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> ### > attach(NULL, name = "CheckExEnv") > assign(".CheckExEnv", as.environment(2), pos = length(search())) # base > ## add some hooks to label plot pages for base and grid graphics > setHook("plot.new", ".newplot.hook") > setHook("persp", ".newplot.hook") > setHook("grid.newpage", ".gridplot.hook") > > assign("cleanEx", + function(env = .GlobalEnv) { + rm(list = ls(envir = env, all.names = TRUE), envir = env) + RNGkind("default", "default") + set.seed(1) + options(warn = 1) + delayedAssign("T", stop("T used instead of TRUE"), + assign.env = .CheckExEnv) + delayedAssign("F", stop("F used instead of FALSE"), + assign.env = .CheckExEnv) + sch <- search() + newitems <- sch[! sch %in% .oldSearch] + for(item in rev(newitems)) + eval(substitute(detach(item), list(item=item))) + missitems <- .oldSearch[! .oldSearch %in% sch] + if(length(missitems)) + warning("items ", paste(missitems, collapse=", "), + " have been removed from the search path") + }, + env = .CheckExEnv) > assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now > assign("ptime", proc.time(), env = .CheckExEnv) > grDevices::postscript("forward-Examples.ps") > assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv) > options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly")) > options(warn = 1) > library('forward') Loading required package: lqs Warning: package 'lqs' has been moved back to package 'MASS' Warning: package 'MASS' has now been loaded > > assign(".oldSearch", search(), env = .CheckExEnv) > assign(".oldNS", loadedNamespaces(), env = .CheckExEnv) > cleanEx(); ..nameEx <- "ar" > > ### * ar > > flush(stderr()); flush(stdout()) > > ### Name: ar > ### Title: ar data > ### Aliases: ar > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "bliss" > > ### * bliss > > flush(stderr()); flush(stdout()) > > ### Name: bliss > ### Title: Bliss data > ### Aliases: bliss > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "calcium" > > ### * calcium > > flush(stderr()); flush(stdout()) > > ### Name: calcium > ### Title: Calcium data > ### Aliases: calcium > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "carinsuk" > > ### * carinsuk > > flush(stderr()); flush(stdout()) > > ### Name: carinsuk > ### Title: Car insurance data > ### Aliases: carinsuk > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "carr" > > ### * carr > > flush(stderr()); flush(stdout()) > > ### Name: carr > ### Title: n-Pentane data > ### Aliases: carr > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "cellular" > > ### * cellular > > flush(stderr()); flush(stdout()) > > ### Name: cellular > ### Title: Cellular differentiation data > ### Aliases: cellular > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "chapman" > > ### * chapman > > flush(stderr()); flush(stdout()) > > ### Name: chapman > ### Title: Chapman data > ### Aliases: chapman > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "derailme" > > ### * derailme > > flush(stderr()); flush(stdout()) > > ### Name: derailme > ### Title: British Train Accidents. > ### Aliases: derailme > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "dialectric" > > ### * dialectric > > flush(stderr()); flush(stdout()) > > ### Name: dialectric > ### Title: Dialectric data > ### Aliases: dialectric > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "forbes" > > ### * forbes > > flush(stderr()); flush(stdout()) > > ### Name: forbes > ### Title: Forbes data > ### Aliases: forbes > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "fwd.combn" > > ### * fwd.combn > > flush(stderr()); flush(stdout()) > > ### Name: fwd.combn > ### Title: Generate all combinations of elements of x taken m at a time > ### Aliases: fwd.combn fwd.nCm > ### Keywords: math > > ### ** Examples > > fwd.combn(letters[1:4], 2) [,1] [,2] [,3] [,4] [,5] [,6] [1,] "a" "a" "a" "b" "b" "c" [2,] "b" "c" "d" "c" "d" "d" > fwd.combn(10, 5, min) # minimum value in each combination [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [186] 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 [223] 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 6 > # Different way of encoding points: > fwd.combn(c(1,1,1,1,2,2,2,3,3,4), 3, tabulate, nbins = 4) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [1,] 3 3 2 2 2 2 2 2 3 2 2 2 2 2 [2,] 0 0 1 1 1 0 0 0 0 1 1 1 0 0 [3,] 0 0 0 0 0 1 1 0 0 0 0 0 1 1 [4,] 0 0 0 0 0 0 0 1 0 0 0 0 0 0 [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26] [1,] 2 2 2 2 2 2 2 1 1 1 1 1 [2,] 0 1 1 1 0 0 0 2 2 1 1 1 [3,] 0 0 0 0 1 1 0 0 0 1 1 0 [4,] 1 0 0 0 0 0 1 0 0 0 0 1 [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38] [1,] 1 1 1 1 1 1 1 1 1 1 3 2 [2,] 2 1 1 1 1 1 1 0 0 0 0 1 [3,] 0 1 1 0 1 1 0 2 1 1 0 0 [4,] 0 0 0 1 0 0 1 0 1 1 0 0 [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50] [1,] 2 2 2 2 2 2 2 2 2 2 2 1 [2,] 1 1 0 0 0 1 1 1 0 0 0 2 [3,] 0 0 1 1 0 0 0 0 1 1 0 0 [4,] 0 0 0 0 1 0 0 0 0 0 1 0 [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61] [,62] [1,] 1 1 1 1 1 1 1 1 1 1 1 1 [2,] 2 1 1 1 2 1 1 1 1 1 1 0 [3,] 0 1 1 0 0 1 1 0 1 1 0 2 [4,] 0 0 0 1 0 0 0 1 0 0 1 0 [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73] [,74] [1,] 1 1 2 2 2 2 2 2 1 1 1 1 [2,] 0 0 1 1 1 0 0 0 2 2 1 1 [3,] 1 1 0 0 0 1 1 0 0 0 1 1 [4,] 1 1 0 0 0 0 0 1 0 0 0 0 [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85] [,86] [1,] 1 1 1 1 1 1 1 1 1 1 1 1 [2,] 1 2 1 1 1 1 1 1 0 0 0 2 [3,] 0 0 1 1 0 1 1 0 2 1 1 0 [4,] 1 0 0 0 1 0 0 1 0 1 1 0 [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96] [,97] [,98] [1,] 1 1 1 1 1 1 1 1 1 1 1 1 [2,] 2 1 1 1 2 1 1 1 1 1 1 0 [3,] 0 1 1 0 0 1 1 0 1 1 0 2 [4,] 0 0 0 1 0 0 0 1 0 0 1 0 [,99] [,100] [,101] [,102] [,103] [,104] [,105] [,106] [,107] [,108] [1,] 1 1 0 0 0 0 0 0 0 0 [2,] 0 0 3 2 2 2 2 2 2 1 [3,] 1 1 0 1 1 0 1 1 0 2 [4,] 1 1 0 0 0 1 0 0 1 0 [,109] [,110] [,111] [,112] [,113] [,114] [,115] [,116] [,117] [,118] [1,] 0 0 0 0 0 0 0 0 0 0 [2,] 1 1 2 2 2 1 1 1 1 1 [3,] 1 1 1 1 0 2 1 1 2 1 [4,] 1 1 0 0 1 0 1 1 0 1 [,119] [,120] [1,] 0 0 [2,] 1 0 [3,] 1 2 [4,] 1 1 > # Compute support points and (scaled) probabilities for a > # Multivariate-Hypergeometric(n = 3, N = c(4,3,2,1)) p.f.: > table(t(fwd.combn(c(1,1,1,1,2,2,2,3,3,4), 3, tabulate, nbins=4))) 0 1 2 3 195 215 65 5 > > > > cleanEx(); ..nameEx <- "fwdglm" > > ### * fwdglm > > flush(stderr()); flush(stdout()) > > ### Name: fwdglm > ### Title: Forward Search in Generalized Linear Models > ### Aliases: fwdglm print.fwdglm > ### Keywords: robust regression models > > ### ** Examples > > data(cellular) > cellular$TNF <- as.factor(cellular$TNF) > cellular$IFN <- as.factor(cellular$IFN) > mod <- fwdglm(y ~ TNF + IFN, data=cellular, family=poisson(log), nsamp=200) lmsglm: found 6 good subsets after trying 10. lmsglm: found 7 good subsets after trying 20. lmsglm: found 13 good subsets after trying 30. lmsglm: found 18 good subsets after trying 40. lmsglm: found 22 good subsets after trying 50. lmsglm: found 25 good subsets after trying 60. lmsglm: found 28 good subsets after trying 70. lmsglm: found 30 good subsets after trying 80. lmsglm: found 33 good subsets after trying 90. lmsglm: found 38 good subsets after trying 100. lmsglm: found 46 good subsets after trying 110. lmsglm: found 48 good subsets after trying 120. lmsglm: found 52 good subsets after trying 130. lmsglm: found 56 good subsets after trying 140. lmsglm: found 62 good subsets after trying 150. lmsglm: found 64 good subsets after trying 160. lmsglm: found 69 good subsets after trying 170. lmsglm: found 71 good subsets after trying 180. lmsglm: found 76 good subsets after trying 190. lmsglm: found 82 good subsets after trying 200. lmsglm: found 84 good subsets after trying 210. lmsglm: found 87 good subsets after trying 220. lmsglm: found 90 good subsets after trying 230. lmsglm: found 94 good subsets after trying 240. lmsglm: found 99 good subsets after trying 250. lmsglm: found 104 good subsets after trying 260. lmsglm: found 109 good subsets after trying 270. lmsglm: found 112 good subsets after trying 280. lmsglm: found 116 good subsets after trying 290. lmsglm: found 120 good subsets after trying 300. lmsglm: found 124 good subsets after trying 310. lmsglm: found 126 good subsets after trying 320. lmsglm: found 128 good subsets after trying 330. lmsglm: found 132 good subsets after trying 340. lmsglm: found 132 good subsets after trying 350. lmsglm: found 137 good subsets after trying 360. lmsglm: found 140 good subsets after trying 370. lmsglm: found 142 good subsets after trying 380. lmsglm: found 144 good subsets after trying 390. lmsglm: found 149 good subsets after trying 400. *** Starting Forward Search *** fwdglm: finished 10 steps out of 16. > summary(mod) Call: fwdglm(formula = y ~ TNF + IFN, family = poisson(log), data = cellular, nsamp = 200) Units included in the last 1 steps: m=12 m=13 m=14 m=15 m=16 Unit 11 4 9 7 16 Deviances: m=12 m=13 m=14 m=15 m=16 4 0.6626 0.31031 0.2148 0.3662 1.3484 6 0.1469 0.20612 0.2086 -0.2811 -0.9398 7 2.0304 2.10300 2.1060 1.2869 0.5729 9 -1.4891 -1.42150 -0.9471 -0.9490 -1.5690 11 -0.1667 -0.08319 0.1574 -0.2091 -1.0907 Leverage: m=12 m=13 m=14 m=15 m=16 4 NA 0.5268 0.5268 0.5084 0.4012 6 0.4422 0.4229 0.4256 0.3541 0.3566 7 NA NA NA 0.3734 0.3749 9 NA NA 0.3460 0.3437 0.3480 11 0.5836 0.5475 0.4969 0.4588 0.4447 Maximum Deviance and mth Deviance: max mth m=12 0.1667 0.1667 m=13 0.3103 0.3103 m=14 0.9471 0.9471 m=15 1.2869 1.2869 m=16 2.4359 2.4359 Minumum Deviance and (m+1)th Deviance: min (m+1)th m=11 0.3991 0.3991 m=12 0.6626 0.6626 m=13 1.4215 1.4215 m=14 2.1060 2.1060 m=15 7.9246 7.9246 Coefficients: (Intercept) TNF1 TNF10 TNF100 IFN4 IFN20 IFN100 m=12 2.400 0.6913 1.290 2.224 0.5219 0.5641 1.1551 m=13 2.437 0.6423 1.242 2.186 0.5245 0.5652 1.1762 m=14 2.386 0.6422 1.213 2.197 0.5750 0.6157 1.2426 m=15 2.387 0.7212 1.213 2.179 0.5750 0.6595 1.2176 m=16 2.490 0.7212 1.213 1.993 0.5750 0.6595 0.9495 t Statistics: (Intercept) TNF1 TNF10 TNF100 IFN4 IFN20 IFN100 m=12 14.84 3.898 7.876 14.79 4.937 5.227 8.703 m=13 17.62 4.604 10.035 17.80 4.969 5.241 9.463 m=14 18.07 4.603 9.991 17.92 5.926 6.186 11.186 m=15 18.10 5.549 9.991 17.89 5.926 6.898 11.105 m=16 19.35 5.550 9.991 17.54 5.926 6.898 10.388 Diagnostics: Deviance Residual Deviance Pseudo R2 Dispersion m=12 0.1159 516.0 0.9998 0.02317 m=13 0.3221 535.2 0.9994 0.05361 m=14 1.6645 561.8 0.9970 0.23165 m=15 4.3876 563.4 0.9923 0.55472 m=16 23.0286 684.0 0.9674 2.55341 Score Test: Score Test m=12 0.05738 m=13 -0.20127 m=14 -0.30448 m=15 -0.58585 m=16 -3.26194 Cook's Distance: Cook's m=12 0.01319 m=13 0.03374 m=14 0.09539 m=15 0.25071 m=16 5.75794 Modified Cook's Distance: Distance m=12 0.2585 m=13 0.4406 m=14 0.8518 m=15 1.3417 m=16 7.6635 > ## Not run: plot(mod) > plot(mod, 1) > plot(mod, 5) > plot(mod, 6, ylim=c(-3, 20)) > plot(mod, 7) > plot(mod, 8) > > > > cleanEx(); ..nameEx <- "fwdlm" > > ### * fwdlm > > flush(stderr()); flush(stdout()) > > ### Name: fwdlm > ### Title: Forward Search in Linear Regression > ### Aliases: fwdlm print.fwdlm > ### Keywords: robust regression models > > ### ** Examples > > data(forbes) > plot(forbes, xlab="Boiling point", ylab="100 × log(pressure)") > mod <- fwdlm(y ~ x, data=forbes) Starting Forward Search. fwdlm: finished 10 steps out of 17. > summary(mod) Call: fwdlm(formula = y ~ x, data = forbes) Units included in the last 5 steps: m=13 m=14 m=15 m=16 m=17 Unit 9 15 14 1 12 Residuals: m=13 m=14 m=15 m=16 m=17 1 -0.5917 -0.6311 -0.6530 -0.5259 -0.6508 9 -0.3056 -0.2857 -0.2587 -0.2029 -0.4098 11 -0.2649 -0.2260 -0.1834 -0.1504 -0.3835 14 -0.7002 -0.6183 -0.5403 -0.5589 -0.8515 15 -0.4766 -0.3766 -0.2837 -0.3240 -0.6416 Leverage: m=13 m=14 m=15 m=16 m=17 1 NA NA NA 0.19458 0.19344 9 0.07995 0.07773 0.07555 0.06648 0.06337 11 0.08061 0.07218 0.06675 0.06357 0.05961 14 NA NA 0.12614 0.12513 0.11890 15 NA 0.20976 0.18395 0.17923 0.17190 Maximum Studentized Residuals: m=13 m=14 m=15 m=16 m=17 1.683 1.938 2.187 1.999 3.708 Minumum Deletion Residuals: m=12 m=13 m=14 m=15 m=16 1.863 2.238 2.589 2.218 12.404 Estimated Coefficients: (Intercept) x m=13 -41.67 0.8930 m=14 -41.02 0.8897 m=15 -40.49 0.8870 m=16 -41.30 0.8910 m=17 -42.13 0.8955 t Statistics: (Intercept) x m=13 -54.54 236.68 m=14 -50.24 221.32 m=15 -42.33 188.69 m=16 -41.29 180.73 m=17 -12.62 54.45 Cook's Distance: m=13 m=14 m=15 m=16 m=17 0.1231 0.4983 0.3453 0.4642 0.4695 Modified Cook's Distance: m=13 m=14 m=15 m=16 m=17 1.288 2.824 2.561 2.884 8.878 s^2 and R^2: s^2 R^2 m=13 0.005144 0.9998 m=14 0.006862 0.9998 m=15 0.010023 0.9996 m=16 0.012829 0.9996 m=17 0.143555 0.9950 > ## Not run: plot(mod) > plot(mod, 1) > plot(mod, 6, ylim=c(-3, 1000)) > > > > cleanEx(); ..nameEx <- "fwdsco" > > ### * fwdsco > > flush(stderr()); flush(stdout()) > > ### Name: fwdsco > ### Title: Forward Search Transformation in Linear Regression > ### Aliases: fwdsco print.fwdsco > ### Keywords: robust regression models > > ### ** Examples > > data(wool) > mod <- fwdsco(y ~ x1 + x2 + x3, data = wool) fwdsco: finished 10 steps out of 120. fwdsco: finished 20 steps out of 120. fwdsco: finished 30 steps out of 120. fwdsco: finished 40 steps out of 120. fwdsco: finished 50 steps out of 120. fwdsco: finished 60 steps out of 120. fwdsco: finished 70 steps out of 120. fwdsco: finished 80 steps out of 120. fwdsco: finished 90 steps out of 120. fwdsco: finished 100 steps out of 120. fwdsco: finished 110 steps out of 120. fwdsco: finished 120 steps out of 120. > summary(mod) Call: fwdsco(formula = y ~ x1 + x2 + x3, data = wool) Units included in the last 5 steps for different lambdas: lambda = -1: m=23 m=24 m=25 m=26 m=27 22 19 7 8 9 lambda = -0.5: m=23 m=24 m=25 m=26 m=27 22 19 7 8 9 lambda = 0: m=23 m=24 m=25 m=26 m=27 6 22 23 27 24 lambda = 0.5: m=23 m=24 m=25 m=26 m=27 27 24 21 19 20 lambda = 1: m=23 m=24 m=25 m=26 m=27 10 22 21 20 19 Log Likelihood: lambda = -1 lambda = -0.5 lambda = 0 lambda = 0.5 lambda = 1 m=23 -231.1 -214.4 -184.3 -206.4 -231.1 m=24 -254.6 -232.7 -201.0 -220.4 -243.2 m=25 -274.1 -247.1 -215.8 -242.2 -269.7 m=26 -299.4 -263.4 -230.0 -268.3 -306.8 m=27 -321.4 -277.3 -246.8 -287.8 -330.0 Score test statistic: lambda = -1 lambda = -0.5 lambda = 0 lambda = 0.5 lambda = 1 m=23 4.916 1.295 0.1654 -4.064 -9.053 m=24 6.407 2.386 0.1546 -4.072 -8.347 m=25 9.358 3.747 -0.1344 -5.967 -12.110 m=26 13.985 5.787 -0.5323 -8.157 -17.004 m=27 17.706 7.493 -0.9122 -9.551 -18.558 > plot(mod, plot.mle=FALSE) > plot(mod, plot.Sco=FALSE, plot.Lik=TRUE) > > > > cleanEx(); ..nameEx <- "hawkins" > > ### * hawkins > > flush(stderr()); flush(stdout()) > > ### Name: hawkins > ### Title: Hawkins' data > ### Aliases: hawkins > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "kinetics" > > ### * kinetics > > flush(stderr()); flush(stdout()) > > ### Name: kinetics > ### Title: Kinetics data > ### Aliases: kinetics > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "lakes" > > ### * lakes > > flush(stderr()); flush(stdout()) > > ### Name: lakes > ### Title: Lakes data > ### Aliases: lakes > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "leafpine" > > ### * leafpine > > flush(stderr()); flush(stdout()) > > ### Name: leafpine > ### Title: Pine data > ### Aliases: leafpine > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "lmsglm" > > ### * lmsglm > > flush(stderr()); flush(stdout()) > > ### Name: lmsglm > ### Title: Forward Search in Generalized Linear Models > ### Aliases: lmsglm > ### Keywords: robust regression models > > ### ** Examples > > > > cleanEx(); ..nameEx <- "mice" > > ### * mice > > flush(stderr()); flush(stdout()) > > ### Name: mice > ### Title: Mice data > ### Aliases: mice > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "molar" > > ### * molar > > flush(stderr()); flush(stdout()) > > ### Name: molar > ### Title: Molar data > ### Aliases: molar > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "mussels" > > ### * mussels > > flush(stderr()); flush(stdout()) > > ### Name: mussels > ### Title: Mussels data > ### Aliases: mussels > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "ozone" > > ### * ozone > > flush(stderr()); flush(stdout()) > > ### Name: ozone > ### Title: Ozone data > ### Aliases: ozone > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "plot.fwdglm" > > ### * plot.fwdglm > > flush(stderr()); flush(stdout()) > > ### Name: plot.fwdglm > ### Title: Forward Search in Generalized Linear Models > ### Aliases: plot.fwdglm > ### Keywords: robust regression models > > ### ** Examples > > ## Not run: data(cellular) > ## Not run: > ##D mod <- fwdglm(y ~ as.factor(TNF) + as.factor(IFN), data=cellular, > ##D family=poisson(log), nsamp=200) > ## End(Not run) > ## Not run: summary(mod) > ## Not run: plot(mod) > > > > cleanEx(); ..nameEx <- "plot.fwdlm" > > ### * plot.fwdlm > > flush(stderr()); flush(stdout()) > > ### Name: plot.fwdlm > ### Title: Forward Search in Linear Regression > ### Aliases: plot.fwdlm > ### Keywords: robust regression models > > ### ** Examples > > ## Not run: data(forbes) > ## Not run: plot(forbes) > ## Not run: mod <- fwdlm(Log.Pressure ~ Boiling.point, data=forbes) > ## Not run: summary(mod) > ## Not run: plot(mod) > > > > cleanEx(); ..nameEx <- "plot.fwdsco" > > ### * plot.fwdsco > > flush(stderr()); flush(stdout()) > > ### Name: plot.fwdsco > ### Title: Forward Search Transformation in Linear Regression > ### Aliases: plot.fwdsco > ### Keywords: robust regression models > > ### ** Examples > > ## Not run: data(wool) > ## Not run: mod <- fwdsco(y ~ x1 + x2 + x3, data = wool) > ## Not run: plot(mod, plot.mle=FALSE) > ## Not run: plot(mod, plot.Sco=FALSE, plot.Lik=TRUE) > > > > cleanEx(); ..nameEx <- "poison" > > ### * poison > > flush(stderr()); flush(stdout()) > > ### Name: poison > ### Title: Poison data > ### Aliases: poison > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "rainfall" > > ### * rainfall > > flush(stderr()); flush(stdout()) > > ### Name: rainfall > ### Title: Rainfall data > ### Aliases: rainfall > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "salinity" > > ### * salinity > > flush(stderr()); flush(stdout()) > > ### Name: salinity > ### Title: Salinity data > ### Aliases: salinity > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "stackloss" > > ### * stackloss > > flush(stderr()); flush(stdout()) > > ### Name: stackloss > ### Title: Stackloss data > ### Aliases: stackloss > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "vaso" > > ### * vaso > > flush(stderr()); flush(stdout()) > > ### Name: vaso > ### Title: Vaso data > ### Aliases: vaso > ### Keywords: datasets > > ### ** Examples > > > > cleanEx(); ..nameEx <- "wool" > > ### * wool > > flush(stderr()); flush(stdout()) > > ### Name: wool > ### Title: Wool data > ### Aliases: wool > ### Keywords: datasets > > ### ** Examples > > > > ### *