721634 0.1195602234
80 1 0.2627041248 0.6407388780 0.3070898308
81 1 0.6279651131 0.0021439509 0.2843777227
82 1 0.8281942395 -0.0848186457 -0.4347199169
83 1 -0.9712292030 -0.1609133622 1.2046870348
84 1 -0.0199197380 0.3280164800 -0.9306286167
85 1 0.5142421022 0.2931683808 -0.5626548305
86 1 -0.4300125693 1.3319083517 -0.7632671561
87 1 -0.8225400839 -0.0476483565 0.1706780107
88 1 0.4906677770 2.2391260841 0.8922123090
89 1 0.3942498996 0.6325214944 0.1025911025
90 1 0.5008706583 -0.9094679509 -2.1972085120
91 1 0.7130781039 1.0282685391 -0.5706327254
92 1 1.2035448160 1.4961826970 -0.1565984322
93 1 -1.2861886466 -0.2725228551 0.1569148369
94 1 -0.8552506726 -1.0057287350 -0.9139523908
95 1 1.2996376499 -0.0293687832 -2.2366218376
96 1 -0.4975791431 -0.6010274043 -0.5121978141
97 1 -0.0240353973 -0.1273903376 -0.1318856204
98 1 0.2109307870 0.7523360895 0.1383795467
99 1 -1.1627837168 0.8546585194 -0.2420958786
100 1 0.2241698326 -0.7368249216 -0.8467107405
101 1 -1.4203238585 -2.4669386469 0.4847158355
102 1 -0.1629454633 0.2204557848 0.8762108117
103 1 -1.9984928459 -1.2347803996 0.8463037177
104 1 0.7674546604 0.8671015019 -1.2545811490
105 1 0.4130208684 0.5642562609 0.7260659521
106 1 0.2242920505 0.7864091404 -0.4233628372
107 1 -0.1835378759 0.8348719973 -0.5872005112
108 1 0.6454110524 -0.3123199556 1.1854482829
109 1 2.1274630349 -0.1877064794 0.0987578449
110 1 2.0930679948 -0.6922359279 -2.2871981109
111 1 -0.4712225866 -0.0332096774 0.3773670678
112 1 2.1185039306 -1.0874598695 1.2160333866
113 1 0.1128439417 0.0652135748 -0.6306272111
114 1 -0.6816232013 -0.0371627219 -0.2872743152
115 1 0.8366902458 -0.3298962638 0.5348022561
116 1 0.5761647328 1.2362238791 0.4755396380
117 1 0.6800620909 -1.3502015698 0.2451024445
118 1 0.2013818061 -1.5754628329 0.0235387774
119 1 -0.5269106809 -1.3128588906 0.8008613463
120 1 1.6818084980 -0.2263103959 -0.1979290319
121 1 -1.6366290973 0.2115625932 -0.4648317284
122 1 -1.3217017095 0.0766927813 -0.3849908794
123 1 -0.4582940078 0.2288621447 -1.3964076285
124 1 0.8028908422 -0.0605252613 0.0893286191
125 1 1.4491694254 0.2055104719 -0.0561199677
126 1 -0.0645002911 0.1324295959 0.0347150846
127 1 -1.0427231529 -0.2136226802 1.7779470769
128 1 -0.6131604214 0.4959067804 0.2456385863
129 1 1.6343509612 0.9754357644 1.0840779376
130 1 1.3355214732 -0.1555622761 0.9266084982
131 1 -0.8186466261 -1.2368228612 2.3172256458
132 1 -1.8112897399 -1.1451476862 1.2808728222
133 1 -1.3686015604 0.5439404116 -1.2380301684
134 1 -0.1533673976 -0.9456886203 0.3124049941
135 1 -0.3843675980 -1.4289357321 0.9634176892
136 1 -1.8070446596 1.3554384433 0.4456801661
137 1 -0.6761129137 1.1360496515 2.1693321275
138 1 1.3026361736 -2.7783255103 0.3413115606
139 1 1.6333026798 -1.8696215131 -0.2500888468
140 1 -2.1379097749 -0.5772610475 0.2364494516
141 1 2.3036270054 2.0208958969 -0.0578785187
142 1 -0.0352204283 0.9242543161 -0.3608283330
143 1 -0.0053473432 -0.5330032870 -0.5154151412
144 1 0.9979542440 -1.1631892607 1.6808938377
145 1 -0.2915456043 0.9726278213 -0.2397067293
146 1 -1.6259750852 -2.1494959449 2.1126279235
147 1 -0.0157344441 -0.1512529191 0.0882313538
148 1 0.3479534670 -1.8995713794 -0.0576824160
149 1 -0.9607436927 -0.8048246106 -0.4959467622
150 1 1.3954864903 3.0936966283 1.9008256312
151 1 0.4617653862 0.0972093387 0.6760255642
152 1 -0.6965953854 -1.2771354355 -0.0924237284
153 1 -1.0308001566 -1.3195143547 -0.0842057018
154 1 0.8633075423 0.9693708778 0.6141290402
155 1 1.8748716168 -0.2375574567 -0.4527795383
156 1 0.8342015841 0.9258648155 0.9431551521
157 1 0.2198254203 -1.0052373893 0.3731011636
158 1 1.0048154415 -0.3738294199 0.9361085041
159 1 0.6292445983 1.0372345783 0.8706170692
160 1 0.2394069412 -1.2561203802 0.2235698835
161 1 -0.8174921146 -0.2492658717 0.4629986479
162 1 1.0764874344 1.1492957868 -1.6221232769
163 1 -0.7405849691 0.2524539178 -0.3369977389
164 1 0.6830537666 0.0783915682 0.5333680907
165 1 0.9401879679 -0.6636770743 -0.8424122326
166 1 0.6504298701 -0.6983808622 -0.4375006119
167 1 0.8969177714 -0.6397434859 -0.0761318178
168 1 -1.9884849273 0.6315952724 -1.9441453638
169 1 0.8878925317 -1.1031640614 -0.3026337057
170 1 0.2574523837 -2.1737384365 0.0261676793
171 1 0.2638040223 0.8894580105 -0.5316916219
172 1 0.8050696694 -0.8247414704 0.6140076455
173 1 -1.0362756757 -2.0581829719 -1.1837015813
174 1 1.7072109072 -0.4014458473 -0.0780482412
175 1 0.2056696111 0.4201750615 1.0637149265
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1851 1 8.712637e-01 -0.5059742897
1852 1 1.042755e+00 -0.2848301306
1853 1 -6.548728e-01 -1.5371535657
1854 1 4.061649e-01 -0.4419641039
1855 1 -3.701574e-01 0.3595010880
1856 1 -8.494660e-01 0.2668094484
1857 1 -4.235023e-01 0.4601201698
1858 1 3.118541e-01 2.5699594034
1859 1 3.858923e-01 1.2908077867
1860 1 5.128240e-01 0.7792068180
1861 1 -4.541796e-01 0.8808773380
1862 1 2.345021e-01 -0.4485462168
1863 1 1.720489e-01 -2.0859594727
1864 1 -5.309989e-01 -0.3731444918
1865 1 -7.640969e-01 1.4966359941
1866 1 1.065466e-02 -0.5176479737
1867 1 -8.209414e-02 -1.1825741286
1868 1 1.269886e+00 -1.3231069934
1869 1 6.340240e-01 -1.2157261114
1870 1 3.964723e-01 -1.9724188031
1871 1 2.129687e-01 -0.2249344209
1872 1 -2.274793e-02 0.4775947994
1873 1 -1.955873e-01 0.1809316898
1874 1 -2.882144e-01 -1.6730899755
1875 1 -8.519617e-02 -0.6308489260
1876 1 -7.614977e-01 -0.4360083612
1877 1 -3.254867e-01 0.2090606673
1878 1 -2.211325e-01 0.2929710562
1879 1 -1.710826e+00 2.3779783778
1880 1 -3.713471e-01 -1.4813535976
1881 1 -1.191724e+00 -2.4675259417
1882 1 5.749024e-01 -0.5813851051
1883 1 -8.643900e-01 -0.2807471520
1884 1 3.922759e-01 -0.3601650438
1885 1 1.321946e+00 -0.2270587191
1886 1 1.275346e+00 0.0060096421
1887 1 -1.461178e+00 1.4219869387
1888 1 4.263679e-01 -0.6560333749
1889 1 1.069635e-01 0.7154114440
1890 1 3.954492e-01 -1.2810537354
1891 1 -5.094093e-01 0.2116882009
1892 1 6.522543e-01 0.4138620126
1893 1 6.110963e-01 -0.7819688049
1894 1 8.737319e-01 2.1829251895
1895 1 -1.470085e+00 0.5001709711
1896 1 -1.193908e-01 0.7720357885
1897 1 1.441880e+00 2.6635069516
1898 1 9.807212e-01 -0.1918281006
1899 1 -1.549435e-01 -0.0477416512
1900 1 -6.565341e-01 -0.7303512121
1901 1 -2.218050e+00 -0.1248124005
1902 1 1.565099e+00 -1.2543759396
1903 1 -6.645336e-01 -0.0018166585
1904 1 -1.106672e+00 0.0184418854
1905 1 8.962007e-01 -1.5790753894
1906 1 -4.423003e-01 1.7012976526
1907 1 -5.403642e-03 -0.4247147067
1908 1 -1.067551e+00 -0.5569274770
1909 1 -6.025665e-01 -0.3705957323
1910 1 1.091899e+00 -0.2698253087
1911 1 6.321106e-01 -1.0712310012
1912 1 -6.891076e-02 1.5093042332
1913 1 -5.260184e-01 -0.2522409869
1914 1 -9.503961e-01 -1.1194321510
1915 1 1.429524e-01 0.0355176979
1916 1 -1.126099e+00 -0.7397448082
1917 1 2.747486e+00 -0.2091672198
1918 1 -4.562190e-01 -0.2211406219
1919 1 -1.615861e+00 -0.1960416425
1920 1 4.395728e-01 0.0851074619
1921 1 2.996376e-01 -0.1635599861
1922 1 1.142633e+00 1.2442966123
1923 1 -1.601222e+00 -0.8298963397
1924 1 -1.514884e+00 -0.9578456729
1925 1 -8.502370e-01 -0.3434408738
1926 1 -4.536467e-01 -0.9954231651
1927 1 7.029974e-02 0.3539936878
1928 1 -1.713754e-01 -1.8076249912
1929 1 1.063081e+00 -0.5676861874
1930 1 -2.771234e-01 0.0987148898
1931 1 -2.123883e-01 -0.7708787733
1932 1 1.524831e+00 -0.3909979096
1933 1 1.754834e-01 -0.3555536583
1934 1 -1.893939e+00 -0.1499947922
1935 1 -7.669144e-01 1.3458038956
1936 1 -2.528206e-01 1.3359909556
1937 1 8.316751e-01 0.3266016616
1938 1 1.010643e+00 -1.0517406052
1939 1 1.796983e+00 -0.5284336240
1940 1 7.229651e-01 -1.1143800298
1941 1 4.178250e-01 -0.0049648993
1942 1 1.196199e-01 0.4954238593
1943 1 1.825934e+00 1.9632261086
1944 1 -1.567389e+00 0.0977832811
1945 1 2.665720e-01 -1.3769637483
1946 1 -2.851051e-02 -0.1787249146
1947 1 7.168300e-01 2.4089706463
1948 1 9.548940e-02 1.1836679379
1949 1 -1.699918e-01 -0.1629387394
1950 1 7.732266e-01 0.4788108005
1951 1 1.055910e+00 1.4768883954
1952 1 1.301988e+00 1.2483121552
1953 1 -5.629535e-01 1.8736015682
1954 1 -1.046239e+00 -1.4697312412
1955 1 7.296283e-01 0.3759873430
1956 1 -1.134532e+00 -0.7568702574
1957 1 -2.730316e+00 0.7899478570
1958 1 6.522059e-01 -0.7758246883
1959 1 -9.077401e-01 1.7400984741
1960 1 -1.746243e-01 0.7273764157
1961 1 -1.749175e+00 0.0412251647
1962 1 6.351639e-01 -0.7465530475
1963 1 3.928764e-01 -0.4669105152
1964 1 1.469288e-01 0.5619024171
1965 1 -4.416916e-02 0.9213398038
1966 1 5.095332e-01 1.3139610919
1967 1 1.036623e+00 -0.6321055477
1968 1 -1.017870e+00 1.1922587748
1969 1 1.540566e+00 -0.2953318428
1970 1 1.905289e+00 -0.7622792482
1971 1 4.042105e-01 0.5947329044
1972 1 -7.069767e-01 1.5472486383
1973 1 -1.008285e+00 0.6816846166
1974 1 4.863330e-01 -0.0116558350
1975 1 -7.022979e-01 0.7742367691
1976 1 3.093437e-01 0.2707596966
1977 1 -1.078647e+00 -0.1883596530
1978 1 5.902743e-01 0.6246672753
1979 1 6.584944e-01 1.4193192881
1980 1 2.048185e+00 -0.9067758246
1981 1 -1.448530e+00 -0.3987914948
1982 1 1.267581e+00 1.4135953220
1983 1 -6.959804e-01 0.2584286770
1984 1 7.405956e-02 -1.2725183814
1985 1 4.446582e-02 -0.0753325430
1986 1 -4.317039e-01 0.0703880778
1987 1 1.688329e+00 -0.7760323143
1988 1 1.932779e-01 -1.6246009465
1989 1 2.666542e-01 0.8940843318
1990 1 -7.428853e-01 1.4278914745
1991 1 2.081491e-01 1.8593265851
1992 1 -8.529617e-01 -0.3979564585
1993 1 9.235143e-01 0.4129440992
1994 1 1.354395e+00 -0.2755167794
1995 1 5.697907e-01 0.8813467819
1996 1 -2.468826e-02 1.6829380542
1997 1 6.948899e-01 0.6945449748
1998 1 -7.752261e-02 0.5096265680
1999 1 4.085734e-01 0.4243439554
2000 1 -1.587949e+00 0.0181455085
attr(,"assign")
[1] 0 1 2
$calc_lik
function (beta)
{
for (n in names(obj$model$submods)) assign(n, eval(get(n)))
p <- eval(obj$model$boolmod)
log.p <- log(p)
log.p1 <- log(1 - p)
eval(llik)
}
<bytecode: 0x555bc6d72188>
<environment: 0x555bc68fb960>
$method
[1] "nlm" "nlminb"
$model.fit
$nlm
$nlm$par
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlm -1.962176 0.2541561 0.6954833 0.9972418 0.006893525 1.702574
x5
nlm -0.197018
$nlm$nhatend
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 115.456997 31.281691 63.6547636 100.2494259 95.25477 -21.8660312
[2,] 31.281691 117.943969 1.8807256 19.3256601 10.65402 -5.9115476
[3,] 63.654764 1.880726 134.6344701 31.1731459 39.15750 -0.5340373
[4,] 100.249426 19.325660 31.1731459 145.7169853 55.20325 -7.2822927
[5,] 95.254774 10.654019 39.1575019 55.2032487 573.49187 37.1083998
[6,] -21.866031 -5.911548 -0.5340373 -7.2822927 37.10840 171.9631686
[7,] -2.522996 -7.597149 -2.7947502 0.8884992 10.58570 38.7618385
[,7]
[1,] -2.5229956
[2,] -7.5971495
[3,] -2.7947502
[4,] 0.8884992
[5,] 10.5857040
[6,] 38.7618385
[7,] 559.8836759
$nlm$value
[1] -771.6595
$nlm$iterations
[1] 24
$nlm$conv
[1] 0
$nlm$detail
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlm -1.962176 0.2541561 0.6954833 0.9972418 0.006893525 1.702574
nlminb -1.962174 0.2541573 0.6954825 0.9972405 0.006893159 1.702572
x5 value fevals gevals niter convcode kkt1 kkt2 xtimes
nlm -0.1970180 771.6595 NA NA 24 0 TRUE TRUE 0.255
nlminb -0.1970193 771.6595 54 408 42 0 TRUE TRUE 0.579
$nlminb
$nlminb$par
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlminb -1.962174 0.2541573 0.6954825 0.9972405 0.006893159 1.702572
x5
nlminb -0.1970193
$nlminb$nhatend
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 115.457214 31.281924 63.6547716 100.2494647 95.25488 -21.8659765
[2,] 31.281924 117.944232 1.8807743 19.3257198 10.65405 -5.9115098
[3,] 63.654772 1.880774 134.6346070 31.1730783 39.15753 -0.5340214
[4,] 100.249465 19.325720 31.1730783 145.7170857 55.20325 -7.2822566
[5,] 95.254883 10.654048 39.1575254 55.2032529 573.49201 37.1084645
[6,] -21.865976 -5.911510 -0.5340214 -7.2822566 37.10846 171.9634863
[7,] -2.522868 -7.597161 -2.7946852 0.8885202 10.58556 38.7621375
[,7]
[1,] -2.5228681
[2,] -7.5971613
[3,] -2.7946852
[4,] 0.8885202
[5,] 10.5855587
[6,] 38.7621375
[7,] 559.8837337
$nlminb$value
[1] -771.6595
$nlminb$iterations
[1] 42
$nlminb$conv
[1] 0
$nlminb$detail
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlm -1.962176 0.2541561 0.6954833 0.9972418 0.006893525 1.702574
nlminb -1.962174 0.2541573 0.6954825 0.9972405 0.006893159 1.702572
x5 value fevals gevals niter convcode kkt1 kkt2 xtimes
nlm -0.1970180 771.6595 NA NA 24 0 TRUE TRUE 0.255
nlminb -0.1970193 771.6595 54 408 42 0 TRUE TRUE 0.579
attr(,"class")
[1] "boolfit" "list"
attr(,"class")
[1] "boolean"
>
> ## Re-fit, with BFGS and a higher maximum number of iterations. All
> ## of the options that go along with nlm(), optim(), and genoud() should
> ## be transparently passable through boolean.
> (fit2.OR <- boolean(mod.OR, method="BFGS", control = list(maxit = 500)))
$call
boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5, data = Df,
family = list(binomial("probit")))
$link
$link$a
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$link$b
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$model
$model$submods
$model$submods$a
[1] "x1" "x2" "x3"
$model$submods$b
[1] "x4" "x5"
$model$boolmod
expression((a %|% b))
$N
[1] 2000
$k
[1] 7
$coef.labels
[1] "(Intercept)_a" "x1_a" "x2_a" "x3_a"
[5] "(Intercept)_b" "x4_b" "x5_b"
$coef.idx
$coef.idx$a
[1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE
$coef.idx$b
[1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE
$response
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
1 1 1 0 0 0 1 1 1 0 0 1 0 1 0 1
33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
0 1 1 1 1 0 0 1 1 1 1 0 0 1 0 1
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1
65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
1 1 1 1 1 0 0 1 1 1 1 1 0 1 0 0
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0
97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
1 1 0 1 0 0 0 0 0 0 1 1 1 0 1 1
113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
1 0 0 0 0 1 0 1 0 1 1 1 0 0 1 1
129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1
145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
0 1 1 0 0 1 0 1 1 1 0 1 1 1 0 1
161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1
177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
1 1 0 1 1 0 1 1 1 1 1 0 0 0 0 0
193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
1 0 1 1 1 0 0 0 0 1 0 1 1 1 0 1
209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
0 1 0 0 1 0 1 1 1 1 1 0 1 0 1 1
225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0
241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
1 1 1 1 0 0 1 0 1 0 0 0 0 0 1 0
257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
1 0 1 1 0 1 0 1 1 0 1 0 1 0 1 0
273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1
289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304
1 1 0 0 1 1 1 1 0 1 0 0 1 1 1 0
305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320
1 1 1 0 0 1 0 0 0 0 0 0 1 1 0 1
321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
0 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1
337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352
0 1 0 1 1 0 1 1 1 1 0 1 1 1 1 0
353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
1 1 1 1 1 1 1 0 1 0 1 1 0 0 0 0
369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384
1 0 0 0 1 1 0 1 0 0 1 1 1 0 1 0
385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1
401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416
1 0 0 1 1 1 1 1 0 0 1 1 1 0 1 1
417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1
433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448
1 0 0 1 1 1 1 0 1 1 0 0 1 0 1 1
449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464
1 1 0 1 1 0 1 1 0 0 1 1 1 0 1 0
465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 1
481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
1 0 1 1 1 1 0 0 1 0 1 0 0 0 0 1
497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
0 0 1 1 0 0 0 0 1 1 0 0 1 1 1 1
513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
1 0 1 1 0 1 0 0 1 1 0 0 0 0 1 0
529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
0 1 0 1 0 0 0 1 1 0 1 1 1 1 0 1
545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560
1 1 1 0 1 1 0 1 1 1 1 1 0 0 0 1
561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
1 1 1 1 0 1 1 0 1 0 1 0 1 0 0 0
577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592
1 1 1 0 1 0 1 0 1 1 0 1 1 1 0 0
593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608
1 1 1 0 0 0 1 1 0 0 1 1 0 1 0 0
609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
1 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0
625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640
0 1 0 1 1 0 1 1 0 1 0 0 0 1 0 1
641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1
657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672
1 1 1 0 1 1 0 0 0 1 0 0 0 0 1 1
673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
0 0 1 0 1 1 1 0 1 1 0 1 1 0 0 0
689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704
0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720
0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1
721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
1 1 0 1 1 1 1 1 0 0 0 1 1 0 1 1
737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752
0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 1
753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
1 0 1 1 0 1 1 1 0 1 1 1 0 0 0 0
769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784
0 0 0 1 1 1 0 0 0 1 1 1 1 0 0 0
785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
1 0 1 1 0 1 1 1 0 1 1 1 0 0 1 0
801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
0 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1
817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 0
833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848
1 1 1 0 0 0 1 0 1 0 1 0 0 0 1 0
849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864
1 1 1 1 0 1 0 0 1 1 1 1 0 0 0 0
865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880
0 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1
881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896
0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1
897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912
0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 1
913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
0 0 0 1 1 0 1 1 0 0 0 1 0 0 1 1
929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944
0 1 1 1 0 1 0 1 0 1 1 0 1 1 1 1
945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0
961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 0
977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
0 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1
1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
1 0 0 1 1 1 1 0 1 1 0 1 1 1 1 0
1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 1
1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
0 0 0 1 1 0 1 1 1 0 0 1 1 0 0 1
1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
0 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0
1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088
0 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1
1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
1 0 0 0 1 1 1 1 0 1 0 1 1 1 1 0
1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
0 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0
1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
1 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0
1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 0
1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168
0 0 1 0 1 1 1 1 1 0 0 1 1 1 0 0
1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
0 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1
1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0
1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216
1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1
1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 1
1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
0 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0
1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
0 0 1 0 1 0 1 1 0 0 1 0 1 1 0 1
1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
0 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1
1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
0 1 1 0 1 1 0 1 1 0 0 1 1 0 1 1
1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
0 1 1 1 0 0 0 0 0 1 1 0 1 0 1 0
1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0
1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
0 1 0 0 0 0 1 1 0 1 1 0 1 0 0 0
1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 1
1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
1 0 1 1 0 0 1 0 1 1 0 0 0 0 0 1
1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
1 0 0 0 1 1 0 0 1 0 1 1 1 1 0 0
1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
1 0 0 1 1 0 1 0 0 1 0 1 0 0 1 0
1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
1 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0
1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
1 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0
1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456
1 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1
1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472
1 1 1 1 1 0 0 1 0 1 0 1 1 1 0 0
1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488
0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1
1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1
1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520
1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1
1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
1 0 0 1 1 0 0 1 0 0 1 1 1 1 1 0
1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552
1 0 1 1 1 1 1 0 1 0 0 1 1 1 0 0
1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568
0 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0
1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584
0 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1
1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600
1 1 1 1 0 1 0 0 0 0 1 0 1 1 0 1
1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
0 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1
1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632
0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 1
1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648
1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1
1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664
1 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0
1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680
1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0
1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696
1 0 1 1 0 0 1 1 0 0 1 1 1 1 1 1
1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712
1 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0
1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1
1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744
0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0
1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760
0 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1
1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776
0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 1
1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792
1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1
1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808
1 1 1 0 1 0 0 0 1 1 1 0 0 1 0 0
1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824
0 1 1 0 0 1 0 0 1 1 1 1 0 1 1 1
1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840
1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0
1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856
0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 0
1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872
0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 0
1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888
0 1 0 0 1 0 0 0 1 1 0 1 1 1 0 1
1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904
0 0 0 1 1 1 1 1 1 1 0 0 0 1 0 0
1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 0
1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936
1 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0
1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952
1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1
1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968
1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 0
1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
1 1 1 0 1 1 0 1 0 0 0 1 0 1 1 0
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1
$frame
$frame$a
(Intercept) x1 x2 x3
1 1 0.5855288178 0.7094660175 -0.1093033147
2 1 -1.8179559677 0.6300985511 -0.2761841052
3 1 -0.1162478064 1.8173120437 0.3706278643
4 1 0.8168998395 -0.8863575212 -0.3315775899
5 1 0.7796219246 1.4557850825 -0.6443284292
6 1 1.8050975188 -0.4816473637 0.6203798013
7 1 0.8118731786 2.1968335463 2.0491903374
8 1 0.4911882793 -0.3240865787 -1.6620502439
9 1 1.1285108336 -2.3803580614 -1.0602655522
10 1 1.4607294031 -1.4130987779 0.5674032534
11 1 -0.5403860739 1.9476926647 0.0535902704
12 1 0.2779536948 0.6911712729 0.8237953287
13 1 0.1495919806 -1.3425314817 0.5533030755
14 1 -1.8323773062 0.8881394327 1.5934884724
15 1 0.0546155753 -0.7846493732 -1.0493528190
16 1 0.9426008507 0.8262582872 -0.8115404902
17 1 0.6453830742 1.0431435516 -0.3043691127
18 1 1.8670991846 0.6720424690 -0.3079533808
19 1 -0.9639014790 -0.8550825111 1.8869469420
20 1 0.6873321006 -0.5050435175 2.1577198165
21 1 0.2239254075 -1.1562233302 0.4224185278
22 1 -0.5360479985 -0.3116060846 1.5561096426
23 1 -1.2301722469 -1.3240586922 1.2612422744
24 1 -0.5050898063 -0.0521535933 0.6288606251
25 1 1.5448636020 1.3214520172 0.3221515754
26 1 -1.1588210228 -1.8453682894 1.1573252871
27 1 1.6421919908 0.8836548332 0.5248758910
28 1 -1.0479137102 -1.0111225236 0.6689216525
29 1 -1.1402641448 -1.2937152900 -0.5946987691
30 1 0.5401695677 -1.5472919670 0.8496529299
31 1 -1.6193283224 0.5483979230 0.1952821538
32 1 -0.2509466239 1.6993466685 -0.3442987963
33 1 -0.4876385152 0.3031512373 -0.2419740196
34 1 -0.2806491388 0.6330173442 -1.2398183431
35 1 0.1999204838 1.3471927776 0.0360734878
36 1 0.4809501553 2.4835500893 0.4013649875
37 1 -0.9117394242 -0.0490446909 -0.4053874767
38 1 0.0764175190 1.4537473512 0.3741210834
39 1 0.5435221079 -0.5051860029 0.7867957894
40 1 0.7984337703 0.8508604376 -0.4435679681
41 1 -1.4361457062 -0.6292596473 0.2435217656
42 1 0.1052118160 -1.7417130945 0.6452469938
43 1 0.9919506777 -0.8592507612 -0.2815796883
44 1 0.3156128247 0.6602933781 -1.7222024071
45 1 0.8678217426 -2.2900441795 -0.1501902882
46 1 0.6722680417 -0.2093011363 0.0121825061
47 1 0.0784375327 -0.7792610704 0.1665596687
48 1 -0.4678883695 0.7583745568 -0.6417363578
49 1 -0.7000758167 -0.5674015851 -0.2613939259
50 1 0.7711037397 2.7474035422 -0.0839347571
51 1 -0.8086741277 1.0011198474 0.4560525046
52 1 0.6417691636 -0.4150210287 -0.4595756762
53 1 0.7108899984 1.2676017522 -0.1431510558
54 1 -0.1625889099 0.0417091744 0.4830398961
55 1 -0.6346498942 -0.7019630314 0.5768503789
56 1 1.1471271903 0.0147936486 -0.3117392439
57 1 -1.5138640797 0.1642809977 -0.8708652186
58 1 0.3573696852 0.1023930632 -0.6752668252
59 1 -0.4282856231 -0.7139247637 -0.1903840656
60 1 0.1837369095 -2.1503105297 -0.6229665257
61 1 0.5222821685 0.0097937599 -0.4405261962
62 1 0.0382097874 1.1948056324 0.3439583521
63 1 -0.9180582028 -0.0878073286 1.3202937198
64 1 -0.3157324793 -0.5750961042 -1.4063564356
65 1 0.3803156970 0.6051367728 1.0196741368
66 1 0.9331921670 0.4754354212 0.3902816000
67 1 1.4239835987 0.4847305112 0.3492364459
68 1 0.3670449214 -1.5191990495 1.5498033320
69 1 2.0767099333 -0.3076505656 0.9523708852
70 1 -0.1420633784 -1.1830257110 0.5453184095
71 1 0.2929403027 -0.0867079053 -1.4663594955
72 1 -0.3603208006 0.3505937650 0.0282576562
73 1 -0.3758234363 -1.8128337643 0.2886002111
74 1 0.6504302424 0.3102549854 1.6683571689
75 1 -0.1460263920 1.7014318317 0.4713578806
76 1 -0.7789022252 1.1633248478 -1.9645441952
77 1 -0.4772722914 -0.1025805088 0.3686961762
78 1 -0.1505570976 0.9042435562 2.2420361375
79 1 0.7982513947 0.4981721634 0.1195602234
80 1 0.2627041248 0.6407388780 0.3070898308
81 1 0.6279651131 0.0021439509 0.2843777227
82 1 0.8281942395 -0.0848186457 -0.4347199169
83 1 -0.9712292030 -0.1609133622 1.2046870348
84 1 -0.0199197380 0.3280164800 -0.9306286167
85 1 0.5142421022 0.2931683808 -0.5626548305
86 1 -0.4300125693 1.3319083517 -0.7632671561
87 1 -0.8225400839 -0.0476483565 0.1706780107
88 1 0.4906677770 2.2391260841 0.8922123090
89 1 0.3942498996 0.6325214944 0.1025911025
90 1 0.5008706583 -0.9094679509 -2.1972085120
91 1 0.7130781039 1.0282685391 -0.5706327254
92 1 1.2035448160 1.4961826970 -0.1565984322
93 1 -1.2861886466 -0.2725228551 0.1569148369
94 1 -0.8552506726 -1.0057287350 -0.9139523908
95 1 1.2996376499 -0.0293687832 -2.2366218376
96 1 -0.4975791431 -0.6010274043 -0.5121978141
97 1 -0.0240353973 -0.1273903376 -0.1318856204
98 1 0.2109307870 0.7523360895 0.1383795467
99 1 -1.1627837168 0.8546585194 -0.2420958786
100 1 0.2241698326 -0.7368249216 -0.8467107405
101 1 -1.4203238585 -2.4669386469 0.4847158355
102 1 -0.1629454633 0.2204557848 0.8762108117
103 1 -1.9984928459 -1.2347803996 0.8463037177
104 1 0.7674546604 0.8671015019 -1.2545811490
105 1 0.4130208684 0.5642562609 0.7260659521
106 1 0.2242920505 0.7864091404 -0.4233628372
107 1 -0.1835378759 0.8348719973 -0.5872005112
108 1 0.6454110524 -0.3123199556 1.1854482829
109 1 2.1274630349 -0.1877064794 0.0987578449
110 1 2.0930679948 -0.6922359279 -2.2871981109
111 1 -0.4712225866 -0.0332096774 0.3773670678
112 1 2.1185039306 -1.0874598695 1.2160333866
113 1 0.1128439417 0.0652135748 -0.6306272111
114 1 -0.6816232013 -0.0371627219 -0.2872743152
115 1 0.8366902458 -0.3298962638 0.5348022561
116 1 0.5761647328 1.2362238791 0.4755396380
117 1 0.6800620909 -1.3502015698 0.2451024445
118 1 0.2013818061 -1.5754628329 0.0235387774
119 1 -0.5269106809 -1.3128588906 0.8008613463
120 1 1.6818084980 -0.2263103959 -0.1979290319
121 1 -1.6366290973 0.2115625932 -0.4648317284
122 1 -1.3217017095 0.0766927813 -0.3849908794
123 1 -0.4582940078 0.2288621447 -1.3964076285
124 1 0.8028908422 -0.0605252613 0.0893286191
125 1 1.4491694254 0.2055104719 -0.0561199677
126 1 -0.0645002911 0.1324295959 0.0347150846
127 1 -1.0427231529 -0.2136226802 1.7779470769
128 1 -0.6131604214 0.4959067804 0.2456385863
129 1 1.6343509612 0.9754357644 1.0840779376
130 1 1.3355214732 -0.1555622761 0.9266084982
131 1 -0.8186466261 -1.2368228612 2.3172256458
132 1 -1.8112897399 -1.1451476862 1.2808728222
133 1 -1.3686015604 0.5439404116 -1.2380301684
134 1 -0.1533673976 -0.9456886203 0.3124049941
135 1 -0.3843675980 -1.4289357321 0.9634176892
136 1 -1.8070446596 1.3554384433 0.4456801661
137 1 -0.6761129137 1.1360496515 2.1693321275
138 1 1.3026361736 -2.7783255103 0.3413115606
139 1 1.6333026798 -1.8696215131 -0.2500888468
140 1 -2.1379097749 -0.5772610475 0.2364494516
141 1 2.3036270054 2.0208958969 -0.0578785187
142 1 -0.0352204283 0.9242543161 -0.3608283330
143 1 -0.0053473432 -0.5330032870 -0.5154151412
144 1 0.9979542440 -1.1631892607 1.6808938377
145 1 -0.2915456043 0.9726278213 -0.2397067293
146 1 -1.6259750852 -2.1494959449 2.1126279235
147 1 -0.0157344441 -0.1512529191 0.0882313538
148 1 0.3479534670 -1.8995713794 -0.0576824160
149 1 -0.9607436927 -0.8048246106 -0.4959467622
150 1 1.3954864903 3.0936966283 1.9008256312
151 1 0.4617653862 0.0972093387 0.6760255642
152 1 -0.6965953854 -1.2771354355 -0.0924237284
153 1 -1.0308001566 -1.3195143547 -0.0842057018
154 1 0.8633075423 0.9693708778 0.6141290402
155 1 1.8748716168 -0.2375574567 -0.4527795383
156 1 0.8342015841 0.9258648155 0.9431551521
157 1 0.2198254203 -1.0052373893 0.3731011636
158 1 1.0048154415 -0.3738294199 0.9361085041
159 1 0.6292445983 1.0372345783 0.8706170692
160 1 0.2394069412 -1.2561203802 0.2235698835
161 1 -0.8174921146 -0.2492658717 0.4629986479
162 1 1.0764874344 1.1492957868 -1.6221232769
163 1 -0.7405849691 0.2524539178 -0.3369977389
164 1 0.6830537666 0.0783915682 0.5333680907
165 1 0.9401879679 -0.6636770743 -0.8424122326
166 1 0.6504298701 -0.6983808622 -0.4375006119
167 1 0.8969177714 -0.6397434859 -0.0761318178
168 1 -1.9884849273 0.6315952724 -1.9441453638
169 1 0.8878925317 -1.1031640614 -0.3026337057
170 1 0.2574523837 -2.1737384365 0.0261676793
171 1 0.2638040223 0.8894580105 -0.5316916219
172 1 0.8050696694 -0.8247414704 0.6140076455
173 1 -1.0362756757 -2.0581829719 -1.1837015813
174 1 1.7072109072 -0.4014458473 -0.0780482412
175 1 0.2056696111 0.4201750615 1.0637149265
176 1 -1.3884242378 0.3100317494 0.1900542145
177 1 0.9000962909 0.1198709617 0.9415274068
178 1 0.7723769019 0.7435963078 -0.5896524839
179 1 0.1398461034 0.2403041739 0.1199819713
180 1 -0.9971703380 -1.1311023094 0.0152093642
181 1 -0.7848609782 -2.5600524404 0.0728007835
182 1 -0.4878667252 0.9087877914 -1.8524641483
183 1 0.0235512251 1.1834596627 -0.5528332480
184 1 0.0119610482 -0.2068075633 0.7388490471
185 1 -1.8156107912 -0.2577453680 -0.0607272387
186 1 0.3807205787 0.2135097407 -0.9685065319
187 1 -1.7006279206 -1.4676776582 -0.2163486506
188 1 -0.2773096650 -1.7433478148 -1.0185528402
189 1 -0.7414661940 0.1912165321 -1.1669836244
190 1 -0.6886211852 0.4078008873 -1.1214051140
191 1 -1.9837605298 0.9780324681 -0.3259468062
192 1 -0.1935253534 -0.2710168892 -0.2649423855
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194 1 -0.1318275149 -1.5780766567 -0.7697308985
195 1 0.9021508715 -0.3494070165 0.5899299967
196 1 1.0865618162 0.8349570023 -0.2968655085
197 1 -0.5352410982 -0.8843313187 1.8610330135
198 1 -0.5770577745 -0.3190149090 -0.3140251328
199 1 0.1092041256 -0.2344074001 0.1619446468
200 1 -0.0677214749 1.4564685363 -1.4429389572
201 1 1.6775117913 0.0794740547 -0.8564274966
202 1 -1.8973583397 -0.0717709117 -1.0308645915
203 1 -0.2793063562 -0.6458904070 -0.5157290929
204 1 0.4537064565 0.2748803304 -1.2717912248
205 1 -0.0258765473 -1.7359637800 -0.9451758837
206 1 0.2073716978 0.4005162697 -0.0514096699
207 1 -0.9232794163 -0.6574483346 1.6549417967
208 1 0.2246130309 1.4205499620 0.5009206892
209 1 0.8964609420 -1.4324676673 0.3859926340
210 1 -1.3872222191 0.2747136542 0.5463119627
211 1 -0.7902889510 -1.0940681260 0.4636973671
212 1 0.3727203973 -0.6314187353 -0.3451947249
213 1 -0.8410156594 -0.5255775617 0.9646760715
214 1 -1.0594503837 -0.2031988588 -0.2605225022
215 1 2.4570741656 -1.4214051662 -1.1706711902
216 1 -0.0336040265 1.0677884112 -1.0843643358
217 1 -0.8257088759 0.4902205365 -0.9265318611
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attr(,"assign")
[1] 0 1 2 3
$frame$b
(Intercept) x4 x5
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1902 1 1.565099e+00 -1.2543759396
1903 1 -6.645336e-01 -0.0018166585
1904 1 -1.106672e+00 0.0184418854
1905 1 8.962007e-01 -1.5790753894
1906 1 -4.423003e-01 1.7012976526
1907 1 -5.403642e-03 -0.4247147067
1908 1 -1.067551e+00 -0.5569274770
1909 1 -6.025665e-01 -0.3705957323
1910 1 1.091899e+00 -0.2698253087
1911 1 6.321106e-01 -1.0712310012
1912 1 -6.891076e-02 1.5093042332
1913 1 -5.260184e-01 -0.2522409869
1914 1 -9.503961e-01 -1.1194321510
1915 1 1.429524e-01 0.0355176979
1916 1 -1.126099e+00 -0.7397448082
1917 1 2.747486e+00 -0.2091672198
1918 1 -4.562190e-01 -0.2211406219
1919 1 -1.615861e+00 -0.1960416425
1920 1 4.395728e-01 0.0851074619
1921 1 2.996376e-01 -0.1635599861
1922 1 1.142633e+00 1.2442966123
1923 1 -1.601222e+00 -0.8298963397
1924 1 -1.514884e+00 -0.9578456729
1925 1 -8.502370e-01 -0.3434408738
1926 1 -4.536467e-01 -0.9954231651
1927 1 7.029974e-02 0.3539936878
1928 1 -1.713754e-01 -1.8076249912
1929 1 1.063081e+00 -0.5676861874
1930 1 -2.771234e-01 0.0987148898
1931 1 -2.123883e-01 -0.7708787733
1932 1 1.524831e+00 -0.3909979096
1933 1 1.754834e-01 -0.3555536583
1934 1 -1.893939e+00 -0.1499947922
1935 1 -7.669144e-01 1.3458038956
1936 1 -2.528206e-01 1.3359909556
1937 1 8.316751e-01 0.3266016616
1938 1 1.010643e+00 -1.0517406052
1939 1 1.796983e+00 -0.5284336240
1940 1 7.229651e-01 -1.1143800298
1941 1 4.178250e-01 -0.0049648993
1942 1 1.196199e-01 0.4954238593
1943 1 1.825934e+00 1.9632261086
1944 1 -1.567389e+00 0.0977832811
1945 1 2.665720e-01 -1.3769637483
1946 1 -2.851051e-02 -0.1787249146
1947 1 7.168300e-01 2.4089706463
1948 1 9.548940e-02 1.1836679379
1949 1 -1.699918e-01 -0.1629387394
1950 1 7.732266e-01 0.4788108005
1951 1 1.055910e+00 1.4768883954
1952 1 1.301988e+00 1.2483121552
1953 1 -5.629535e-01 1.8736015682
1954 1 -1.046239e+00 -1.4697312412
1955 1 7.296283e-01 0.3759873430
1956 1 -1.134532e+00 -0.7568702574
1957 1 -2.730316e+00 0.7899478570
1958 1 6.522059e-01 -0.7758246883
1959 1 -9.077401e-01 1.7400984741
1960 1 -1.746243e-01 0.7273764157
1961 1 -1.749175e+00 0.0412251647
1962 1 6.351639e-01 -0.7465530475
1963 1 3.928764e-01 -0.4669105152
1964 1 1.469288e-01 0.5619024171
1965 1 -4.416916e-02 0.9213398038
1966 1 5.095332e-01 1.3139610919
1967 1 1.036623e+00 -0.6321055477
1968 1 -1.017870e+00 1.1922587748
1969 1 1.540566e+00 -0.2953318428
1970 1 1.905289e+00 -0.7622792482
1971 1 4.042105e-01 0.5947329044
1972 1 -7.069767e-01 1.5472486383
1973 1 -1.008285e+00 0.6816846166
1974 1 4.863330e-01 -0.0116558350
1975 1 -7.022979e-01 0.7742367691
1976 1 3.093437e-01 0.2707596966
1977 1 -1.078647e+00 -0.1883596530
1978 1 5.902743e-01 0.6246672753
1979 1 6.584944e-01 1.4193192881
1980 1 2.048185e+00 -0.9067758246
1981 1 -1.448530e+00 -0.3987914948
1982 1 1.267581e+00 1.4135953220
1983 1 -6.959804e-01 0.2584286770
1984 1 7.405956e-02 -1.2725183814
1985 1 4.446582e-02 -0.0753325430
1986 1 -4.317039e-01 0.0703880778
1987 1 1.688329e+00 -0.7760323143
1988 1 1.932779e-01 -1.6246009465
1989 1 2.666542e-01 0.8940843318
1990 1 -7.428853e-01 1.4278914745
1991 1 2.081491e-01 1.8593265851
1992 1 -8.529617e-01 -0.3979564585
1993 1 9.235143e-01 0.4129440992
1994 1 1.354395e+00 -0.2755167794
1995 1 5.697907e-01 0.8813467819
1996 1 -2.468826e-02 1.6829380542
1997 1 6.948899e-01 0.6945449748
1998 1 -7.752261e-02 0.5096265680
1999 1 4.085734e-01 0.4243439554
2000 1 -1.587949e+00 0.0181455085
attr(,"assign")
[1] 0 1 2
$calc_lik
function (beta)
{
for (n in names(obj$model$submods)) assign(n, eval(get(n)))
p <- eval(obj$model$boolmod)
log.p <- log(p)
log.p1 <- log(1 - p)
eval(llik)
}
<bytecode: 0x555bc6d72188>
<environment: 0x555bc68fb960>
$method
[1] "BFGS"
$model.fit
$BFGS
$BFGS$par
X.Intercept. x1 x2 x3 X.Intercept..1 x4 x5
BFGS -1.96264 0.25421 0.6956157 0.9975053 0.006945397 1.70257 -0.1970051
$BFGS$nhatend
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 115.415456 31.274778 63.6456959 100.237354 95.23245 -21.8675831
[2,] 31.274778 117.904100 1.8828406 19.327963 10.65650 -5.9135497
[3,] 63.645696 1.882841 134.5975938 31.183303 39.15290 -0.5354693
[4,] 100.237354 19.327963 31.1833027 145.693121 55.20376 -7.2869532
[5,] 95.232446 10.656496 39.1529009 55.203757 573.52298 37.0939880
[6,] -21.867583 -5.913550 -0.5354693 -7.286953 37.09399 171.9705056
[7,] -2.524958 -7.593851 -2.7941225 0.889493 10.58814 38.7623054
[,7]
[1,] -2.524958
[2,] -7.593851
[3,] -2.794123
[4,] 0.889493
[5,] 10.588142
[6,] 38.762305
[7,] 559.921821
$BFGS$value
[1] -771.6595
$BFGS$iterations
[1] NA
$BFGS$conv
[1] 0
$BFGS$detail
X.Intercept. x1 x2 x3 X.Intercept..1 x4 x5
BFGS -1.96264 0.25421 0.6956157 0.9975053 0.006945397 1.70257 -0.1970051
value fevals gevals niter convcode kkt1 kkt2 xtimes
BFGS 771.6595 58 18 NA 0 TRUE TRUE 0.42
attr(,"class")
[1] "boolfit" "list"
attr(,"class")
[1] "boolean"
>
> ## Induce a convergence warning message.
> (fit3.OR <- boolean(mod.OR, method="BFGS", control = list(maxit = 5)))
$call
boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5, data = Df,
family = list(binomial("probit")))
$link
$link$a
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$link$b
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$model
$model$submods
$model$submods$a
[1] "x1" "x2" "x3"
$model$submods$b
[1] "x4" "x5"
$model$boolmod
expression((a %|% b))
$N
[1] 2000
$k
[1] 7
$coef.labels
[1] "(Intercept)_a" "x1_a" "x2_a" "x3_a"
[5] "(Intercept)_b" "x4_b" "x5_b"
$coef.idx
$coef.idx$a
[1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE
$coef.idx$b
[1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE
$response
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
1 1 1 0 0 0 1 1 1 0 0 1 0 1 0 1
33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
0 1 1 1 1 0 0 1 1 1 1 0 0 1 0 1
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1
65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
1 1 1 1 1 0 0 1 1 1 1 1 0 1 0 0
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0
97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
1 1 0 1 0 0 0 0 0 0 1 1 1 0 1 1
113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
1 0 0 0 0 1 0 1 0 1 1 1 0 0 1 1
129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1
145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
0 1 1 0 0 1 0 1 1 1 0 1 1 1 0 1
161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1
177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
1 1 0 1 1 0 1 1 1 1 1 0 0 0 0 0
193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
1 0 1 1 1 0 0 0 0 1 0 1 1 1 0 1
209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
0 1 0 0 1 0 1 1 1 1 1 0 1 0 1 1
225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0
241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
1 1 1 1 0 0 1 0 1 0 0 0 0 0 1 0
257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
1 0 1 1 0 1 0 1 1 0 1 0 1 0 1 0
273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1
289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304
1 1 0 0 1 1 1 1 0 1 0 0 1 1 1 0
305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320
1 1 1 0 0 1 0 0 0 0 0 0 1 1 0 1
321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
0 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1
337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352
0 1 0 1 1 0 1 1 1 1 0 1 1 1 1 0
353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
1 1 1 1 1 1 1 0 1 0 1 1 0 0 0 0
369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384
1 0 0 0 1 1 0 1 0 0 1 1 1 0 1 0
385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1
401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416
1 0 0 1 1 1 1 1 0 0 1 1 1 0 1 1
417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1
433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448
1 0 0 1 1 1 1 0 1 1 0 0 1 0 1 1
449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464
1 1 0 1 1 0 1 1 0 0 1 1 1 0 1 0
465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 1
481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
1 0 1 1 1 1 0 0 1 0 1 0 0 0 0 1
497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
0 0 1 1 0 0 0 0 1 1 0 0 1 1 1 1
513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
1 0 1 1 0 1 0 0 1 1 0 0 0 0 1 0
529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
0 1 0 1 0 0 0 1 1 0 1 1 1 1 0 1
545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560
1 1 1 0 1 1 0 1 1 1 1 1 0 0 0 1
561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
1 1 1 1 0 1 1 0 1 0 1 0 1 0 0 0
577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592
1 1 1 0 1 0 1 0 1 1 0 1 1 1 0 0
593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608
1 1 1 0 0 0 1 1 0 0 1 1 0 1 0 0
609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
1 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0
625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640
0 1 0 1 1 0 1 1 0 1 0 0 0 1 0 1
641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1
657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672
1 1 1 0 1 1 0 0 0 1 0 0 0 0 1 1
673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
0 0 1 0 1 1 1 0 1 1 0 1 1 0 0 0
689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704
0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720
0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1
721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
1 1 0 1 1 1 1 1 0 0 0 1 1 0 1 1
737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752
0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 1
753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
1 0 1 1 0 1 1 1 0 1 1 1 0 0 0 0
769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784
0 0 0 1 1 1 0 0 0 1 1 1 1 0 0 0
785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
1 0 1 1 0 1 1 1 0 1 1 1 0 0 1 0
801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
0 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1
817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 0
833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848
1 1 1 0 0 0 1 0 1 0 1 0 0 0 1 0
849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864
1 1 1 1 0 1 0 0 1 1 1 1 0 0 0 0
865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880
0 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1
881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896
0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1
897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912
0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 1
913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
0 0 0 1 1 0 1 1 0 0 0 1 0 0 1 1
929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944
0 1 1 1 0 1 0 1 0 1 1 0 1 1 1 1
945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0
961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 0
977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
0 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1
1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
1 0 0 1 1 1 1 0 1 1 0 1 1 1 1 0
1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 1
1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
0 0 0 1 1 0 1 1 1 0 0 1 1 0 0 1
1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
0 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0
1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088
0 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1
1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
1 0 0 0 1 1 1 1 0 1 0 1 1 1 1 0
1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
0 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0
1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
1 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0
1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 0
1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168
0 0 1 0 1 1 1 1 1 0 0 1 1 1 0 0
1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
0 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1
1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0
1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216
1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1
1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 1
1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
0 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0
1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
0 0 1 0 1 0 1 1 0 0 1 0 1 1 0 1
1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
0 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1
1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
0 1 1 0 1 1 0 1 1 0 0 1 1 0 1 1
1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
0 1 1 1 0 0 0 0 0 1 1 0 1 0 1 0
1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0
1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
0 1 0 0 0 0 1 1 0 1 1 0 1 0 0 0
1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 1
1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
1 0 1 1 0 0 1 0 1 1 0 0 0 0 0 1
1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
1 0 0 0 1 1 0 0 1 0 1 1 1 1 0 0
1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
1 0 0 1 1 0 1 0 0 1 0 1 0 0 1 0
1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
1 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0
1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
1 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0
1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456
1 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1
1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472
1 1 1 1 1 0 0 1 0 1 0 1 1 1 0 0
1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488
0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1
1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1
1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520
1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1
1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
1 0 0 1 1 0 0 1 0 0 1 1 1 1 1 0
1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552
1 0 1 1 1 1 1 0 1 0 0 1 1 1 0 0
1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568
0 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0
1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584
0 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1
1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600
1 1 1 1 0 1 0 0 0 0 1 0 1 1 0 1
1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
0 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1
1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632
0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 1
1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648
1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1
1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664
1 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0
1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680
1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0
1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696
1 0 1 1 0 0 1 1 0 0 1 1 1 1 1 1
1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712
1 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0
1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1
1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744
0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0
1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760
0 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1
1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776
0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 1
1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792
1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1
1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808
1 1 1 0 1 0 0 0 1 1 1 0 0 1 0 0
1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824
0 1 1 0 0 1 0 0 1 1 1 1 0 1 1 1
1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840
1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0
1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856
0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 0
1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872
0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 0
1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888
0 1 0 0 1 0 0 0 1 1 0 1 1 1 0 1
1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904
0 0 0 1 1 1 1 1 1 1 0 0 0 1 0 0
1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 0
1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936
1 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0
1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952
1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1
1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968
1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 0
1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
1 1 1 0 1 1 0 1 0 0 0 1 0 1 1 0
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1
$frame
$frame$a
(Intercept) x1 x2 x3
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1997 1 6.948899e-01 0.6945449748
1998 1 -7.752261e-02 0.5096265680
1999 1 4.085734e-01 0.4243439554
2000 1 -1.587949e+00 0.0181455085
attr(,"assign")
[1] 0 1 2
$calc_lik
function (beta)
{
for (n in names(obj$model$submods)) assign(n, eval(get(n)))
p <- eval(obj$model$boolmod)
log.p <- log(p)
log.p1 <- log(1 - p)
eval(llik)
}
<bytecode: 0x555bc6d72188>
<environment: 0x555bc68fb960>
$method
[1] "BFGS"
$model.fit
$BFGS
$BFGS$par
X.Intercept. x1 x2 x3 X.Intercept..1 x4
BFGS -1.55604 0.1957866 0.6223986 0.7455059 -0.04884058 1.311676
x5
BFGS -0.2085441
$BFGS$nhatend
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 152.724870 30.350731 78.5240869 95.7768127 127.11743 -10.971630
[2,] 30.350731 156.269234 -1.5652812 14.0418127 10.66959 -4.423217
[3,] 78.524087 -1.565281 172.8665894 21.9240707 48.76609 3.315847
[4,] 95.776813 14.041813 21.9240707 158.7687197 61.88226 -3.110111
[5,] 127.117432 10.669594 48.7660940 61.8822613 633.64449 49.401769
[6,] -10.971630 -4.423217 3.3158474 -3.1101106 49.40177 268.091052
[7,] 4.493651 -6.401603 -0.9885231 0.5891863 3.17210 51.520332
[,7]
[1,] 4.4936515
[2,] -6.4016028
[3,] -0.9885231
[4,] 0.5891863
[5,] 3.1720999
[6,] 51.5203315
[7,] 611.6443925
$BFGS$value
[1] -793.5796
$BFGS$iterations
[1] NA
$BFGS$conv
[1] 1
$BFGS$detail
X.Intercept. x1 x2 x3 X.Intercept..1 x4
BFGS -1.55604 0.1957866 0.6223986 0.7455059 -0.04884058 1.311676
x5 value fevals gevals niter convcode kkt1 kkt2 xtimes
BFGS -0.2085441 793.5796 19 5 NA 1 FALSE TRUE 0.155
attr(,"class")
[1] "boolfit" "list"
attr(,"class")
[1] "boolean"
>
> ## Not run:
> ##D ## Now estimate model with genoud, a genetic optimizer. This has the
> ##D ## capability of using multiple processors via parallel.
> ##D (fit6.OR <- boolean(mod.OR, method="genoud",
> ##D cluster=c("localhost", "localhost"),
> ##D print.level=2))
> ##D
> ##D ## The default SANN optimizer is also available.
> ##D (fit7.OR <- boolean(mod.OR, method="SANN"))
> ## End(Not run)
>
> ## The fit is stored as "model.fit", within the boolean object.
> str(fit.OR$model.fit)
List of 1
$ nlminb:List of 6
..$ par : num [1, 1:7] -1.96217 0.25416 0.69548 0.99724 0.00689 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr "nlminb"
.. .. ..$ : chr [1:7] "X.Intercept." "x1" "x2" "x3" ...
..$ nhatend : num [1:7, 1:7] 115.5 31.3 63.7 100.2 95.3 ...
..$ value : num -772
..$ iterations: num 42
..$ conv : num 0
..$ detail :Classes ‘optimx’ and 'data.frame': 1 obs. of 15 variables:
.. ..$ X.Intercept. : num -1.96
.. ..$ x1 : num 0.254
.. ..$ x2 : num 0.695
.. ..$ x3 : num 0.997
.. ..$ X.Intercept..1: num 0.00689
.. ..$ x4 : num 1.7
.. ..$ x5 : num -0.197
.. ..$ value : num 772
.. ..$ fevals : num 54
.. ..$ gevals : num 408
.. ..$ niter : num 42
.. ..$ convcode : num 0
.. ..$ kkt1 : logi TRUE
.. ..$ kkt2 : logi TRUE
.. ..$ xtimes : num 0.563
.. ..- attr(*, "details")=List of 5
.. .. ..$ : chr "nlminb"
.. .. ..$ : num [1:7] 1.23e-05 1.32e-04 -4.30e-05 -1.06e-04 -4.30e-04 ...
.. .. ..$ : num [1:7, 1:7] 115.5 31.3 63.7 100.2 95.3 ...
.. .. ..$ : num [1:7] 617 562 246 158 121 ...
.. .. ..$ : chr "none"
.. .. ..- attr(*, "dim")= int [1:2] 1 5
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr "nlminb"
.. .. .. ..$ : chr [1:5] "method" "ngatend" "nhatend" "hev" ...
.. ..- attr(*, "maximize")= logi FALSE
.. ..- attr(*, "npar")= int 7
.. ..- attr(*, "follow.on")= logi FALSE
- attr(*, "class")= chr [1:2] "boolfit" "list"
>
> ## Create a summary object, saving and printing it. Then take a look at
> ## the objects stored in the summary object.
> (smry <- summary(fit.OR))
Length Class Mode
call 6 -none- call
link 2 -none- list
model 2 -none- list
N 1 -none- numeric
k 1 -none- numeric
coef.labels 7 -none- character
coef.idx 2 -none- list
response 2000 -none- numeric
frame 2 -none- list
calc_lik 1 -none- function
method 1 -none- character
model.fit 1 boolfit list
> str(smry)
'summaryDefault' chr [1:12, 1:3] " 6" " 2" " 2" " 1" " 1" " 7" ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:12] "call" "link" "model" "N" ...
..$ : chr [1:3] "Length" "Class" "Mode"
>
> ## Extract log-likelihood and coefficient vector.
> logLik(fit.OR)
Error in UseMethod("logLik") :
no applicable method for 'logLik' applied to an object of class "boolean"
Calls: logLik
Execution halted
Flavor: r-patched-linux-x86_64
Version: 3.1.6
Check: examples
Result: ERROR
Running examples in ‘boolean3-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: boolean3-package
> ### Title: Modeling Causal Complexity
> ### Aliases: boolean3 boolean3-package
> ### Keywords: package
>
> ### ** Examples
>
> ## Generate some fake data
> require(mvtnorm)
Loading required package: mvtnorm
> set.seed(12345)
> N <- 2000
> Df <- cbind(1, rmvnorm(N, mean=rep(0, 5)))
>
> ## Set coefficients
> beta.a <- c(-2.00, 0.33, 0.66, 1.00)
> beta.b <- c(0.00, 1.50, -0.25)
>
> ## Generate path probabilities following a normal model.
> y.a <- as.vector(pnorm(tcrossprod(beta.a, Df[, 1:4])))
> y.b <- as.vector(pnorm(tcrossprod(beta.b, Df[, c(1, 5, 6)])))
>
> ## AND and OR-model
> or <- function(x, y) { x + y - x * y }
> and <- function(x, y) { x * y }
> y.star.OR <- or(y.a, y.b)
> y.star.AND <- and(y.a, y.b)
>
> ## Observed responses
> y.OR <- rbinom(N, 1, y.star.OR)
> y.AND <- rbinom(N, 1, y.star.AND)
>
> ## Set up data.frame for estimation
> Df <- cbind(1, Df)
> Df <- as.data.frame(Df)
> Df[,1] <- y.OR
> Df[,2] <- y.AND
> names(Df) <- c("y.OR", "y.AND", "x1", "x2", "x3", "x4", "x5")
>
> ## Before estimating, boolean models need to be specified using the
> ## boolprep function.
>
> ## OR model, specified to use a probit link for each causal path. This
> ## matches the data generating process above.
> mod.OR <- boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5,
+ data = Df, family=list(binomial("probit")))
>
> ## IF you really want to, it's also possible to specify a different
> ## link function for each causal path. These should be in the same
> ## order as specified in the model formula.
> mod.OR2 <- boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5,
+ data = Df, family=list(binomial("probit"),
+ binomial("logit")))
>
> ## Fit the prepared model using the nlminb optimizer (the default).
> (fit.OR <- boolean(mod.OR, method="nlminb", control=list(trace=1)))
fn is fn
Looking for method = nlminb
Function has 7 arguments
Analytic gradient not made available.
Analytic Hessian not made available.
Scale check -- log parameter ratio= 2.069957 log bounds ratio= NA
Method: nlminb
0: 2035.0322: 0.555932 0.00473222 0.0518577 0.0496661 0.555932 0.314324 -0.0401190
1: 1125.8726: -0.128002 0.0388608 0.148522 0.127276 -0.0324267 0.721923 -0.0984183
2: 842.16854: -0.894107 0.0450576 0.220895 0.294603 0.0566570 1.33118 -0.124427
3: 801.45277: -1.16971 0.0904748 0.304796 0.364513 -0.181651 1.54390 -0.197683
4: 793.97921: -1.20286 0.147914 0.450047 0.558015 0.0953275 1.78334 -0.178614
5: 780.71673: -1.44471 0.189016 0.538420 0.765386 -0.198236 1.79638 -0.181454
6: 775.40425: -1.51478 0.180762 0.546683 0.747108 -0.0165510 1.79489 -0.229489
7: 774.57448: -1.57276 0.182887 0.545728 0.743597 -0.0714210 1.78130 -0.190647
8: 773.65776: -1.60590 0.198898 0.574778 0.783028 -0.0163173 1.74617 -0.198394
9: 773.45521: -1.67407 0.202747 0.586465 0.808583 -0.0539769 1.72187 -0.223385
10: 773.01515: -1.71678 0.200876 0.598546 0.838030 -0.0208753 1.72345 -0.159004
11: 772.50230: -1.72318 0.206564 0.606078 0.846533 -0.0158345 1.72432 -0.200933
12: 772.18615: -1.78954 0.231404 0.641645 0.890729 -0.0149774 1.74469 -0.180530
13: 771.89407: -1.85032 0.220442 0.644318 0.940353 -0.00765249 1.69664 -0.201119
14: 771.82963: -1.85752 0.227057 0.652232 0.940176 0.00321924 1.70914 -0.192294
15: 771.80804: -1.86904 0.234336 0.658832 0.936483 -0.00922186 1.71128 -0.202710
16: 771.77147: -1.87385 0.245677 0.671660 0.936852 0.000202047 1.70546 -0.194253
17: 771.73610: -1.88837 0.239128 0.666675 0.949032 -0.000940174 1.71251 -0.199984
18: 771.73251: -1.89568 0.239325 0.673717 0.966182 0.00207717 1.70995 -0.190095
19: 771.69796: -1.91188 0.246202 0.678638 0.968550 0.000991727 1.69952 -0.197926
20: 771.68769: -1.91635 0.246152 0.679388 0.968983 0.00465926 1.70560 -0.195221
21: 771.68585: -1.92140 0.245884 0.681944 0.970632 -0.000298807 1.70718 -0.199282
22: 771.67581: -1.92565 0.249540 0.682440 0.975775 0.00323946 1.70555 -0.197016
23: 771.67138: -1.93310 0.248427 0.685083 0.977666 0.00545687 1.70803 -0.197440
24: 771.66787: -1.93884 0.251036 0.686583 0.982438 0.00259472 1.70576 -0.196931
25: 771.66515: -1.94282 0.249059 0.691551 0.986158 0.00554980 1.70248 -0.197777
26: 771.66252: -1.94927 0.253205 0.688497 0.988277 0.00637468 1.70450 -0.196717
27: 771.66179: -1.94943 0.252731 0.690801 0.989858 0.00511422 1.70388 -0.197930
28: 771.66159: -1.95076 0.251950 0.692556 0.990551 0.00589815 1.70452 -0.195798
29: 771.66071: -1.95311 0.252596 0.692511 0.991245 0.00686234 1.70299 -0.197147
30: 771.66048: -1.95352 0.252645 0.692540 0.991439 0.00603419 1.70315 -0.197013
31: 771.66035: -1.95391 0.252809 0.692812 0.992092 0.00645048 1.70345 -0.197051
32: 771.66010: -1.95539 0.253106 0.692932 0.993201 0.00597275 1.70362 -0.197059
33: 771.65989: -1.95682 0.253095 0.693679 0.993688 0.00656203 1.70289 -0.196863
34: 771.65976: -1.95822 0.253576 0.694616 0.994380 0.00638257 1.70290 -0.197323
35: 771.65960: -1.95958 0.253686 0.694374 0.995708 0.00653884 1.70291 -0.197074
36: 771.65960: -1.96080 0.254736 0.695269 0.996154 0.00683515 1.70302 -0.196825
37: 771.65953: -1.96146 0.254391 0.695463 0.996637 0.00680183 1.70275 -0.197052
38: 771.65952: -1.96215 0.254142 0.695673 0.997170 0.00692894 1.70255 -0.196925
39: 771.65951: -1.96220 0.254146 0.695537 0.997255 0.00692672 1.70258 -0.197040
40: 771.65951: -1.96221 0.254149 0.695523 0.997250 0.00689011 1.70258 -0.197017
41: 771.65951: -1.96219 0.254155 0.695486 0.997245 0.00689580 1.70257 -0.197015
42: 771.65951: -1.96217 0.254157 0.695483 0.997240 0.00689316 1.70257 -0.197019
Post processing for method nlminb
Successful convergence!
Compute Hessian approximation at finish of nlminb
Compute gradient approximation at finish of nlminb
Save results from method nlminb
$par
(Intercept) x1 x2 x3 (Intercept) x4
-1.962174116 0.254157295 0.695482511 0.997240458 0.006893159 1.702572353
x5
-0.197019270
$message
[1] "relative convergence (4)"
$convcode
[1] 0
$value
[1] 771.6595
$fevals
function
54
$gevals
gradient
408
$nitns
[1] 42
$kkt1
[1] TRUE
$kkt2
[1] TRUE
$xtimes
user.self
0.539
Assemble the answers
$call
boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5, data = Df,
family = list(binomial("probit")))
$link
$link$a
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$link$b
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$model
$model$submods
$model$submods$a
[1] "x1" "x2" "x3"
$model$submods$b
[1] "x4" "x5"
$model$boolmod
expression((a %|% b))
$N
[1] 2000
$k
[1] 7
$coef.labels
[1] "(Intercept)_a" "x1_a" "x2_a" "x3_a"
[5] "(Intercept)_b" "x4_b" "x5_b"
$coef.idx
$coef.idx$a
[1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE
$coef.idx$b
[1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE
$response
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
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1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1
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0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1
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929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944
0 1 1 1 0 1 0 1 0 1 1 0 1 1 1 1
945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0
961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 0
977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
0 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1
1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
1 0 0 1 1 1 1 0 1 1 0 1 1 1 1 0
1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 1
1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
0 0 0 1 1 0 1 1 1 0 0 1 1 0 0 1
1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
0 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0
1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088
0 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1
1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
1 0 0 0 1 1 1 1 0 1 0 1 1 1 1 0
1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
0 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0
1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
1 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0
1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 0
1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168
0 0 1 0 1 1 1 1 1 0 0 1 1 1 0 0
1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
0 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1
1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0
1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216
1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1
1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 1
1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
0 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0
1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
0 0 1 0 1 0 1 1 0 0 1 0 1 1 0 1
1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
0 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1
1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
0 1 1 0 1 1 0 1 1 0 0 1 1 0 1 1
1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
0 1 1 1 0 0 0 0 0 1 1 0 1 0 1 0
1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0
1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
0 1 0 0 0 0 1 1 0 1 1 0 1 0 0 0
1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 1
1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
1 0 1 1 0 0 1 0 1 1 0 0 0 0 0 1
1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
1 0 0 0 1 1 0 0 1 0 1 1 1 1 0 0
1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
1 0 0 1 1 0 1 0 0 1 0 1 0 0 1 0
1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
1 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0
1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
1 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0
1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456
1 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1
1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472
1 1 1 1 1 0 0 1 0 1 0 1 1 1 0 0
1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488
0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1
1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1
1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520
1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1
1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
1 0 0 1 1 0 0 1 0 0 1 1 1 1 1 0
1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552
1 0 1 1 1 1 1 0 1 0 0 1 1 1 0 0
1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568
0 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0
1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584
0 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1
1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600
1 1 1 1 0 1 0 0 0 0 1 0 1 1 0 1
1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
0 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1
1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632
0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 1
1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648
1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1
1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664
1 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0
1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680
1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0
1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696
1 0 1 1 0 0 1 1 0 0 1 1 1 1 1 1
1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712
1 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0
1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1
1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744
0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0
1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760
0 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1
1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776
0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 1
1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792
1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1
1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808
1 1 1 0 1 0 0 0 1 1 1 0 0 1 0 0
1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824
0 1 1 0 0 1 0 0 1 1 1 1 0 1 1 1
1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840
1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0
1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856
0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 0
1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872
0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 0
1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888
0 1 0 0 1 0 0 0 1 1 0 1 1 1 0 1
1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904
0 0 0 1 1 1 1 1 1 1 0 0 0 1 0 0
1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 0
1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936
1 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0
1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952
1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1
1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968
1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 0
1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
1 1 1 0 1 1 0 1 0 0 0 1 0 1 1 0
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1
$frame
$frame$a
(Intercept) x1 x2 x3
1 1 0.5855288178 0.7094660175 -0.1093033147
2 1 -1.8179559677 0.6300985511 -0.2761841052
3 1 -0.1162478064 1.8173120437 0.3706278643
4 1 0.8168998395 -0.8863575212 -0.3315775899
5 1 0.7796219246 1.4557850825 -0.6443284292
6 1 1.8050975188 -0.4816473637 0.6203798013
7 1 0.8118731786 2.1968335463 2.0491903374
8 1 0.4911882793 -0.3240865787 -1.6620502439
9 1 1.1285108336 -2.3803580614 -1.0602655522
10 1 1.4607294031 -1.4130987779 0.5674032534
11 1 -0.5403860739 1.9476926647 0.0535902704
12 1 0.2779536948 0.6911712729 0.8237953287
13 1 0.1495919806 -1.3425314817 0.5533030755
14 1 -1.8323773062 0.8881394327 1.5934884724
15 1 0.0546155753 -0.7846493732 -1.0493528190
16 1 0.9426008507 0.8262582872 -0.8115404902
17 1 0.6453830742 1.0431435516 -0.3043691127
18 1 1.8670991846 0.6720424690 -0.3079533808
19 1 -0.9639014790 -0.8550825111 1.8869469420
20 1 0.6873321006 -0.5050435175 2.1577198165
21 1 0.2239254075 -1.1562233302 0.4224185278
22 1 -0.5360479985 -0.3116060846 1.5561096426
23 1 -1.2301722469 -1.3240586922 1.2612422744
24 1 -0.5050898063 -0.0521535933 0.6288606251
25 1 1.5448636020 1.3214520172 0.3221515754
26 1 -1.1588210228 -1.8453682894 1.1573252871
27 1 1.6421919908 0.8836548332 0.5248758910
28 1 -1.0479137102 -1.0111225236 0.6689216525
29 1 -1.1402641448 -1.2937152900 -0.5946987691
30 1 0.5401695677 -1.5472919670 0.8496529299
31 1 -1.6193283224 0.5483979230 0.1952821538
32 1 -0.2509466239 1.6993466685 -0.3442987963
33 1 -0.4876385152 0.3031512373 -0.2419740196
34 1 -0.2806491388 0.6330173442 -1.2398183431
35 1 0.1999204838 1.3471927776 0.0360734878
36 1 0.4809501553 2.4835500893 0.4013649875
37 1 -0.9117394242 -0.0490446909 -0.4053874767
38 1 0.0764175190 1.4537473512 0.3741210834
39 1 0.5435221079 -0.5051860029 0.7867957894
40 1 0.7984337703 0.8508604376 -0.4435679681
41 1 -1.4361457062 -0.6292596473 0.2435217656
42 1 0.1052118160 -1.7417130945 0.6452469938
43 1 0.9919506777 -0.8592507612 -0.2815796883
44 1 0.3156128247 0.6602933781 -1.7222024071
45 1 0.8678217426 -2.2900441795 -0.1501902882
46 1 0.6722680417 -0.2093011363 0.0121825061
47 1 0.0784375327 -0.7792610704 0.1665596687
48 1 -0.4678883695 0.7583745568 -0.6417363578
49 1 -0.7000758167 -0.5674015851 -0.2613939259
50 1 0.7711037397 2.7474035422 -0.0839347571
51 1 -0.8086741277 1.0011198474 0.4560525046
52 1 0.6417691636 -0.4150210287 -0.4595756762
53 1 0.7108899984 1.2676017522 -0.1431510558
54 1 -0.1625889099 0.0417091744 0.4830398961
55 1 -0.6346498942 -0.7019630314 0.5768503789
56 1 1.1471271903 0.0147936486 -0.3117392439
57 1 -1.5138640797 0.1642809977 -0.8708652186
58 1 0.3573696852 0.1023930632 -0.6752668252
59 1 -0.4282856231 -0.7139247637 -0.1903840656
60 1 0.1837369095 -2.1503105297 -0.6229665257
61 1 0.5222821685 0.0097937599 -0.4405261962
62 1 0.0382097874 1.1948056324 0.3439583521
63 1 -0.9180582028 -0.0878073286 1.3202937198
64 1 -0.3157324793 -0.5750961042 -1.4063564356
65 1 0.3803156970 0.6051367728 1.0196741368
66 1 0.9331921670 0.4754354212 0.3902816000
67 1 1.4239835987 0.4847305112 0.3492364459
68 1 0.3670449214 -1.5191990495 1.5498033320
69 1 2.0767099333 -0.3076505656 0.9523708852
70 1 -0.1420633784 -1.1830257110 0.5453184095
71 1 0.2929403027 -0.0867079053 -1.4663594955
72 1 -0.3603208006 0.3505937650 0.0282576562
73 1 -0.3758234363 -1.8128337643 0.2886002111
74 1 0.6504302424 0.3102549854 1.6683571689
75 1 -0.1460263920 1.7014318317 0.4713578806
76 1 -0.7789022252 1.1633248478 -1.9645441952
77 1 -0.4772722914 -0.1025805088 0.3686961762
78 1 -0.1505570976 0.9042435562 2.2420361375
79 1 0.7982513947 0.4981721634 0.1195602234
80 1 0.2627041248 0.6407388780 0.3070898308
81 1 0.6279651131 0.0021439509 0.2843777227
82 1 0.8281942395 -0.0848186457 -0.4347199169
83 1 -0.9712292030 -0.1609133622 1.2046870348
84 1 -0.0199197380 0.3280164800 -0.9306286167
85 1 0.5142421022 0.2931683808 -0.5626548305
86 1 -0.4300125693 1.3319083517 -0.7632671561
87 1 -0.8225400839 -0.0476483565 0.1706780107
88 1 0.4906677770 2.2391260841 0.8922123090
89 1 0.3942498996 0.6325214944 0.1025911025
90 1 0.5008706583 -0.9094679509 -2.1972085120
91 1 0.7130781039 1.0282685391 -0.5706327254
92 1 1.2035448160 1.4961826970 -0.1565984322
93 1 -1.2861886466 -0.2725228551 0.1569148369
94 1 -0.8552506726 -1.0057287350 -0.9139523908
95 1 1.2996376499 -0.0293687832 -2.2366218376
96 1 -0.4975791431 -0.6010274043 -0.5121978141
97 1 -0.0240353973 -0.1273903376 -0.1318856204
98 1 0.2109307870 0.7523360895 0.1383795467
99 1 -1.1627837168 0.8546585194 -0.2420958786
100 1 0.2241698326 -0.7368249216 -0.8467107405
101 1 -1.4203238585 -2.4669386469 0.4847158355
102 1 -0.1629454633 0.2204557848 0.8762108117
103 1 -1.9984928459 -1.2347803996 0.8463037177
104 1 0.7674546604 0.8671015019 -1.2545811490
105 1 0.4130208684 0.5642562609 0.7260659521
106 1 0.2242920505 0.7864091404 -0.4233628372
107 1 -0.1835378759 0.8348719973 -0.5872005112
108 1 0.6454110524 -0.3123199556 1.1854482829
109 1 2.1274630349 -0.1877064794 0.0987578449
110 1 2.0930679948 -0.6922359279 -2.2871981109
111 1 -0.4712225866 -0.0332096774 0.3773670678
112 1 2.1185039306 -1.0874598695 1.2160333866
113 1 0.1128439417 0.0652135748 -0.6306272111
114 1 -0.6816232013 -0.0371627219 -0.2872743152
115 1 0.8366902458 -0.3298962638 0.5348022561
116 1 0.5761647328 1.2362238791 0.4755396380
117 1 0.6800620909 -1.3502015698 0.2451024445
118 1 0.2013818061 -1.5754628329 0.0235387774
119 1 -0.5269106809 -1.3128588906 0.8008613463
120 1 1.6818084980 -0.2263103959 -0.1979290319
121 1 -1.6366290973 0.2115625932 -0.4648317284
122 1 -1.3217017095 0.0766927813 -0.3849908794
123 1 -0.4582940078 0.2288621447 -1.3964076285
124 1 0.8028908422 -0.0605252613 0.0893286191
125 1 1.4491694254 0.2055104719 -0.0561199677
126 1 -0.0645002911 0.1324295959 0.0347150846
127 1 -1.0427231529 -0.2136226802 1.7779470769
128 1 -0.6131604214 0.4959067804 0.2456385863
129 1 1.6343509612 0.9754357644 1.0840779376
130 1 1.3355214732 -0.1555622761 0.9266084982
131 1 -0.8186466261 -1.2368228612 2.3172256458
132 1 -1.8112897399 -1.1451476862 1.2808728222
133 1 -1.3686015604 0.5439404116 -1.2380301684
134 1 -0.1533673976 -0.9456886203 0.3124049941
135 1 -0.3843675980 -1.4289357321 0.9634176892
136 1 -1.8070446596 1.3554384433 0.4456801661
137 1 -0.6761129137 1.1360496515 2.1693321275
138 1 1.3026361736 -2.7783255103 0.3413115606
139 1 1.6333026798 -1.8696215131 -0.2500888468
140 1 -2.1379097749 -0.5772610475 0.2364494516
141 1 2.3036270054 2.0208958969 -0.0578785187
142 1 -0.0352204283 0.9242543161 -0.3608283330
143 1 -0.0053473432 -0.5330032870 -0.5154151412
144 1 0.9979542440 -1.1631892607 1.6808938377
145 1 -0.2915456043 0.9726278213 -0.2397067293
146 1 -1.6259750852 -2.1494959449 2.1126279235
147 1 -0.0157344441 -0.1512529191 0.0882313538
148 1 0.3479534670 -1.8995713794 -0.0576824160
149 1 -0.9607436927 -0.8048246106 -0.4959467622
150 1 1.3954864903 3.0936966283 1.9008256312
151 1 0.4617653862 0.0972093387 0.6760255642
152 1 -0.6965953854 -1.2771354355 -0.0924237284
153 1 -1.0308001566 -1.3195143547 -0.0842057018
154 1 0.8633075423 0.9693708778 0.6141290402
155 1 1.8748716168 -0.2375574567 -0.4527795383
156 1 0.8342015841 0.9258648155 0.9431551521
157 1 0.2198254203 -1.0052373893 0.3731011636
158 1 1.0048154415 -0.3738294199 0.9361085041
159 1 0.6292445983 1.0372345783 0.8706170692
160 1 0.2394069412 -1.2561203802 0.2235698835
161 1 -0.8174921146 -0.2492658717 0.4629986479
162 1 1.0764874344 1.1492957868 -1.6221232769
163 1 -0.7405849691 0.2524539178 -0.3369977389
164 1 0.6830537666 0.0783915682 0.5333680907
165 1 0.9401879679 -0.6636770743 -0.8424122326
166 1 0.6504298701 -0.6983808622 -0.4375006119
167 1 0.8969177714 -0.6397434859 -0.0761318178
168 1 -1.9884849273 0.6315952724 -1.9441453638
169 1 0.8878925317 -1.1031640614 -0.3026337057
170 1 0.2574523837 -2.1737384365 0.0261676793
171 1 0.2638040223 0.8894580105 -0.5316916219
172 1 0.8050696694 -0.8247414704 0.6140076455
173 1 -1.0362756757 -2.0581829719 -1.1837015813
174 1 1.7072109072 -0.4014458473 -0.0780482412
175 1 0.2056696111 0.4201750615 1.0637149265
176 1 -1.3884242378 0.3100317494 0.1900542145
177 1 0.9000962909 0.1198709617 0.9415274068
178 1 0.7723769019 0.7435963078 -0.5896524839
179 1 0.1398461034 0.2403041739 0.1199819713
180 1 -0.9971703380 -1.1311023094 0.0152093642
181 1 -0.7848609782 -2.5600524404 0.0728007835
182 1 -0.4878667252 0.9087877914 -1.8524641483
183 1 0.0235512251 1.1834596627 -0.5528332480
184 1 0.0119610482 -0.2068075633 0.7388490471
185 1 -1.8156107912 -0.2577453680 -0.0607272387
186 1 0.3807205787 0.2135097407 -0.9685065319
187 1 -1.7006279206 -1.4676776582 -0.2163486506
188 1 -0.2773096650 -1.7433478148 -1.0185528402
189 1 -0.7414661940 0.1912165321 -1.1669836244
190 1 -0.6886211852 0.4078008873 -1.1214051140
191 1 -1.9837605298 0.9780324681 -0.3259468062
192 1 -0.1935253534 -0.2710168892 -0.2649423855
193 1 0.3250866403 -0.7821450904 0.5210319607
194 1 -0.1318275149 -1.5780766567 -0.7697308985
195 1 0.9021508715 -0.3494070165 0.5899299967
196 1 1.0865618162 0.8349570023 -0.2968655085
197 1 -0.5352410982 -0.8843313187 1.8610330135
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1878 1 -2.211325e-01 0.2929710562
1879 1 -1.710826e+00 2.3779783778
1880 1 -3.713471e-01 -1.4813535976
1881 1 -1.191724e+00 -2.4675259417
1882 1 5.749024e-01 -0.5813851051
1883 1 -8.643900e-01 -0.2807471520
1884 1 3.922759e-01 -0.3601650438
1885 1 1.321946e+00 -0.2270587191
1886 1 1.275346e+00 0.0060096421
1887 1 -1.461178e+00 1.4219869387
1888 1 4.263679e-01 -0.6560333749
1889 1 1.069635e-01 0.7154114440
1890 1 3.954492e-01 -1.2810537354
1891 1 -5.094093e-01 0.2116882009
1892 1 6.522543e-01 0.4138620126
1893 1 6.110963e-01 -0.7819688049
1894 1 8.737319e-01 2.1829251895
1895 1 -1.470085e+00 0.5001709711
1896 1 -1.193908e-01 0.7720357885
1897 1 1.441880e+00 2.6635069516
1898 1 9.807212e-01 -0.1918281006
1899 1 -1.549435e-01 -0.0477416512
1900 1 -6.565341e-01 -0.7303512121
1901 1 -2.218050e+00 -0.1248124005
1902 1 1.565099e+00 -1.2543759396
1903 1 -6.645336e-01 -0.0018166585
1904 1 -1.106672e+00 0.0184418854
1905 1 8.962007e-01 -1.5790753894
1906 1 -4.423003e-01 1.7012976526
1907 1 -5.403642e-03 -0.4247147067
1908 1 -1.067551e+00 -0.5569274770
1909 1 -6.025665e-01 -0.3705957323
1910 1 1.091899e+00 -0.2698253087
1911 1 6.321106e-01 -1.0712310012
1912 1 -6.891076e-02 1.5093042332
1913 1 -5.260184e-01 -0.2522409869
1914 1 -9.503961e-01 -1.1194321510
1915 1 1.429524e-01 0.0355176979
1916 1 -1.126099e+00 -0.7397448082
1917 1 2.747486e+00 -0.2091672198
1918 1 -4.562190e-01 -0.2211406219
1919 1 -1.615861e+00 -0.1960416425
1920 1 4.395728e-01 0.0851074619
1921 1 2.996376e-01 -0.1635599861
1922 1 1.142633e+00 1.2442966123
1923 1 -1.601222e+00 -0.8298963397
1924 1 -1.514884e+00 -0.9578456729
1925 1 -8.502370e-01 -0.3434408738
1926 1 -4.536467e-01 -0.9954231651
1927 1 7.029974e-02 0.3539936878
1928 1 -1.713754e-01 -1.8076249912
1929 1 1.063081e+00 -0.5676861874
1930 1 -2.771234e-01 0.0987148898
1931 1 -2.123883e-01 -0.7708787733
1932 1 1.524831e+00 -0.3909979096
1933 1 1.754834e-01 -0.3555536583
1934 1 -1.893939e+00 -0.1499947922
1935 1 -7.669144e-01 1.3458038956
1936 1 -2.528206e-01 1.3359909556
1937 1 8.316751e-01 0.3266016616
1938 1 1.010643e+00 -1.0517406052
1939 1 1.796983e+00 -0.5284336240
1940 1 7.229651e-01 -1.1143800298
1941 1 4.178250e-01 -0.0049648993
1942 1 1.196199e-01 0.4954238593
1943 1 1.825934e+00 1.9632261086
1944 1 -1.567389e+00 0.0977832811
1945 1 2.665720e-01 -1.3769637483
1946 1 -2.851051e-02 -0.1787249146
1947 1 7.168300e-01 2.4089706463
1948 1 9.548940e-02 1.1836679379
1949 1 -1.699918e-01 -0.1629387394
1950 1 7.732266e-01 0.4788108005
1951 1 1.055910e+00 1.4768883954
1952 1 1.301988e+00 1.2483121552
1953 1 -5.629535e-01 1.8736015682
1954 1 -1.046239e+00 -1.4697312412
1955 1 7.296283e-01 0.3759873430
1956 1 -1.134532e+00 -0.7568702574
1957 1 -2.730316e+00 0.7899478570
1958 1 6.522059e-01 -0.7758246883
1959 1 -9.077401e-01 1.7400984741
1960 1 -1.746243e-01 0.7273764157
1961 1 -1.749175e+00 0.0412251647
1962 1 6.351639e-01 -0.7465530475
1963 1 3.928764e-01 -0.4669105152
1964 1 1.469288e-01 0.5619024171
1965 1 -4.416916e-02 0.9213398038
1966 1 5.095332e-01 1.3139610919
1967 1 1.036623e+00 -0.6321055477
1968 1 -1.017870e+00 1.1922587748
1969 1 1.540566e+00 -0.2953318428
1970 1 1.905289e+00 -0.7622792482
1971 1 4.042105e-01 0.5947329044
1972 1 -7.069767e-01 1.5472486383
1973 1 -1.008285e+00 0.6816846166
1974 1 4.863330e-01 -0.0116558350
1975 1 -7.022979e-01 0.7742367691
1976 1 3.093437e-01 0.2707596966
1977 1 -1.078647e+00 -0.1883596530
1978 1 5.902743e-01 0.6246672753
1979 1 6.584944e-01 1.4193192881
1980 1 2.048185e+00 -0.9067758246
1981 1 -1.448530e+00 -0.3987914948
1982 1 1.267581e+00 1.4135953220
1983 1 -6.959804e-01 0.2584286770
1984 1 7.405956e-02 -1.2725183814
1985 1 4.446582e-02 -0.0753325430
1986 1 -4.317039e-01 0.0703880778
1987 1 1.688329e+00 -0.7760323143
1988 1 1.932779e-01 -1.6246009465
1989 1 2.666542e-01 0.8940843318
1990 1 -7.428853e-01 1.4278914745
1991 1 2.081491e-01 1.8593265851
1992 1 -8.529617e-01 -0.3979564585
1993 1 9.235143e-01 0.4129440992
1994 1 1.354395e+00 -0.2755167794
1995 1 5.697907e-01 0.8813467819
1996 1 -2.468826e-02 1.6829380542
1997 1 6.948899e-01 0.6945449748
1998 1 -7.752261e-02 0.5096265680
1999 1 4.085734e-01 0.4243439554
2000 1 -1.587949e+00 0.0181455085
attr(,"assign")
[1] 0 1 2
$calc_lik
function (beta)
{
for (n in names(obj$model$submods)) assign(n, eval(get(n)))
p <- eval(obj$model$boolmod)
log.p <- log(p)
log.p1 <- log(1 - p)
eval(llik)
}
<bytecode: 0x5651a922d690>
<environment: 0x5651a8f58740>
$method
[1] "nlminb"
$model.fit
$nlminb
$nlminb$par
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlminb -1.962174 0.2541573 0.6954825 0.9972405 0.006893159 1.702572
x5
nlminb -0.1970193
$nlminb$nhatend
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 115.457214 31.281924 63.6547716 100.2494647 95.25488 -21.8659765
[2,] 31.281924 117.944232 1.8807743 19.3257198 10.65405 -5.9115098
[3,] 63.654772 1.880774 134.6346070 31.1730783 39.15753 -0.5340214
[4,] 100.249465 19.325720 31.1730783 145.7170857 55.20325 -7.2822566
[5,] 95.254883 10.654048 39.1575254 55.2032529 573.49201 37.1084645
[6,] -21.865976 -5.911510 -0.5340214 -7.2822566 37.10846 171.9634863
[7,] -2.522868 -7.597161 -2.7946852 0.8885202 10.58556 38.7621375
[,7]
[1,] -2.5228681
[2,] -7.5971613
[3,] -2.7946852
[4,] 0.8885202
[5,] 10.5855587
[6,] 38.7621375
[7,] 559.8837337
$nlminb$value
[1] -771.6595
$nlminb$iterations
[1] 42
$nlminb$conv
[1] 0
$nlminb$detail
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlminb -1.962174 0.2541573 0.6954825 0.9972405 0.006893159 1.702572
x5 value fevals gevals niter convcode kkt1 kkt2 xtimes
nlminb -0.1970193 771.6595 54 408 42 0 TRUE TRUE 0.539
attr(,"class")
[1] "boolfit" "list"
attr(,"class")
[1] "boolean"
>
> ## Multiple optimizers can be specified in a single call to boolean.
> ## Here we fit with the nlm and nlminb optimizers.
> (fit1.OR <- boolean(mod.OR, method=c("nlm", "nlminb")))
$call
boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5, data = Df,
family = list(binomial("probit")))
$link
$link$a
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$link$b
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$model
$model$submods
$model$submods$a
[1] "x1" "x2" "x3"
$model$submods$b
[1] "x4" "x5"
$model$boolmod
expression((a %|% b))
$N
[1] 2000
$k
[1] 7
$coef.labels
[1] "(Intercept)_a" "x1_a" "x2_a" "x3_a"
[5] "(Intercept)_b" "x4_b" "x5_b"
$coef.idx
$coef.idx$a
[1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE
$coef.idx$b
[1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE
$response
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
1 1 1 0 0 0 1 1 1 0 0 1 0 1 0 1
33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
0 1 1 1 1 0 0 1 1 1 1 0 0 1 0 1
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1
65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
1 1 1 1 1 0 0 1 1 1 1 1 0 1 0 0
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0
97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
1 1 0 1 0 0 0 0 0 0 1 1 1 0 1 1
113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
1 0 0 0 0 1 0 1 0 1 1 1 0 0 1 1
129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1
145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
0 1 1 0 0 1 0 1 1 1 0 1 1 1 0 1
161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1
177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
1 1 0 1 1 0 1 1 1 1 1 0 0 0 0 0
193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
1 0 1 1 1 0 0 0 0 1 0 1 1 1 0 1
209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
0 1 0 0 1 0 1 1 1 1 1 0 1 0 1 1
225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0
241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
1 1 1 1 0 0 1 0 1 0 0 0 0 0 1 0
257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
1 0 1 1 0 1 0 1 1 0 1 0 1 0 1 0
273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1
289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304
1 1 0 0 1 1 1 1 0 1 0 0 1 1 1 0
305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320
1 1 1 0 0 1 0 0 0 0 0 0 1 1 0 1
321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
0 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1
337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352
0 1 0 1 1 0 1 1 1 1 0 1 1 1 1 0
353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
1 1 1 1 1 1 1 0 1 0 1 1 0 0 0 0
369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384
1 0 0 0 1 1 0 1 0 0 1 1 1 0 1 0
385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1
401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416
1 0 0 1 1 1 1 1 0 0 1 1 1 0 1 1
417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1
433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448
1 0 0 1 1 1 1 0 1 1 0 0 1 0 1 1
449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464
1 1 0 1 1 0 1 1 0 0 1 1 1 0 1 0
465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 1
481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
1 0 1 1 1 1 0 0 1 0 1 0 0 0 0 1
497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
0 0 1 1 0 0 0 0 1 1 0 0 1 1 1 1
513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
1 0 1 1 0 1 0 0 1 1 0 0 0 0 1 0
529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
0 1 0 1 0 0 0 1 1 0 1 1 1 1 0 1
545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560
1 1 1 0 1 1 0 1 1 1 1 1 0 0 0 1
561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
1 1 1 1 0 1 1 0 1 0 1 0 1 0 0 0
577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592
1 1 1 0 1 0 1 0 1 1 0 1 1 1 0 0
593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608
1 1 1 0 0 0 1 1 0 0 1 1 0 1 0 0
609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
1 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0
625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640
0 1 0 1 1 0 1 1 0 1 0 0 0 1 0 1
641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1
657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672
1 1 1 0 1 1 0 0 0 1 0 0 0 0 1 1
673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
0 0 1 0 1 1 1 0 1 1 0 1 1 0 0 0
689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704
0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720
0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1
721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
1 1 0 1 1 1 1 1 0 0 0 1 1 0 1 1
737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752
0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 1
753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
1 0 1 1 0 1 1 1 0 1 1 1 0 0 0 0
769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784
0 0 0 1 1 1 0 0 0 1 1 1 1 0 0 0
785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
1 0 1 1 0 1 1 1 0 1 1 1 0 0 1 0
801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
0 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1
817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 0
833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848
1 1 1 0 0 0 1 0 1 0 1 0 0 0 1 0
849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864
1 1 1 1 0 1 0 0 1 1 1 1 0 0 0 0
865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880
0 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1
881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896
0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1
897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912
0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 1
913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
0 0 0 1 1 0 1 1 0 0 0 1 0 0 1 1
929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944
0 1 1 1 0 1 0 1 0 1 1 0 1 1 1 1
945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0
961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 0
977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
0 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1
1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
1 0 0 1 1 1 1 0 1 1 0 1 1 1 1 0
1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 1
1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
0 0 0 1 1 0 1 1 1 0 0 1 1 0 0 1
1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
0 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0
1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088
0 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1
1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
1 0 0 0 1 1 1 1 0 1 0 1 1 1 1 0
1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
0 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0
1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
1 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0
1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 0
1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168
0 0 1 0 1 1 1 1 1 0 0 1 1 1 0 0
1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
0 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1
1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0
1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216
1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1
1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 1
1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
0 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0
1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
0 0 1 0 1 0 1 1 0 0 1 0 1 1 0 1
1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
0 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1
1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
0 1 1 0 1 1 0 1 1 0 0 1 1 0 1 1
1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
0 1 1 1 0 0 0 0 0 1 1 0 1 0 1 0
1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0
1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
0 1 0 0 0 0 1 1 0 1 1 0 1 0 0 0
1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 1
1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
1 0 1 1 0 0 1 0 1 1 0 0 0 0 0 1
1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
1 0 0 0 1 1 0 0 1 0 1 1 1 1 0 0
1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
1 0 0 1 1 0 1 0 0 1 0 1 0 0 1 0
1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
1 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0
1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
1 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0
1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456
1 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1
1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472
1 1 1 1 1 0 0 1 0 1 0 1 1 1 0 0
1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488
0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1
1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1
1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520
1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1
1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
1 0 0 1 1 0 0 1 0 0 1 1 1 1 1 0
1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552
1 0 1 1 1 1 1 0 1 0 0 1 1 1 0 0
1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568
0 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0
1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584
0 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1
1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600
1 1 1 1 0 1 0 0 0 0 1 0 1 1 0 1
1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
0 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1
1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632
0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 1
1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648
1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1
1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664
1 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0
1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680
1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0
1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696
1 0 1 1 0 0 1 1 0 0 1 1 1 1 1 1
1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712
1 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0
1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1
1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744
0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0
1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760
0 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1
1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776
0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 1
1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792
1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1
1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808
1 1 1 0 1 0 0 0 1 1 1 0 0 1 0 0
1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824
0 1 1 0 0 1 0 0 1 1 1 1 0 1 1 1
1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840
1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0
1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856
0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 0
1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872
0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 0
1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888
0 1 0 0 1 0 0 0 1 1 0 1 1 1 0 1
1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904
0 0 0 1 1 1 1 1 1 1 0 0 0 1 0 0
1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 0
1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936
1 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0
1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952
1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1
1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968
1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 0
1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
1 1 1 0 1 1 0 1 0 0 0 1 0 1 1 0
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1
$frame
$frame$a
(Intercept) x1 x2 x3
1 1 0.5855288178 0.7094660175 -0.1093033147
2 1 -1.8179559677 0.6300985511 -0.2761841052
3 1 -0.1162478064 1.8173120437 0.3706278643
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5 1 0.7796219246 1.4557850825 -0.6443284292
6 1 1.8050975188 -0.4816473637 0.6203798013
7 1 0.8118731786 2.1968335463 2.0491903374
8 1 0.4911882793 -0.3240865787 -1.6620502439
9 1 1.1285108336 -2.3803580614 -1.0602655522
10 1 1.4607294031 -1.4130987779 0.5674032534
11 1 -0.5403860739 1.9476926647 0.0535902704
12 1 0.2779536948 0.6911712729 0.8237953287
13 1 0.1495919806 -1.3425314817 0.5533030755
14 1 -1.8323773062 0.8881394327 1.5934884724
15 1 0.0546155753 -0.7846493732 -1.0493528190
16 1 0.9426008507 0.8262582872 -0.8115404902
17 1 0.6453830742 1.0431435516 -0.3043691127
18 1 1.8670991846 0.6720424690 -0.3079533808
19 1 -0.9639014790 -0.8550825111 1.8869469420
20 1 0.6873321006 -0.5050435175 2.1577198165
21 1 0.2239254075 -1.1562233302 0.4224185278
22 1 -0.5360479985 -0.3116060846 1.5561096426
23 1 -1.2301722469 -1.3240586922 1.2612422744
24 1 -0.5050898063 -0.0521535933 0.6288606251
25 1 1.5448636020 1.3214520172 0.3221515754
26 1 -1.1588210228 -1.8453682894 1.1573252871
27 1 1.6421919908 0.8836548332 0.5248758910
28 1 -1.0479137102 -1.0111225236 0.6689216525
29 1 -1.1402641448 -1.2937152900 -0.5946987691
30 1 0.5401695677 -1.5472919670 0.8496529299
31 1 -1.6193283224 0.5483979230 0.1952821538
32 1 -0.2509466239 1.6993466685 -0.3442987963
33 1 -0.4876385152 0.3031512373 -0.2419740196
34 1 -0.2806491388 0.6330173442 -1.2398183431
35 1 0.1999204838 1.3471927776 0.0360734878
36 1 0.4809501553 2.4835500893 0.4013649875
37 1 -0.9117394242 -0.0490446909 -0.4053874767
38 1 0.0764175190 1.4537473512 0.3741210834
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51 1 -0.8086741277 1.0011198474 0.4560525046
52 1 0.6417691636 -0.4150210287 -0.4595756762
53 1 0.7108899984 1.2676017522 -0.1431510558
54 1 -0.1625889099 0.0417091744 0.4830398961
55 1 -0.6346498942 -0.7019630314 0.5768503789
56 1 1.1471271903 0.0147936486 -0.3117392439
57 1 -1.5138640797 0.1642809977 -0.8708652186
58 1 0.3573696852 0.1023930632 -0.6752668252
59 1 -0.4282856231 -0.7139247637 -0.1903840656
60 1 0.1837369095 -2.1503105297 -0.6229665257
61 1 0.5222821685 0.0097937599 -0.4405261962
62 1 0.0382097874 1.1948056324 0.3439583521
63 1 -0.9180582028 -0.0878073286 1.3202937198
64 1 -0.3157324793 -0.5750961042 -1.4063564356
65 1 0.3803156970 0.6051367728 1.0196741368
66 1 0.9331921670 0.4754354212 0.3902816000
67 1 1.4239835987 0.4847305112 0.3492364459
68 1 0.3670449214 -1.5191990495 1.5498033320
69 1 2.0767099333 -0.3076505656 0.9523708852
70 1 -0.1420633784 -1.1830257110 0.5453184095
71 1 0.2929403027 -0.0867079053 -1.4663594955
72 1 -0.3603208006 0.3505937650 0.0282576562
73 1 -0.3758234363 -1.8128337643 0.2886002111
74 1 0.6504302424 0.3102549854 1.6683571689
75 1 -0.1460263920 1.7014318317 0.4713578806
76 1 -0.7789022252 1.1633248478 -1.9645441952
77 1 -0.4772722914 -0.1025805088 0.3686961762
78 1 -0.1505570976 0.9042435562 2.2420361375
79 1 0.7982513947 0.4981721634 0.1195602234
80 1 0.2627041248 0.6407388780 0.3070898308
81 1 0.6279651131 0.0021439509 0.2843777227
82 1 0.8281942395 -0.0848186457 -0.4347199169
83 1 -0.9712292030 -0.1609133622 1.2046870348
84 1 -0.0199197380 0.3280164800 -0.9306286167
85 1 0.5142421022 0.2931683808 -0.5626548305
86 1 -0.4300125693 1.3319083517 -0.7632671561
87 1 -0.8225400839 -0.0476483565 0.1706780107
88 1 0.4906677770 2.2391260841 0.8922123090
89 1 0.3942498996 0.6325214944 0.1025911025
90 1 0.5008706583 -0.9094679509 -2.1972085120
91 1 0.7130781039 1.0282685391 -0.5706327254
92 1 1.2035448160 1.4961826970 -0.1565984322
93 1 -1.2861886466 -0.2725228551 0.1569148369
94 1 -0.8552506726 -1.0057287350 -0.9139523908
95 1 1.2996376499 -0.0293687832 -2.2366218376
96 1 -0.4975791431 -0.6010274043 -0.5121978141
97 1 -0.0240353973 -0.1273903376 -0.1318856204
98 1 0.2109307870 0.7523360895 0.1383795467
99 1 -1.1627837168 0.8546585194 -0.2420958786
100 1 0.2241698326 -0.7368249216 -0.8467107405
101 1 -1.4203238585 -2.4669386469 0.4847158355
102 1 -0.1629454633 0.2204557848 0.8762108117
103 1 -1.9984928459 -1.2347803996 0.8463037177
104 1 0.7674546604 0.8671015019 -1.2545811490
105 1 0.4130208684 0.5642562609 0.7260659521
106 1 0.2242920505 0.7864091404 -0.4233628372
107 1 -0.1835378759 0.8348719973 -0.5872005112
108 1 0.6454110524 -0.3123199556 1.1854482829
109 1 2.1274630349 -0.1877064794 0.0987578449
110 1 2.0930679948 -0.6922359279 -2.2871981109
111 1 -0.4712225866 -0.0332096774 0.3773670678
112 1 2.1185039306 -1.0874598695 1.2160333866
113 1 0.1128439417 0.0652135748 -0.6306272111
114 1 -0.6816232013 -0.0371627219 -0.2872743152
115 1 0.8366902458 -0.3298962638 0.5348022561
116 1 0.5761647328 1.2362238791 0.4755396380
117 1 0.6800620909 -1.3502015698 0.2451024445
118 1 0.2013818061 -1.5754628329 0.0235387774
119 1 -0.5269106809 -1.3128588906 0.8008613463
120 1 1.6818084980 -0.2263103959 -0.1979290319
121 1 -1.6366290973 0.2115625932 -0.4648317284
122 1 -1.3217017095 0.0766927813 -0.3849908794
123 1 -0.4582940078 0.2288621447 -1.3964076285
124 1 0.8028908422 -0.0605252613 0.0893286191
125 1 1.4491694254 0.2055104719 -0.0561199677
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127 1 -1.0427231529 -0.2136226802 1.7779470769
128 1 -0.6131604214 0.4959067804 0.2456385863
129 1 1.6343509612 0.9754357644 1.0840779376
130 1 1.3355214732 -0.1555622761 0.9266084982
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141 1 2.3036270054 2.0208958969 -0.0578785187
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150 1 1.3954864903 3.0936966283 1.9008256312
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162 1 1.0764874344 1.1492957868 -1.6221232769
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167 1 0.8969177714 -0.6397434859 -0.0761318178
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170 1 0.2574523837 -2.1737384365 0.0261676793
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1967 1 1.036623e+00 -0.6321055477
1968 1 -1.017870e+00 1.1922587748
1969 1 1.540566e+00 -0.2953318428
1970 1 1.905289e+00 -0.7622792482
1971 1 4.042105e-01 0.5947329044
1972 1 -7.069767e-01 1.5472486383
1973 1 -1.008285e+00 0.6816846166
1974 1 4.863330e-01 -0.0116558350
1975 1 -7.022979e-01 0.7742367691
1976 1 3.093437e-01 0.2707596966
1977 1 -1.078647e+00 -0.1883596530
1978 1 5.902743e-01 0.6246672753
1979 1 6.584944e-01 1.4193192881
1980 1 2.048185e+00 -0.9067758246
1981 1 -1.448530e+00 -0.3987914948
1982 1 1.267581e+00 1.4135953220
1983 1 -6.959804e-01 0.2584286770
1984 1 7.405956e-02 -1.2725183814
1985 1 4.446582e-02 -0.0753325430
1986 1 -4.317039e-01 0.0703880778
1987 1 1.688329e+00 -0.7760323143
1988 1 1.932779e-01 -1.6246009465
1989 1 2.666542e-01 0.8940843318
1990 1 -7.428853e-01 1.4278914745
1991 1 2.081491e-01 1.8593265851
1992 1 -8.529617e-01 -0.3979564585
1993 1 9.235143e-01 0.4129440992
1994 1 1.354395e+00 -0.2755167794
1995 1 5.697907e-01 0.8813467819
1996 1 -2.468826e-02 1.6829380542
1997 1 6.948899e-01 0.6945449748
1998 1 -7.752261e-02 0.5096265680
1999 1 4.085734e-01 0.4243439554
2000 1 -1.587949e+00 0.0181455085
attr(,"assign")
[1] 0 1 2
$calc_lik
function (beta)
{
for (n in names(obj$model$submods)) assign(n, eval(get(n)))
p <- eval(obj$model$boolmod)
log.p <- log(p)
log.p1 <- log(1 - p)
eval(llik)
}
<bytecode: 0x5651a922d690>
<environment: 0x5651a8f58740>
$method
[1] "nlm" "nlminb"
$model.fit
$nlm
$nlm$par
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlm -1.962176 0.2541561 0.6954833 0.9972418 0.006893525 1.702574
x5
nlm -0.197018
$nlm$nhatend
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 115.456997 31.281691 63.6547636 100.2494259 95.25477 -21.8660312
[2,] 31.281691 117.943969 1.8807256 19.3256601 10.65402 -5.9115476
[3,] 63.654764 1.880726 134.6344701 31.1731459 39.15750 -0.5340373
[4,] 100.249426 19.325660 31.1731459 145.7169853 55.20325 -7.2822927
[5,] 95.254774 10.654019 39.1575019 55.2032487 573.49187 37.1083998
[6,] -21.866031 -5.911548 -0.5340373 -7.2822927 37.10840 171.9631686
[7,] -2.522996 -7.597149 -2.7947502 0.8884992 10.58570 38.7618385
[,7]
[1,] -2.5229956
[2,] -7.5971495
[3,] -2.7947502
[4,] 0.8884992
[5,] 10.5857040
[6,] 38.7618385
[7,] 559.8836759
$nlm$value
[1] -771.6595
$nlm$iterations
[1] 24
$nlm$conv
[1] 0
$nlm$detail
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlm -1.962176 0.2541561 0.6954833 0.9972418 0.006893525 1.702574
nlminb -1.962174 0.2541573 0.6954825 0.9972405 0.006893159 1.702572
x5 value fevals gevals niter convcode kkt1 kkt2 xtimes
nlm -0.1970180 771.6595 NA NA 24 0 TRUE TRUE 0.247
nlminb -0.1970193 771.6595 54 408 42 0 TRUE TRUE 0.534
$nlminb
$nlminb$par
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlminb -1.962174 0.2541573 0.6954825 0.9972405 0.006893159 1.702572
x5
nlminb -0.1970193
$nlminb$nhatend
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 115.457214 31.281924 63.6547716 100.2494647 95.25488 -21.8659765
[2,] 31.281924 117.944232 1.8807743 19.3257198 10.65405 -5.9115098
[3,] 63.654772 1.880774 134.6346070 31.1730783 39.15753 -0.5340214
[4,] 100.249465 19.325720 31.1730783 145.7170857 55.20325 -7.2822566
[5,] 95.254883 10.654048 39.1575254 55.2032529 573.49201 37.1084645
[6,] -21.865976 -5.911510 -0.5340214 -7.2822566 37.10846 171.9634863
[7,] -2.522868 -7.597161 -2.7946852 0.8885202 10.58556 38.7621375
[,7]
[1,] -2.5228681
[2,] -7.5971613
[3,] -2.7946852
[4,] 0.8885202
[5,] 10.5855587
[6,] 38.7621375
[7,] 559.8837337
$nlminb$value
[1] -771.6595
$nlminb$iterations
[1] 42
$nlminb$conv
[1] 0
$nlminb$detail
X.Intercept. x1 x2 x3 X.Intercept..1 x4
nlm -1.962176 0.2541561 0.6954833 0.9972418 0.006893525 1.702574
nlminb -1.962174 0.2541573 0.6954825 0.9972405 0.006893159 1.702572
x5 value fevals gevals niter convcode kkt1 kkt2 xtimes
nlm -0.1970180 771.6595 NA NA 24 0 TRUE TRUE 0.247
nlminb -0.1970193 771.6595 54 408 42 0 TRUE TRUE 0.534
attr(,"class")
[1] "boolfit" "list"
attr(,"class")
[1] "boolean"
>
> ## Re-fit, with BFGS and a higher maximum number of iterations. All
> ## of the options that go along with nlm(), optim(), and genoud() should
> ## be transparently passable through boolean.
> (fit2.OR <- boolean(mod.OR, method="BFGS", control = list(maxit = 500)))
$call
boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5, data = Df,
family = list(binomial("probit")))
$link
$link$a
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$link$b
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$model
$model$submods
$model$submods$a
[1] "x1" "x2" "x3"
$model$submods$b
[1] "x4" "x5"
$model$boolmod
expression((a %|% b))
$N
[1] 2000
$k
[1] 7
$coef.labels
[1] "(Intercept)_a" "x1_a" "x2_a" "x3_a"
[5] "(Intercept)_b" "x4_b" "x5_b"
$coef.idx
$coef.idx$a
[1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE
$coef.idx$b
[1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE
$response
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
1 1 1 0 0 0 1 1 1 0 0 1 0 1 0 1
33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
0 1 1 1 1 0 0 1 1 1 1 0 0 1 0 1
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1
65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
1 1 1 1 1 0 0 1 1 1 1 1 0 1 0 0
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0
97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
1 1 0 1 0 0 0 0 0 0 1 1 1 0 1 1
113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
1 0 0 0 0 1 0 1 0 1 1 1 0 0 1 1
129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1
145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
0 1 1 0 0 1 0 1 1 1 0 1 1 1 0 1
161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1
177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
1 1 0 1 1 0 1 1 1 1 1 0 0 0 0 0
193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
1 0 1 1 1 0 0 0 0 1 0 1 1 1 0 1
209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
0 1 0 0 1 0 1 1 1 1 1 0 1 0 1 1
225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0
241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
1 1 1 1 0 0 1 0 1 0 0 0 0 0 1 0
257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
1 0 1 1 0 1 0 1 1 0 1 0 1 0 1 0
273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1
289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304
1 1 0 0 1 1 1 1 0 1 0 0 1 1 1 0
305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320
1 1 1 0 0 1 0 0 0 0 0 0 1 1 0 1
321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
0 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1
337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352
0 1 0 1 1 0 1 1 1 1 0 1 1 1 1 0
353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
1 1 1 1 1 1 1 0 1 0 1 1 0 0 0 0
369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384
1 0 0 0 1 1 0 1 0 0 1 1 1 0 1 0
385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1
401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416
1 0 0 1 1 1 1 1 0 0 1 1 1 0 1 1
417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1
433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448
1 0 0 1 1 1 1 0 1 1 0 0 1 0 1 1
449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464
1 1 0 1 1 0 1 1 0 0 1 1 1 0 1 0
465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 1
481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
1 0 1 1 1 1 0 0 1 0 1 0 0 0 0 1
497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
0 0 1 1 0 0 0 0 1 1 0 0 1 1 1 1
513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
1 0 1 1 0 1 0 0 1 1 0 0 0 0 1 0
529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
0 1 0 1 0 0 0 1 1 0 1 1 1 1 0 1
545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560
1 1 1 0 1 1 0 1 1 1 1 1 0 0 0 1
561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
1 1 1 1 0 1 1 0 1 0 1 0 1 0 0 0
577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592
1 1 1 0 1 0 1 0 1 1 0 1 1 1 0 0
593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608
1 1 1 0 0 0 1 1 0 0 1 1 0 1 0 0
609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
1 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0
625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640
0 1 0 1 1 0 1 1 0 1 0 0 0 1 0 1
641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1
657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672
1 1 1 0 1 1 0 0 0 1 0 0 0 0 1 1
673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
0 0 1 0 1 1 1 0 1 1 0 1 1 0 0 0
689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704
0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720
0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1
721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
1 1 0 1 1 1 1 1 0 0 0 1 1 0 1 1
737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752
0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 1
753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
1 0 1 1 0 1 1 1 0 1 1 1 0 0 0 0
769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784
0 0 0 1 1 1 0 0 0 1 1 1 1 0 0 0
785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
1 0 1 1 0 1 1 1 0 1 1 1 0 0 1 0
801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
0 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1
817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 0
833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848
1 1 1 0 0 0 1 0 1 0 1 0 0 0 1 0
849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864
1 1 1 1 0 1 0 0 1 1 1 1 0 0 0 0
865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880
0 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1
881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896
0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1
897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912
0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 1
913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
0 0 0 1 1 0 1 1 0 0 0 1 0 0 1 1
929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944
0 1 1 1 0 1 0 1 0 1 1 0 1 1 1 1
945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0
961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 0
977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
0 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1
1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
1 0 0 1 1 1 1 0 1 1 0 1 1 1 1 0
1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 1
1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
0 0 0 1 1 0 1 1 1 0 0 1 1 0 0 1
1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
0 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0
1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088
0 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1
1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
1 0 0 0 1 1 1 1 0 1 0 1 1 1 1 0
1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
0 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0
1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
1 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0
1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 0
1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168
0 0 1 0 1 1 1 1 1 0 0 1 1 1 0 0
1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
0 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1
1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0
1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216
1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1
1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 1
1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
0 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0
1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
0 0 1 0 1 0 1 1 0 0 1 0 1 1 0 1
1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
0 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1
1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
0 1 1 0 1 1 0 1 1 0 0 1 1 0 1 1
1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
0 1 1 1 0 0 0 0 0 1 1 0 1 0 1 0
1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0
1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
0 1 0 0 0 0 1 1 0 1 1 0 1 0 0 0
1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 1
1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
1 0 1 1 0 0 1 0 1 1 0 0 0 0 0 1
1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
1 0 0 0 1 1 0 0 1 0 1 1 1 1 0 0
1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
1 0 0 1 1 0 1 0 0 1 0 1 0 0 1 0
1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
1 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0
1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
1 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0
1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456
1 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1
1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472
1 1 1 1 1 0 0 1 0 1 0 1 1 1 0 0
1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488
0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1
1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1
1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520
1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1
1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
1 0 0 1 1 0 0 1 0 0 1 1 1 1 1 0
1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552
1 0 1 1 1 1 1 0 1 0 0 1 1 1 0 0
1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568
0 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0
1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584
0 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1
1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600
1 1 1 1 0 1 0 0 0 0 1 0 1 1 0 1
1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
0 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1
1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632
0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 1
1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648
1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1
1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664
1 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0
1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680
1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0
1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696
1 0 1 1 0 0 1 1 0 0 1 1 1 1 1 1
1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712
1 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0
1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1
1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744
0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0
1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760
0 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1
1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776
0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 1
1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792
1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1
1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808
1 1 1 0 1 0 0 0 1 1 1 0 0 1 0 0
1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824
0 1 1 0 0 1 0 0 1 1 1 1 0 1 1 1
1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840
1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0
1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856
0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 0
1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872
0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 0
1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888
0 1 0 0 1 0 0 0 1 1 0 1 1 1 0 1
1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904
0 0 0 1 1 1 1 1 1 1 0 0 0 1 0 0
1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 0
1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936
1 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0
1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952
1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1
1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968
1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 0
1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
1 1 1 0 1 1 0 1 0 0 0 1 0 1 1 0
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1
$frame
$frame$a
(Intercept) x1 x2 x3
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3 1 -0.1162478064 1.8173120437 0.3706278643
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66 1 0.9331921670 0.4754354212 0.3902816000
67 1 1.4239835987 0.4847305112 0.3492364459
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1956 1 -1.134532e+00 -0.7568702574
1957 1 -2.730316e+00 0.7899478570
1958 1 6.522059e-01 -0.7758246883
1959 1 -9.077401e-01 1.7400984741
1960 1 -1.746243e-01 0.7273764157
1961 1 -1.749175e+00 0.0412251647
1962 1 6.351639e-01 -0.7465530475
1963 1 3.928764e-01 -0.4669105152
1964 1 1.469288e-01 0.5619024171
1965 1 -4.416916e-02 0.9213398038
1966 1 5.095332e-01 1.3139610919
1967 1 1.036623e+00 -0.6321055477
1968 1 -1.017870e+00 1.1922587748
1969 1 1.540566e+00 -0.2953318428
1970 1 1.905289e+00 -0.7622792482
1971 1 4.042105e-01 0.5947329044
1972 1 -7.069767e-01 1.5472486383
1973 1 -1.008285e+00 0.6816846166
1974 1 4.863330e-01 -0.0116558350
1975 1 -7.022979e-01 0.7742367691
1976 1 3.093437e-01 0.2707596966
1977 1 -1.078647e+00 -0.1883596530
1978 1 5.902743e-01 0.6246672753
1979 1 6.584944e-01 1.4193192881
1980 1 2.048185e+00 -0.9067758246
1981 1 -1.448530e+00 -0.3987914948
1982 1 1.267581e+00 1.4135953220
1983 1 -6.959804e-01 0.2584286770
1984 1 7.405956e-02 -1.2725183814
1985 1 4.446582e-02 -0.0753325430
1986 1 -4.317039e-01 0.0703880778
1987 1 1.688329e+00 -0.7760323143
1988 1 1.932779e-01 -1.6246009465
1989 1 2.666542e-01 0.8940843318
1990 1 -7.428853e-01 1.4278914745
1991 1 2.081491e-01 1.8593265851
1992 1 -8.529617e-01 -0.3979564585
1993 1 9.235143e-01 0.4129440992
1994 1 1.354395e+00 -0.2755167794
1995 1 5.697907e-01 0.8813467819
1996 1 -2.468826e-02 1.6829380542
1997 1 6.948899e-01 0.6945449748
1998 1 -7.752261e-02 0.5096265680
1999 1 4.085734e-01 0.4243439554
2000 1 -1.587949e+00 0.0181455085
attr(,"assign")
[1] 0 1 2
$calc_lik
function (beta)
{
for (n in names(obj$model$submods)) assign(n, eval(get(n)))
p <- eval(obj$model$boolmod)
log.p <- log(p)
log.p1 <- log(1 - p)
eval(llik)
}
<bytecode: 0x5651a922d690>
<environment: 0x5651a8f58740>
$method
[1] "BFGS"
$model.fit
$BFGS
$BFGS$par
X.Intercept. x1 x2 x3 X.Intercept..1 x4 x5
BFGS -1.96264 0.25421 0.6956157 0.9975053 0.006945397 1.70257 -0.1970051
$BFGS$nhatend
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 115.415456 31.274778 63.6456959 100.237354 95.23245 -21.8675831
[2,] 31.274778 117.904100 1.8828406 19.327963 10.65650 -5.9135497
[3,] 63.645696 1.882841 134.5975938 31.183303 39.15290 -0.5354693
[4,] 100.237354 19.327963 31.1833027 145.693121 55.20376 -7.2869532
[5,] 95.232446 10.656496 39.1529009 55.203757 573.52298 37.0939880
[6,] -21.867583 -5.913550 -0.5354693 -7.286953 37.09399 171.9705056
[7,] -2.524958 -7.593851 -2.7941225 0.889493 10.58814 38.7623054
[,7]
[1,] -2.524958
[2,] -7.593851
[3,] -2.794123
[4,] 0.889493
[5,] 10.588142
[6,] 38.762305
[7,] 559.921821
$BFGS$value
[1] -771.6595
$BFGS$iterations
[1] NA
$BFGS$conv
[1] 0
$BFGS$detail
X.Intercept. x1 x2 x3 X.Intercept..1 x4 x5
BFGS -1.96264 0.25421 0.6956157 0.9975053 0.006945397 1.70257 -0.1970051
value fevals gevals niter convcode kkt1 kkt2 xtimes
BFGS 771.6595 58 18 NA 0 TRUE TRUE 0.409
attr(,"class")
[1] "boolfit" "list"
attr(,"class")
[1] "boolean"
>
> ## Induce a convergence warning message.
> (fit3.OR <- boolean(mod.OR, method="BFGS", control = list(maxit = 5)))
$call
boolprep(y.OR ~ (a | b), a ~ x1 + x2 + x3, b ~ x4 + x5, data = Df,
family = list(binomial("probit")))
$link
$link$a
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$link$b
$linkfun
function (mu)
qnorm(mu)
<environment: namespace:stats>
$linkinv
function (eta)
{
thresh <- -qnorm(.Machine$double.eps)
eta <- pmin(pmax(eta, -thresh), thresh)
pnorm(eta)
}
<environment: namespace:stats>
$mu.eta
function (eta)
pmax(dnorm(eta), .Machine$double.eps)
<environment: namespace:stats>
$valideta
function (eta)
TRUE
<environment: namespace:stats>
$name
[1] "probit"
attr(,"class")
[1] "link-glm"
$model
$model$submods
$model$submods$a
[1] "x1" "x2" "x3"
$model$submods$b
[1] "x4" "x5"
$model$boolmod
expression((a %|% b))
$N
[1] 2000
$k
[1] 7
$coef.labels
[1] "(Intercept)_a" "x1_a" "x2_a" "x3_a"
[5] "(Intercept)_b" "x4_b" "x5_b"
$coef.idx
$coef.idx$a
[1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE
$coef.idx$b
[1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE
$response
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
1 1 1 0 0 0 1 1 1 0 0 1 0 1 0 1
33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
0 1 1 1 1 0 0 1 1 1 1 0 0 1 0 1
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1
65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
1 1 1 1 1 0 0 1 1 1 1 1 0 1 0 0
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0
97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
1 1 0 1 0 0 0 0 0 0 1 1 1 0 1 1
113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
1 0 0 0 0 1 0 1 0 1 1 1 0 0 1 1
129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1
145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
0 1 1 0 0 1 0 1 1 1 0 1 1 1 0 1
161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1
177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
1 1 0 1 1 0 1 1 1 1 1 0 0 0 0 0
193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
1 0 1 1 1 0 0 0 0 1 0 1 1 1 0 1
209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
0 1 0 0 1 0 1 1 1 1 1 0 1 0 1 1
225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0
241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
1 1 1 1 0 0 1 0 1 0 0 0 0 0 1 0
257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
1 0 1 1 0 1 0 1 1 0 1 0 1 0 1 0
273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1
289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304
1 1 0 0 1 1 1 1 0 1 0 0 1 1 1 0
305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320
1 1 1 0 0 1 0 0 0 0 0 0 1 1 0 1
321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
0 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1
337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352
0 1 0 1 1 0 1 1 1 1 0 1 1 1 1 0
353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
1 1 1 1 1 1 1 0 1 0 1 1 0 0 0 0
369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384
1 0 0 0 1 1 0 1 0 0 1 1 1 0 1 0
385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1
401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416
1 0 0 1 1 1 1 1 0 0 1 1 1 0 1 1
417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1
433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448
1 0 0 1 1 1 1 0 1 1 0 0 1 0 1 1
449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464
1 1 0 1 1 0 1 1 0 0 1 1 1 0 1 0
465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 1
481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
1 0 1 1 1 1 0 0 1 0 1 0 0 0 0 1
497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
0 0 1 1 0 0 0 0 1 1 0 0 1 1 1 1
513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
1 0 1 1 0 1 0 0 1 1 0 0 0 0 1 0
529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
0 1 0 1 0 0 0 1 1 0 1 1 1 1 0 1
545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560
1 1 1 0 1 1 0 1 1 1 1 1 0 0 0 1
561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
1 1 1 1 0 1 1 0 1 0 1 0 1 0 0 0
577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592
1 1 1 0 1 0 1 0 1 1 0 1 1 1 0 0
593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608
1 1 1 0 0 0 1 1 0 0 1 1 0 1 0 0
609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
1 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0
625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640
0 1 0 1 1 0 1 1 0 1 0 0 0 1 0 1
641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1
657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672
1 1 1 0 1 1 0 0 0 1 0 0 0 0 1 1
673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
0 0 1 0 1 1 1 0 1 1 0 1 1 0 0 0
689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704
0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720
0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1
721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
1 1 0 1 1 1 1 1 0 0 0 1 1 0 1 1
737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752
0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 1
753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
1 0 1 1 0 1 1 1 0 1 1 1 0 0 0 0
769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784
0 0 0 1 1 1 0 0 0 1 1 1 1 0 0 0
785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
1 0 1 1 0 1 1 1 0 1 1 1 0 0 1 0
801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
0 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1
817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 0
833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848
1 1 1 0 0 0 1 0 1 0 1 0 0 0 1 0
849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864
1 1 1 1 0 1 0 0 1 1 1 1 0 0 0 0
865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880
0 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1
881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896
0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1
897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912
0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 1
913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
0 0 0 1 1 0 1 1 0 0 0 1 0 0 1 1
929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944
0 1 1 1 0 1 0 1 0 1 1 0 1 1 1 1
945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0
961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 0
977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
0 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1
1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
1 0 0 1 1 1 1 0 1 1 0 1 1 1 1 0
1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 1
1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
0 0 0 1 1 0 1 1 1 0 0 1 1 0 0 1
1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
0 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0
1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088
0 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1
1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
1 0 0 0 1 1 1 1 0 1 0 1 1 1 1 0
1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
0 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0
1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
1 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0
1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 0
1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168
0 0 1 0 1 1 1 1 1 0 0 1 1 1 0 0
1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
0 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1
1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0
1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216
1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1
1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 1
1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
0 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0
1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
0 0 1 0 1 0 1 1 0 0 1 0 1 1 0 1
1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
0 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1
1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
0 1 1 0 1 1 0 1 1 0 0 1 1 0 1 1
1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
0 1 1 1 0 0 0 0 0 1 1 0 1 0 1 0
1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0
1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
0 1 0 0 0 0 1 1 0 1 1 0 1 0 0 0
1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 1
1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
1 0 1 1 0 0 1 0 1 1 0 0 0 0 0 1
1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
1 0 0 0 1 1 0 0 1 0 1 1 1 1 0 0
1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
1 0 0 1 1 0 1 0 0 1 0 1 0 0 1 0
1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
1 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0
1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
1 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0
1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456
1 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1
1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472
1 1 1 1 1 0 0 1 0 1 0 1 1 1 0 0
1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488
0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1
1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1
1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520
1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1
1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
1 0 0 1 1 0 0 1 0 0 1 1 1 1 1 0
1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552
1 0 1 1 1 1 1 0 1 0 0 1 1 1 0 0
1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568
0 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0
1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584
0 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1
1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600
1 1 1 1 0 1 0 0 0 0 1 0 1 1 0 1
1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
0 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1
1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632
0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 1
1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648
1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1
1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664
1 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0
1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680
1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0
1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696
1 0 1 1 0 0 1 1 0 0 1 1 1 1 1 1
1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712
1 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0
1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1
1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744
0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0
1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760
0 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1
1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776
0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 1
1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792
1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1
1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808
1 1 1 0 1 0 0 0 1 1 1 0 0 1 0 0
1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824
0 1 1 0 0 1 0 0 1 1 1 1 0 1 1 1
1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840
1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0
1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856
0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 0
1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872
0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 0
1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888
0 1 0 0 1 0 0 0 1 1 0 1 1 1 0 1
1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904
0 0 0 1 1 1 1 1 1 1 0 0 0 1 0 0
1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 0
1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936
1 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0
1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952
1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1
1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968
1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 0
1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
1 1 1 0 1 1 0 1 0 0 0 1 0 1 1 0
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1
$frame
$frame$a
(Intercept) x1 x2 x3
1 1 0.5855288178 0.7094660175 -0.1093033147
2 1 -1.8179559677 0.6300985511 -0.2761841052
3 1 -0.1162478064 1.8173120437 0.3706278643
4 1 0.8168998395 -0.8863575212 -0.3315775899
5 1 0.7796219246 1.4557850825 -0.6443284292
6 1 1.8050975188 -0.4816473637 0.6203798013
7 1 0.8118731786 2.1968335463 2.0491903374
8 1 0.4911882793 -0.3240865787 -1.6620502439
9 1 1.1285108336 -2.3803580614 -1.0602655522
10 1 1.4607294031 -1.4130987779 0.5674032534
11 1 -0.5403860739 1.9476926647 0.0535902704
12 1 0.2779536948 0.6911712729 0.8237953287
13 1 0.1495919806 -1.3425314817 0.5533030755
14 1 -1.8323773062 0.8881394327 1.5934884724
15 1 0.0546155753 -0.7846493732 -1.0493528190
16 1 0.9426008507 0.8262582872 -0.8115404902
17 1 0.6453830742 1.0431435516 -0.3043691127
18 1 1.8670991846 0.6720424690 -0.3079533808
19 1 -0.9639014790 -0.8550825111 1.8869469420
20 1 0.6873321006 -0.5050435175 2.1577198165
21 1 0.2239254075 -1.1562233302 0.4224185278
22 1 -0.5360479985 -0.3116060846 1.5561096426
23 1 -1.2301722469 -1.3240586922 1.2612422744
24 1 -0.5050898063 -0.0521535933 0.6288606251
25 1 1.5448636020 1.3214520172 0.3221515754
26 1 -1.1588210228 -1.8453682894 1.1573252871
27 1 1.6421919908 0.8836548332 0.5248758910
28 1 -1.0479137102 -1.0111225236 0.6689216525
29 1 -1.1402641448 -1.2937152900 -0.5946987691
30 1 0.5401695677 -1.5472919670 0.8496529299
31 1 -1.6193283224 0.5483979230 0.1952821538
32 1 -0.2509466239 1.6993466685 -0.3442987963
33 1 -0.4876385152 0.3031512373 -0.2419740196
34 1 -0.2806491388 0.6330173442 -1.2398183431
35 1 0.1999204838 1.3471927776 0.0360734878
36 1 0.4809501553 2.4835500893 0.4013649875
37 1 -0.9117394242 -0.0490446909 -0.4053874767
38 1 0.0764175190 1.4537473512 0.3741210834
39 1 0.5435221079 -0.5051860029 0.7867957894
40 1 0.7984337703 0.8508604376 -0.4435679681
41 1 -1.4361457062 -0.6292596473 0.2435217656
42 1 0.1052118160 -1.7417130945 0.6452469938
43 1 0.9919506777 -0.8592507612 -0.2815796883
44 1 0.3156128247 0.6602933781 -1.7222024071
45 1 0.8678217426 -2.2900441795 -0.1501902882
46 1 0.6722680417 -0.2093011363 0.0121825061
47 1 0.0784375327 -0.7792610704 0.1665596687
48 1 -0.4678883695 0.7583745568 -0.6417363578
49 1 -0.7000758167 -0.5674015851 -0.2613939259
50 1 0.7711037397 2.7474035422 -0.0839347571
51 1 -0.8086741277 1.0011198474 0.4560525046
52 1 0.6417691636 -0.4150210287 -0.4595756762
53 1 0.7108899984 1.2676017522 -0.1431510558
54 1 -0.1625889099 0.0417091744 0.4830398961
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2000 1 1.3895910200 1.5576396694 0.8287567177
attr(,"assign")
[1] 0 1 2 3
$frame$b
(Intercept) x4 x5
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3 1 5.202165e-01 -0.7505319945
4 1 1.120713e+00 0.2987236993
5 1 -1.553137e+00 -1.5977095167
6 1 6.121235e-01 -0.1623109769
7 1 1.632446e+00 0.2542711928
8 1 1.767734e+00 0.0258010486
9 1 9.371405e-01 0.8544517203
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11 1 3.516628e-01 -0.6709765390
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16 1 4.762483e-01 1.0212584075
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1883 1 -8.643900e-01 -0.2807471520
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1885 1 1.321946e+00 -0.2270587191
1886 1 1.275346e+00 0.0060096421
1887 1 -1.461178e+00 1.4219869387
1888 1 4.263679e-01 -0.6560333749
1889 1 1.069635e-01 0.7154114440
1890 1 3.954492e-01 -1.2810537354
1891 1 -5.094093e-01 0.2116882009
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1893 1 6.110963e-01 -0.7819688049
1894 1 8.737319e-01 2.1829251895
1895 1 -1.470085e+00 0.5001709711
1896 1 -1.193908e-01 0.7720357885
1897 1 1.441880e+00 2.6635069516
1898 1 9.807212e-01 -0.1918281006
1899 1 -1.549435e-01 -0.0477416512
1900 1 -6.565341e-01 -0.7303512121
1901 1 -2.218050e+00 -0.1248124005
1902 1 1.565099e+00 -1.2543759396
1903 1 -6.645336e-01 -0.0018166585
1904 1 -1.106672e+00 0.0184418854
1905 1 8.962007e-01 -1.5790753894
1906 1 -4.423003e-01 1.7012976526
1907 1 -5.403642e-03 -0.4247147067
1908 1 -1.067551e+00 -0.5569274770
1909 1 -6.025665e-01 -0.3705957323
1910 1 1.091899e+00 -0.2698253087
1911 1 6.321106e-01 -1.0712310012
1912 1 -6.891076e-02 1.5093042332
1913 1 -5.260184e-01 -0.2522409869
1914 1 -9.503961e-01 -1.1194321510
1915 1 1.429524e-01 0.0355176979
1916 1 -1.126099e+00 -0.7397448082
1917 1 2.747486e+00 -0.2091672198
1918 1 -4.562190e-01 -0.2211406219
1919 1 -1.615861e+00 -0.1960416425
1920 1 4.395728e-01 0.0851074619
1921 1 2.996376e-01 -0.1635599861
1922 1 1.142633e+00 1.2442966123
1923 1 -1.601222e+00 -0.8298963397
1924 1 -1.514884e+00 -0.9578456729
1925 1 -8.502370e-01 -0.3434408738
1926 1 -4.536467e-01 -0.9954231651
1927 1 7.029974e-02 0.3539936878
1928 1 -1.713754e-01 -1.8076249912
1929 1 1.063081e+00 -0.5676861874
1930 1 -2.771234e-01 0.0987148898
1931 1 -2.123883e-01 -0.7708787733
1932 1 1.524831e+00 -0.3909979096
1933 1 1.754834e-01 -0.3555536583
1934 1 -1.893939e+00 -0.1499947922
1935 1 -7.669144e-01 1.3458038956
1936 1 -2.528206e-01 1.3359909556
1937 1 8.316751e-01 0.3266016616
1938 1 1.010643e+00 -1.0517406052
1939 1 1.796983e+00 -0.5284336240
1940 1 7.229651e-01 -1.1143800298
1941 1 4.178250e-01 -0.0049648993
1942 1 1.196199e-01 0.4954238593
1943 1 1.825934e+00 1.9632261086
1944 1 -1.567389e+00 0.0977832811
1945 1 2.665720e-01 -1.3769637483
1946 1 -2.851051e-02 -0.1787249146
1947 1 7.168300e-01 2.4089706463
1948 1 9.548940e-02 1.1836679379
1949 1 -1.699918e-01 -0.1629387394
1950 1 7.732266e-01 0.4788108005
1951 1 1.055910e+00 1.4768883954
1952 1 1.301988e+00 1.2483121552
1953 1 -5.629535e-01 1.8736015682
1954 1 -1.046239e+00 -1.4697312412
1955 1 7.296283e-01 0.3759873430
1956 1 -1.134532e+00 -0.7568702574
1957 1 -2.730316e+00 0.7899478570
1958 1 6.522059e-01 -0.7758246883
1959 1 -9.077401e-01 1.7400984741
1960 1 -1.746243e-01 0.7273764157
1961 1 -1.749175e+00 0.0412251647
1962 1 6.351639e-01 -0.7465530475
1963 1 3.928764e-01 -0.4669105152
1964 1 1.469288e-01 0.5619024171
1965 1 -4.416916e-02 0.9213398038
1966 1 5.095332e-01 1.3139610919
1967 1 1.036623e+00 -0.6321055477
1968 1 -1.017870e+00 1.1922587748
1969 1 1.540566e+00 -0.2953318428
1970 1 1.905289e+00 -0.7622792482
1971 1 4.042105e-01 0.5947329044
1972 1 -7.069767e-01 1.5472486383
1973 1 -1.008285e+00 0.6816846166
1974 1 4.863330e-01 -0.0116558350
1975 1 -7.022979e-01 0.7742367691
1976 1 3.093437e-01 0.2707596966
1977 1 -1.078647e+00 -0.1883596530
1978 1 5.902743e-01 0.6246672753
1979 1 6.584944e-01 1.4193192881
1980 1 2.048185e+00 -0.9067758246
1981 1 -1.448530e+00 -0.3987914948
1982 1 1.267581e+00 1.4135953220
1983 1 -6.959804e-01 0.2584286770
1984 1 7.405956e-02 -1.2725183814
1985 1 4.446582e-02 -0.0753325430
1986 1 -4.317039e-01 0.0703880778
1987 1 1.688329e+00 -0.7760323143
1988 1 1.932779e-01 -1.6246009465
1989 1 2.666542e-01 0.8940843318
1990 1 -7.428853e-01 1.4278914745
1991 1 2.081491e-01 1.8593265851
1992 1 -8.529617e-01 -0.3979564585
1993 1 9.235143e-01 0.4129440992
1994 1 1.354395e+00 -0.2755167794
1995 1 5.697907e-01 0.8813467819
1996 1 -2.468826e-02 1.6829380542
1997 1 6.948899e-01 0.6945449748
1998 1 -7.752261e-02 0.5096265680
1999 1 4.085734e-01 0.4243439554
2000 1 -1.587949e+00 0.0181455085
attr(,"assign")
[1] 0 1 2
$calc_lik
function (beta)
{
for (n in names(obj$model$submods)) assign(n, eval(get(n)))
p <- eval(obj$model$boolmod)
log.p <- log(p)
log.p1 <- log(1 - p)
eval(llik)
}
<bytecode: 0x5651a922d690>
<environment: 0x5651a8f58740>
$method
[1] "BFGS"
$model.fit
$BFGS
$BFGS$par
X.Intercept. x1 x2 x3 X.Intercept..1 x4
BFGS -1.55604 0.1957866 0.6223986 0.7455059 -0.04884058 1.311676
x5
BFGS -0.2085441
$BFGS$nhatend
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 152.724870 30.350731 78.5240869 95.7768127 127.11743 -10.971630
[2,] 30.350731 156.269234 -1.5652812 14.0418127 10.66959 -4.423217
[3,] 78.524087 -1.565281 172.8665894 21.9240707 48.76609 3.315847
[4,] 95.776813 14.041813 21.9240707 158.7687197 61.88226 -3.110111
[5,] 127.117432 10.669594 48.7660940 61.8822613 633.64449 49.401769
[6,] -10.971630 -4.423217 3.3158474 -3.1101106 49.40177 268.091052
[7,] 4.493651 -6.401603 -0.9885231 0.5891863 3.17210 51.520332
[,7]
[1,] 4.4936515
[2,] -6.4016028
[3,] -0.9885231
[4,] 0.5891863
[5,] 3.1720999
[6,] 51.5203315
[7,] 611.6443925
$BFGS$value
[1] -793.5796
$BFGS$iterations
[1] NA
$BFGS$conv
[1] 1
$BFGS$detail
X.Intercept. x1 x2 x3 X.Intercept..1 x4
BFGS -1.55604 0.1957866 0.6223986 0.7455059 -0.04884058 1.311676
x5 value fevals gevals niter convcode kkt1 kkt2 xtimes
BFGS -0.2085441 793.5796 19 5 NA 1 FALSE TRUE 0.151
attr(,"class")
[1] "boolfit" "list"
attr(,"class")
[1] "boolean"
>
> ## Not run:
> ##D ## Now estimate model with genoud, a genetic optimizer. This has the
> ##D ## capability of using multiple processors via parallel.
> ##D (fit6.OR <- boolean(mod.OR, method="genoud",
> ##D cluster=c("localhost", "localhost"),
> ##D print.level=2))
> ##D
> ##D ## The default SANN optimizer is also available.
> ##D (fit7.OR <- boolean(mod.OR, method="SANN"))
> ## End(Not run)
>
> ## The fit is stored as "model.fit", within the boolean object.
> str(fit.OR$model.fit)
List of 1
$ nlminb:List of 6
..$ par : num [1, 1:7] -1.96217 0.25416 0.69548 0.99724 0.00689 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr "nlminb"
.. .. ..$ : chr [1:7] "X.Intercept." "x1" "x2" "x3" ...
..$ nhatend : num [1:7, 1:7] 115.5 31.3 63.7 100.2 95.3 ...
..$ value : num -772
..$ iterations: num 42
..$ conv : num 0
..$ detail :Classes ‘optimx’ and 'data.frame': 1 obs. of 15 variables:
.. ..$ X.Intercept. : num -1.96
.. ..$ x1 : num 0.254
.. ..$ x2 : num 0.695
.. ..$ x3 : num 0.997
.. ..$ X.Intercept..1: num 0.00689
.. ..$ x4 : num 1.7
.. ..$ x5 : num -0.197
.. ..$ value : num 772
.. ..$ fevals : num 54
.. ..$ gevals : num 408
.. ..$ niter : num 42
.. ..$ convcode : num 0
.. ..$ kkt1 : logi TRUE
.. ..$ kkt2 : logi TRUE
.. ..$ xtimes : num 0.539
.. ..- attr(*, "details")=List of 5
.. .. ..$ : chr "nlminb"
.. .. ..$ : num [1:7] 1.23e-05 1.32e-04 -4.30e-05 -1.06e-04 -4.30e-04 ...
.. .. ..$ : num [1:7, 1:7] 115.5 31.3 63.7 100.2 95.3 ...
.. .. ..$ : num [1:7] 617 562 246 158 121 ...
.. .. ..$ : chr "none"
.. .. ..- attr(*, "dim")= int [1:2] 1 5
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr "nlminb"
.. .. .. ..$ : chr [1:5] "method" "ngatend" "nhatend" "hev" ...
.. ..- attr(*, "maximize")= logi FALSE
.. ..- attr(*, "npar")= int 7
.. ..- attr(*, "follow.on")= logi FALSE
- attr(*, "class")= chr [1:2] "boolfit" "list"
>
> ## Create a summary object, saving and printing it. Then take a look at
> ## the objects stored in the summary object.
> (smry <- summary(fit.OR))
Length Class Mode
call 6 -none- call
link 2 -none- list
model 2 -none- list
N 1 -none- numeric
k 1 -none- numeric
coef.labels 7 -none- character
coef.idx 2 -none- list
response 2000 -none- numeric
frame 2 -none- list
calc_lik 1 -none- function
method 1 -none- character
model.fit 1 boolfit list
> str(smry)
'summaryDefault' chr [1:12, 1:3] " 6" " 2" " 2" " 1" " 1" " 7" ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:12] "call" "link" "model" "N" ...
..$ : chr [1:3] "Length" "Class" "Mode"
>
> ## Extract log-likelihood and coefficient vector.
> logLik(fit.OR)
Error in UseMethod("logLik") :
no applicable method for 'logLik' applied to an object of class "boolean"
Calls: logLik
Execution halted
Flavor: r-release-linux-x86_64