Last updated on 2024-09-05 05:49:04 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.9.12 | 16.47 | 112.11 | 128.58 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 0.9.12 | 10.92 | 79.75 | 90.67 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.9.12 | 209.40 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 0.9.12 | 202.19 | ERROR | |||
| r-devel-windows-x86_64 | 0.9.12 | 18.00 | 130.00 | 148.00 | ERROR | |
| r-patched-linux-x86_64 | 0.9.12 | 15.89 | 105.47 | 121.36 | ERROR | |
| r-release-linux-x86_64 | 0.9.12 | 14.92 | 104.27 | 119.19 | ERROR | |
| r-release-macos-arm64 | 0.9.12 | 55.00 | OK | |||
| r-release-macos-x86_64 | 0.9.12 | 129.00 | OK | |||
| r-release-windows-x86_64 | 0.9.12 | 18.00 | 139.00 | 157.00 | ERROR | |
| r-oldrel-macos-arm64 | 0.9.12 | 67.00 | OK | |||
| r-oldrel-macos-x86_64 | 0.9.12 | 133.00 | OK | |||
| r-oldrel-windows-x86_64 | 0.9.12 | 23.00 | 156.00 | 179.00 | ERROR |
Version: 0.9.12
Check: examples
Result: ERROR
Running examples in ‘ModTools-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: ModTools-package
> ### Title: Regression and Classification Tools
> ### Aliases: ModTools-package ModTools
> ### Keywords: package
>
> ### ** Examples
>
>
> r.swiss <- FitMod(Fertility ~ ., swiss, fitfn="lm")
> r.swiss
Call:
lm(formula = Fertility ~ ., data = swiss)
Coefficients:
(Intercept) Agriculture Examination Education
66.9152 -0.1721 -0.2580 -0.8709
Catholic Infant.Mortality
0.1041 1.0770
> # PlotTA(r.swiss)
> # PlotQQNorm(r.swiss)
>
>
> ## Count models
>
> data(housing, package="MASS")
>
> # poisson count
> r.pois <- FitMod(Freq ~ Infl*Type*Cont + Sat, family=poisson, data=housing, fitfn="poisson")
>
> # negative binomial count
> r.nb <- FitMod(Freq ~ Infl*Type*Cont + Sat, data=housing, fitfn="negbin")
> summary(r.nb)
Call:
glm.nb(formula = Freq ~ Infl * Type * Cont + Sat, data = housing,
init.theta = 11.58904011, link = log)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.14037 0.20801 15.097 < 2e-16 ***
InflMedium 0.26615 0.28836 0.923 0.356028
InflHigh -0.25336 0.30111 -0.841 0.400113
TypeApartment 0.39532 0.28603 1.382 0.166945
TypeAtrium -0.77634 0.32149 -2.415 0.015742 *
TypeTerrace -0.80653 0.32296 -2.497 0.012515 *
ContHigh -0.02395 0.29474 -0.081 0.935244
Sat.L 0.16860 0.07525 2.240 0.025070 *
Sat.Q 0.25749 0.07821 3.292 0.000994 ***
InflMedium:TypeApartment -0.12807 0.39875 -0.321 0.748082
InflHigh:TypeApartment 0.15140 0.41087 0.368 0.712507
InflMedium:TypeAtrium -0.41265 0.45633 -0.904 0.365849
InflHigh:TypeAtrium -0.13006 0.47471 -0.274 0.784108
InflMedium:TypeTerrace 0.01666 0.44423 0.038 0.970080
InflHigh:TypeTerrace -0.06969 0.47429 -0.147 0.883177
InflMedium:ContHigh -0.12632 0.41042 -0.308 0.758256
InflHigh:ContHigh -0.59072 0.44424 -1.330 0.183609
TypeApartment:ContHigh 0.52179 0.40019 1.304 0.192281
TypeAtrium:ContHigh 0.71556 0.43760 1.635 0.102013
TypeTerrace:ContHigh 1.15028 0.43270 2.658 0.007852 **
InflMedium:TypeApartment:ContHigh 0.01178 0.56001 0.021 0.983224
InflHigh:TypeApartment:ContHigh 0.13235 0.59111 0.224 0.822837
InflMedium:TypeAtrium:ContHigh 0.14135 0.62032 0.228 0.819752
InflHigh:TypeAtrium:ContHigh 0.42364 0.65770 0.644 0.519496
InflMedium:TypeTerrace:ContHigh -0.51739 0.60483 -0.855 0.392307
InflHigh:TypeTerrace:ContHigh -0.49705 0.66522 -0.747 0.454946
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for Negative Binomial(11.589) family taken to be 1)
Null deviance: 275.558 on 71 degrees of freedom
Residual deviance: 67.887 on 46 degrees of freedom
AIC: 535.29
Number of Fisher Scoring iterations: 1
Theta: 11.59
Std. Err.: 2.92
2 x log-likelihood: -481.289
>
> r.log <- FitMod(log(Freq) ~ Infl*Type*Cont + Sat, data=housing, fitfn="lm")
> summary(r.log)
Call:
lm(formula = log(Freq) ~ Infl * Type * Cont + Sat, data = housing)
Residuals:
Min 1Q Median 3Q Max
-0.90539 -0.22284 0.01119 0.18857 0.96514
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.140416 0.267518 11.739 1.95e-15 ***
InflMedium 0.259891 0.378328 0.687 0.4956
InflHigh -0.379083 0.378328 -1.002 0.3216
TypeApartment 0.219444 0.378328 0.580 0.5647
TypeAtrium -0.785497 0.378328 -2.076 0.0435 *
TypeTerrace -0.931069 0.378328 -2.461 0.0177 *
ContHigh -0.075612 0.378328 -0.200 0.8425
Sat.L 0.199559 0.094582 2.110 0.0403 *
Sat.Q 0.199376 0.094582 2.108 0.0405 *
InflMedium:TypeApartment 0.048725 0.535036 0.091 0.9278
InflHigh:TypeApartment 0.398374 0.535036 0.745 0.4603
InflMedium:TypeAtrium -0.400214 0.535036 -0.748 0.4583
InflHigh:TypeAtrium 0.002462 0.535036 0.005 0.9963
InflMedium:TypeTerrace 0.143412 0.535036 0.268 0.7899
InflHigh:TypeTerrace 0.154151 0.535036 0.288 0.7746
InflMedium:ContHigh -0.105500 0.535036 -0.197 0.8446
InflHigh:ContHigh -0.737873 0.535036 -1.379 0.1745
TypeApartment:ContHigh 0.697935 0.535036 1.304 0.1986
TypeAtrium:ContHigh 0.763012 0.535036 1.426 0.1606
TypeTerrace:ContHigh 1.114096 0.535036 2.082 0.0429 *
InflMedium:TypeApartment:ContHigh -0.141229 0.756655 -0.187 0.8528
InflHigh:TypeApartment:ContHigh 0.087753 0.756655 0.116 0.9082
InflMedium:TypeAtrium:ContHigh 0.060731 0.756655 0.080 0.9364
InflHigh:TypeAtrium:ContHigh 0.503181 0.756655 0.665 0.5094
InflMedium:TypeTerrace:ContHigh -0.531148 0.756655 -0.702 0.4862
InflHigh:TypeTerrace:ContHigh -0.296310 0.756655 -0.392 0.6972
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4634 on 46 degrees of freedom
Multiple R-squared: 0.751, Adjusted R-squared: 0.6157
F-statistic: 5.551 on 25 and 46 DF, p-value: 2.796e-07
>
> r.ols <- FitMod(Freq ~ Infl*Type*Cont + Sat, data=housing, fitfn="lm")
> summary(r.ols)
Call:
lm(formula = Freq ~ Infl * Type * Cont + Sat, data = housing)
Residuals:
Min 1Q Median 3Q Max
-22.4861 -5.2361 -0.1944 3.5347 27.0556
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.333e+01 7.076e+00 3.298 0.00189 **
InflMedium 7.333e+00 1.001e+01 0.733 0.46737
InflHigh -4.333e+00 1.001e+01 -0.433 0.66700
TypeApartment 1.033e+01 1.001e+01 1.033 0.30717
TypeAtrium -1.267e+01 1.001e+01 -1.266 0.21195
TypeTerrace -1.300e+01 1.001e+01 -1.299 0.20037
ContHigh -1.816e-14 1.001e+01 0.000 1.00000
Sat.L 2.976e+00 2.502e+00 1.190 0.24034
Sat.Q 5.835e+00 2.502e+00 2.332 0.02412 *
InflMedium:TypeApartment -1.667e+00 1.415e+01 -0.118 0.90676
InflHigh:TypeApartment 3.333e+00 1.415e+01 0.236 0.81483
InflMedium:TypeAtrium -8.667e+00 1.415e+01 -0.612 0.54328
InflHigh:TypeAtrium 1.000e+00 1.415e+01 0.071 0.94397
InflMedium:TypeTerrace -4.000e+00 1.415e+01 -0.283 0.77871
InflHigh:TypeTerrace 1.667e+00 1.415e+01 0.118 0.90676
InflMedium:ContHigh -4.000e+00 1.415e+01 -0.283 0.77871
InflHigh:ContHigh -8.667e+00 1.415e+01 -0.612 0.54328
TypeApartment:ContHigh 2.200e+01 1.415e+01 1.555 0.12689
TypeAtrium:ContHigh 1.033e+01 1.415e+01 0.730 0.46897
TypeTerrace:ContHigh 2.067e+01 1.415e+01 1.460 0.15098
InflMedium:TypeApartment:ContHigh 2.333e+00 2.001e+01 0.117 0.90769
InflHigh:TypeApartment:ContHigh -1.200e+01 2.001e+01 -0.600 0.55171
InflMedium:TypeAtrium:ContHigh 3.000e+00 2.001e+01 0.150 0.88150
InflHigh:TypeAtrium:ContHigh 3.667e+00 2.001e+01 0.183 0.85544
InflMedium:TypeTerrace:ContHigh -8.667e+00 2.001e+01 -0.433 0.66700
InflHigh:TypeTerrace:ContHigh -1.167e+01 2.001e+01 -0.583 0.56278
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 12.26 on 46 degrees of freedom
Multiple R-squared: 0.6882, Adjusted R-squared: 0.5187
F-statistic: 4.061 on 25 and 46 DF, p-value: 1.939e-05
>
> r.gam <- FitMod(Freq ~ Infl*Type*Cont + Sat, data=housing, fitfn="gamma")
> summary(r.gam)
Call:
glm(formula = Freq ~ Infl * Type * Cont + Sat, family = "Gamma",
data = housing)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.355e-02 1.143e-02 3.809 0.000412 ***
InflMedium -1.005e-02 1.433e-02 -0.702 0.486503
InflHigh 9.653e-03 1.811e-02 0.533 0.596679
TypeApartment -1.288e-02 1.386e-02 -0.929 0.357851
TypeAtrium 5.053e-02 2.761e-02 1.830 0.073681 .
TypeTerrace 5.354e-02 2.835e-02 1.889 0.065241 .
ContHigh 7.502e-17 1.615e-02 0.000 1.000000
Sat.L -3.211e-03 3.155e-03 -1.018 0.314213
Sat.Q -9.144e-03 4.197e-03 -2.179 0.034492 *
InflMedium:TypeApartment 5.915e-03 1.766e-02 0.335 0.739147
InflHigh:TypeApartment -8.770e-03 2.134e-02 -0.411 0.683073
InflMedium:TypeAtrium 2.340e-02 4.077e-02 0.574 0.568768
InflHigh:TypeAtrium 3.286e-02 4.794e-02 0.685 0.496500
InflMedium:TypeTerrace -1.346e-02 3.553e-02 -0.379 0.706642
InflHigh:TypeTerrace 2.393e-02 4.718e-02 0.507 0.614453
InflMedium:ContHigh 4.784e-03 2.086e-02 0.229 0.819606
InflHigh:ContHigh 4.389e-02 3.364e-02 1.305 0.198497
TypeApartment:ContHigh -1.121e-02 1.856e-02 -0.604 0.548836
TypeAtrium:ContHigh -4.583e-02 3.247e-02 -1.412 0.164758
TypeTerrace:ContHigh -6.393e-02 3.174e-02 -2.014 0.049831 *
InflMedium:TypeApartment:ContHigh -1.765e-03 2.412e-02 -0.073 0.941979
InflHigh:TypeApartment:ContHigh -3.385e-02 3.662e-02 -0.924 0.360209
InflMedium:TypeAtrium:ContHigh -1.225e-02 4.752e-02 -0.258 0.797727
InflHigh:TypeAtrium:ContHigh -5.532e-02 6.091e-02 -0.908 0.368570
InflMedium:TypeTerrace:ContHigh 3.236e-02 4.143e-02 0.781 0.438774
InflHigh:TypeTerrace:ContHigh 1.462e-02 6.501e-02 0.225 0.823077
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for Gamma family taken to be 0.2163103)
Null deviance: 38.140 on 71 degrees of freedom
Residual deviance: 10.671 on 46 degrees of freedom
AIC: 538.18
Number of Fisher Scoring iterations: 6
>
> r.gami <- FitMod(Freq ~ Infl*Type*Cont + Sat, data=housing, fitfn="gamma", link="identity")
> summary(r.gami)
Call:
glm(formula = Freq ~ Infl * Type * Cont + Sat, family = function ()
Gamma(link = identity), data = housing)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.3635 5.7553 4.060 0.000189 ***
InflMedium 7.1563 9.5281 0.751 0.456438
InflHigh -7.2034 6.9307 -1.039 0.304079
TypeApartment 12.6892 10.6939 1.187 0.241486
TypeAtrium -11.1942 6.3987 -1.749 0.086883 .
TypeTerrace -10.9532 6.4273 -1.704 0.095097 .
ContHigh -1.6216 7.8406 -0.207 0.837058
Sat.L 4.9368 1.4858 3.323 0.001755 **
Sat.Q 3.2415 1.1421 2.838 0.006731 **
InflMedium:TypeApartment -3.4540 16.4721 -0.210 0.834835
InflHigh:TypeApartment 1.4504 13.6446 0.106 0.915810
InflMedium:TypeAtrium -9.2801 10.1778 -0.912 0.366629
InflHigh:TypeAtrium 3.5471 7.6884 0.461 0.646714
InflMedium:TypeTerrace -4.5638 10.5680 -0.432 0.667870
InflHigh:TypeTerrace 2.9450 7.6893 0.383 0.703488
InflMedium:ContHigh -3.5733 12.5840 -0.284 0.777715
InflHigh:ContHigh -7.1152 8.8705 -0.802 0.426610
TypeApartment:ContHigh 22.5209 18.6701 1.206 0.233888
TypeAtrium:ContHigh 11.5520 9.9375 1.162 0.251043
TypeTerrace:ContHigh 22.9328 11.8581 1.934 0.059288 .
InflMedium:TypeApartment:ContHigh 0.9018 27.5339 0.033 0.974015
InflHigh:TypeApartment:ContHigh -13.2180 21.9476 -0.602 0.549965
InflMedium:TypeAtrium:ContHigh 2.3032 14.8708 0.155 0.877591
InflHigh:TypeAtrium:ContHigh 0.3769 11.2392 0.034 0.973392
InflMedium:TypeTerrace:ContHigh -8.8401 16.8767 -0.524 0.602931
InflHigh:TypeTerrace:ContHigh -14.3587 12.7806 -1.123 0.267063
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for Gamma family taken to be 0.1923107)
Null deviance: 38.1400 on 71 degrees of freedom
Residual deviance: 9.0822 on 46 degrees of freedom
AIC: 526.3
Number of Fisher Scoring iterations: 14
>
> old <-options(digits=3)
> TMod(r.pois, r.nb, r.log, r.ols, r.gam, r.gami)
Waiting for profiling to be done...
Waiting for profiling to be done...
Waiting for profiling to be done...
Waiting for profiling to be done...
coef r.pois r.nb r.log
1 (Intercept) 3.136 *** 3.140 *** 3.140 ***
2 InflMedium 0.273 . 0.266 0.260
3 InflHigh -0.205 -0.253 -0.379
4 TypeApartment 0.367 * 0.395 0.219
5 TypeAtrium -0.783 *** -0.776 * -0.785 *
6 TypeTerrace -0.815 *** -0.807 * -0.931 *
7 ContHigh -2.151e-15 -0.024 -0.076
8 Sat.L 0.116 ** 0.169 * 0.200 *
9 Sat.Q 0.263 *** 0.257 *** 0.199 *
10 InflMedium:TypeApartment -0.118 -0.128 0.049
11 InflHigh:TypeApartment 0.175 0.151 0.398
12 InflMedium:TypeAtrium -0.407 -0.413 -0.400
13 InflHigh:TypeAtrium -0.169 -0.130 0.002
14 InflMedium:TypeTerrace 0.006 0.017 0.143
15 InflHigh:TypeTerrace -0.093 -0.070 0.154
16 InflMedium:ContHigh -0.140 -0.126 -0.105
17 InflHigh:ContHigh -0.609 * -0.591 -0.738
18 TypeApartment:ContHigh 0.503 * 0.522 0.698
19 TypeAtrium:ContHigh 0.677 * 0.716 0.763
20 TypeTerrace:ContHigh 1.099 *** 1.150 ** 1.114 *
21 InflMedium:TypeApartment:ContHigh 0.054 0.012 -0.141
22 InflHigh:TypeApartment:ContHigh 0.146 0.132 0.088
23 InflMedium:TypeAtrium:ContHigh 0.156 0.141 0.061
24 InflHigh:TypeAtrium:ContHigh 0.478 0.424 0.503
25 InflMedium:TypeTerrace:ContHigh -0.498 -0.517 -0.531
26 InflHigh:TypeTerrace:ContHigh -0.447 -0.497 -0.296
27 ---
28 r.squared - - 0.751
29 adj.r.squared - - 0.616
30 sigma - - 0.463
31 logLik -279.213 -240.645 -30.648
32 logLik0 -587.313 -290.899 -
33 G2 616.201 100.508 -
34 deviance - - 9.876
35 AIC 610.426 535.289 115.296
36 BIC 669.619 596.759 176.766
37 numdf 26 27 25
38 dendf - - 46
39 N 72 72 72
40 n vars 8 8 8
41 n coef 26 26 26
42 F - - 5.551
43 p - - 0.000
44 MAE 6.641 6.608 0.276
45 MAPE 0.354 0.345 0.104
46 MSE 92.754 97.134 0.137
47 RMSE 9.631 9.856 0.370
48 McFadden 0.525 0.173 -
49 McFaddenAdj 0.480 0.083 -
50 Nagelkerke 1.000 0.753 -
51 CoxSnell 1.000 0.752 -
r.ols r.gam r.gami
1 23.333 ** 0.044 *** 23.363 ***
2 7.333 -0.010 7.156
3 -4.333 0.010 -7.203
4 10.333 -0.013 12.689
5 -12.667 0.051 . -11.194 .
6 -13.000 0.054 . -10.953 .
7 -1.816e-14 0.000 -1.622
8 2.976 -0.003 4.937 **
9 5.835 * -0.009 * 3.241 **
10 -1.667 0.006 -3.454
11 3.333 -0.009 1.450
12 -8.667 0.023 -9.280
13 1.000 0.033 3.547
14 -4.000 -0.013 -4.564
15 1.667 0.024 2.945
16 -4.000 0.005 -3.573
17 -8.667 0.044 -7.115
18 22.000 -0.011 22.521
19 10.333 -0.046 11.552
20 20.667 -0.064 * 22.933 .
21 2.333 -0.002 0.902
22 -12.000 -0.034 -13.218
23 3.000 -0.012 2.303
24 3.667 -0.055 0.377
25 -8.667 0.032 -8.840
26 -11.667 0.015 -14.359
27
28 0.688 - -
29 0.519 - -
30 12.256 - -
31 -266.465 -242.088 -236.152
32 - -290.204 -290.204
33 - 96.232 108.104
34 6909.139 - -
35 586.930 538.176 526.304
36 648.400 599.646 587.774
37 25 27 27
38 46 - -
39 72 72 72
40 8 8 8
41 26 26 26
42 4.061 - -
43 0.000 - -
44 7.050 6.883 6.823
45 0.385 0.371 0.321
46 95.960 98.993 102.471
47 9.796 9.950 10.123
48 - 0.166 0.186
49 - 0.076 0.097
50 - 0.737 0.777
51 - 0.737 0.777
> options(old)
>
>
> ## Ordered Regression
>
> r.polr <- FitMod(Sat ~ Infl + Type + Cont, data=housing, fitfn="polr", weights = Freq)
>
> # multinomial Regression
> # r.mult <- FitMod(factor(Sat, ordered=FALSE) ~ Infl + Type + Cont, data=housing,
> # weights = housing$Freq, fitfn="multinom")
>
>
> # Regression tree
> r.rp <- FitMod(factor(Sat, ordered=FALSE) ~ Infl + Type + Cont, data=housing,
+ weights = housing$Freq, fitfn="rpart")
>
> # compare predictions
> d.p <- expand.grid(Infl=levels(housing$Infl), Type=levels(housing$Type), Cont=levels(housing$Cont))
> d.p$polr <- predict(r.polr, newdata=d.p)
> # ??
> # d.p$ols <- factor(round(predict(r.ols, newdata=d.p)^2), labels=levels(housing$Sat))
> # d.p$mult <- predict(r.mult, newdata=d.p)
> d.p$rp <- predict(r.rp, newdata=d.p, type="class")
>
> d.p
Infl Type Cont polr rp
1 Low Tower Low Low High
2 Medium Tower Low High High
3 High Tower Low High High
4 Low Apartment Low Low Low
5 Medium Apartment Low Low High
6 High Apartment Low High High
7 Low Atrium Low Low High
8 Medium Atrium Low High High
9 High Atrium Low High High
10 Low Terrace Low Low Low
11 Medium Terrace Low Low High
12 High Terrace Low High High
13 Low Tower High High High
14 Medium Tower High High High
15 High Tower High High High
16 Low Apartment High Low Low
17 Medium Apartment High High High
18 High Apartment High High High
19 Low Atrium High Low High
20 Medium Atrium High High High
21 High Atrium High High High
22 Low Terrace High Low Low
23 Medium Terrace High Low High
24 High Terrace High High High
>
>
> # Classification with 2 classes ***************
>
> r.pima <- FitMod(diabetes ~ ., d.pima, fitfn="logit")
> r.pima
Call: glm(formula = diabetes ~ ., family = "binomial", data = d.pima)
Coefficients:
(Intercept) pregnant glucose pressure triceps insulin
-8.404696 0.123182 0.035164 -0.013296 0.000619 -0.001192
mass pedigree age
0.089701 0.945180 0.014869
Degrees of Freedom: 767 Total (i.e. Null); 759 Residual
Null Deviance: 993.5
Residual Deviance: 723.4 AIC: 741.4
> Conf(r.pima)
Confusion Matrix and Statistics
Reference
Prediction pos neg
pos 156 55
neg 112 445
Total n : 768
Accuracy : 0.7826
95% CI : (0.7520, 0.8103)
No Information Rate : 0.6510
P-Value [Acc > NIR] : 1.37e-15
Kappa : 0.4966
Mcnemar's Test P-Value : 1.47e-05
Sensitivity : 0.5821
Specificity : 0.8900
Pos Pred Value : 0.7393
Neg Pred Value : 0.7989
Prevalence : 0.3490
Detection Rate : 0.2747
Detection Prevalence : 0.2031
Balanced Accuracy : 0.7360
F-val Accuracy : 0.6514
Matthews Cor.-Coef : 0.5041
'Positive' Class : pos
> plot(ROC(r.pima))
Setting levels: control = neg, case = pos
Setting direction: controls < cases
> OddsRatio(r.pima)
Call:
glm(formula = diabetes ~ ., family = "binomial", data = d.pima)
Odds Ratios:
or or.lci or.uci Pr(>|z|)
(Intercept) 0.000 0.000 0.001 < 2.2e-16 ***
pregnant 1.131 1.062 1.204 1.23e-04 ***
glucose 1.036 1.028 1.043 < 2.2e-16 ***
pressure 0.987 0.977 0.997 0.0111 *
triceps 1.001 0.987 1.014 0.9285
insulin 0.999 0.997 1.001 0.1861
mass 1.094 1.062 1.127 2.76e-09 ***
pedigree 2.573 1.432 4.625 0.0016 **
age 1.015 0.997 1.034 0.1112
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Brier Score: 0.153 Nagelkerke R2: 0.408
>
>
> # rpart tree
> rp.pima <- FitMod(diabetes ~ ., d.pima, fitfn="rpart")
> rp.pima
n= 768
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 768 268 neg (0.65104167 0.34895833)
2) glucose< 127.5 485 94 neg (0.80618557 0.19381443)
4) age< 28.5 271 23 neg (0.91512915 0.08487085) *
5) age>=28.5 214 71 neg (0.66822430 0.33177570)
10) mass< 26.35 41 2 neg (0.95121951 0.04878049) *
11) mass>=26.35 173 69 neg (0.60115607 0.39884393)
22) glucose< 99.5 55 10 neg (0.81818182 0.18181818) *
23) glucose>=99.5 118 59 neg (0.50000000 0.50000000)
46) pedigree< 0.561 84 34 neg (0.59523810 0.40476190)
92) pedigree< 0.2 21 4 neg (0.80952381 0.19047619) *
93) pedigree>=0.2 63 30 neg (0.52380952 0.47619048)
186) pregnant>=1.5 52 21 neg (0.59615385 0.40384615)
372) pressure>=67 40 12 neg (0.70000000 0.30000000) *
373) pressure< 67 12 3 pos (0.25000000 0.75000000) *
187) pregnant< 1.5 11 2 pos (0.18181818 0.81818182) *
47) pedigree>=0.561 34 9 pos (0.26470588 0.73529412) *
3) glucose>=127.5 283 109 pos (0.38515901 0.61484099)
6) mass< 29.95 76 24 neg (0.68421053 0.31578947)
12) glucose< 145.5 41 6 neg (0.85365854 0.14634146) *
13) glucose>=145.5 35 17 pos (0.48571429 0.51428571)
26) insulin< 14.5 21 8 neg (0.61904762 0.38095238) *
27) insulin>=14.5 14 4 pos (0.28571429 0.71428571) *
7) mass>=29.95 207 57 pos (0.27536232 0.72463768)
14) glucose< 157.5 115 45 pos (0.39130435 0.60869565)
28) age< 30.5 50 23 neg (0.54000000 0.46000000)
56) pressure>=61 40 13 neg (0.67500000 0.32500000)
112) mass< 41.8 31 7 neg (0.77419355 0.22580645) *
113) mass>=41.8 9 3 pos (0.33333333 0.66666667) *
57) pressure< 61 10 0 pos (0.00000000 1.00000000) *
29) age>=30.5 65 18 pos (0.27692308 0.72307692) *
15) glucose>=157.5 92 12 pos (0.13043478 0.86956522) *
> Conf(rp.pima)
Confusion Matrix and Statistics
Reference
Prediction neg pos
neg 449 72
pos 51 196
Total n : 768
Accuracy : 0.8398
95% CI : (0.8122, 0.8641)
No Information Rate : 0.6510
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.6410
Mcnemar's Test P-Value : 0.0713
Sensitivity : 0.8980
Specificity : 0.7313
Pos Pred Value : 0.8618
Neg Pred Value : 0.7935
Prevalence : 0.6510
Detection Rate : 0.6784
Detection Prevalence : 0.5846
Balanced Accuracy : 0.8147
F-val Accuracy : 0.8795
Matthews Cor.-Coef : 0.6422
'Positive' Class : neg
> lines(ROC(rp.pima), col=hblue)
Setting levels: control = neg, case = pos
Setting direction: controls < cases
> # to be improved
> plot(rp.pima, col=SetAlpha(c("blue","red"), 0.4), cex=0.7)
>
>
> # Random Forest
> rf.pima <- FitMod(diabetes ~ ., d.pima, method="class", fitfn="randomForest")
> rf.pima
Call:
randomForest(formula = diabetes ~ ., data = d.pima, method = "class", na.action = function (object, ...) UseMethod("na.omit"))
Type of random forest: classification
Number of trees: 500
No. of variables tried at each split: 2
OOB estimate of error rate: 23.18%
Confusion matrix:
neg pos class.error
neg 430 70 0.1400000
pos 108 160 0.4029851
> Conf(rf.pima)
Confusion Matrix and Statistics
Reference
Prediction neg pos
neg 430 108
pos 70 160
Total n : 768
Accuracy : 0.7682
95% CI : (0.7371, 0.7967)
No Information Rate : 0.6510
P-Value [Acc > NIR] : 1.29e-12
Kappa : 0.4726
Mcnemar's Test P-Value : 0.0055
Sensitivity : 0.8600
Specificity : 0.5970
Pos Pred Value : 0.7993
Neg Pred Value : 0.6957
Prevalence : 0.6510
Detection Rate : 0.7005
Detection Prevalence : 0.5599
Balanced Accuracy : 0.7285
F-val Accuracy : 0.8285
Matthews Cor.-Coef : 0.4756
'Positive' Class : neg
> lines(ROC(r.pima), col=hred)
Setting levels: control = neg, case = pos
Setting direction: controls < cases
>
>
>
> # more models to compare
>
> d.pim <- SplitTrainTest(d.pima, p = 0.2)
> mdiab <- formula(diabetes ~ pregnant + glucose + pressure + triceps
+ + insulin + mass + pedigree + age)
>
> r.glm <- FitMod(mdiab, data=d.pim$train, fitfn="logit")
> r.rp <- FitMod(mdiab, data=d.pim$train, fitfn="rpart")
> r.rf <- FitMod(mdiab, data=d.pim$train, fitfn="randomForest")
> r.svm <- FitMod(mdiab, data=d.pim$train, fitfn="svm")
> r.c5 <- FitMod(mdiab, data=d.pim$train, fitfn="C5.0")
> r.nn <- FitMod(mdiab, data=d.pim$train, fitfn="nnet")
> r.nb <- FitMod(mdiab, data=d.pim$train, fitfn="naive_bayes")
> r.lda <- FitMod(mdiab, data=d.pim$train, fitfn="lda")
> r.qda <- FitMod(mdiab, data=d.pim$train, fitfn="qda")
> r.lb <- FitMod(mdiab, data=d.pim$train, fitfn="lb")
>
> mods <- list(glm=r.glm, rp=r.rp, rf=r.rf, svm=r.svm, c5=r.c5
+ , nn=r.nn, nb=r.nb, lda=r.lda, qda=r.qda, lb=r.lb)
>
> # insight in the Regression tree
> plot(r.rp, box.palette = as.list(Pal("Helsana", alpha = 0.5)))
>
> # Insample accuracy ...
> TModC(mods, ord="auc")
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Error in if (attr(attr(data, "terms"), "response")) { :
argument is of length zero
Calls: TModC ... sapply -> lapply -> FUN -> model.extract -> model.response
Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-release-linux-x86_64
Version: 0.9.12
Check: Rd cross-references
Result: NOTE
Undeclared package ‘pscl’ in Rd xrefs
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.9.12
Check: examples
Result: ERROR
Running examples in ‘ModTools-Ex.R’ failed
The error most likely occurred in:
> ### Name: ModTools-package
> ### Title: Regression and Classification Tools
> ### Aliases: ModTools-package ModTools
> ### Keywords: package
>
> ### ** Examples
>
>
> r.swiss <- FitMod(Fertility ~ ., swiss, fitfn="lm")
> r.swiss
Call:
lm(formula = Fertility ~ ., data = swiss)
Coefficients:
(Intercept) Agriculture Examination Education
66.9152 -0.1721 -0.2580 -0.8709
Catholic Infant.Mortality
0.1041 1.0770
> # PlotTA(r.swiss)
> # PlotQQNorm(r.swiss)
>
>
> ## Count models
>
> data(housing, package="MASS")
>
> # poisson count
> r.pois <- FitMod(Freq ~ Infl*Type*Cont + Sat, family=poisson, data=housing, fitfn="poisson")
>
> # negative binomial count
> r.nb <- FitMod(Freq ~ Infl*Type*Cont + Sat, data=housing, fitfn="negbin")
> summary(r.nb)
Call:
glm.nb(formula = Freq ~ Infl * Type * Cont + Sat, data = housing,
init.theta = 11.58904011, link = log)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.14037 0.20801 15.097 < 2e-16 ***
InflMedium 0.26615 0.28836 0.923 0.356028
InflHigh -0.25336 0.30111 -0.841 0.400113
TypeApartment 0.39532 0.28603 1.382 0.166945
TypeAtrium -0.77634 0.32149 -2.415 0.015742 *
TypeTerrace -0.80653 0.32296 -2.497 0.012515 *
ContHigh -0.02395 0.29474 -0.081 0.935244
Sat.L 0.16860 0.07525 2.240 0.025070 *
Sat.Q 0.25749 0.07821 3.292 0.000994 ***
InflMedium:TypeApartment -0.12807 0.39875 -0.321 0.748082
InflHigh:TypeApartment 0.15140 0.41087 0.368 0.712507
InflMedium:TypeAtrium -0.41265 0.45633 -0.904 0.365849
InflHigh:TypeAtrium -0.13006 0.47471 -0.274 0.784108
InflMedium:TypeTerrace 0.01666 0.44423 0.038 0.970080
InflHigh:TypeTerrace -0.06969 0.47429 -0.147 0.883177
InflMedium:ContHigh -0.12632 0.41042 -0.308 0.758256
InflHigh:ContHigh -0.59072 0.44424 -1.330 0.183609
TypeApartment:ContHigh 0.52179 0.40019 1.304 0.192281
TypeAtrium:ContHigh 0.71556 0.43760 1.635 0.102013
TypeTerrace:ContHigh 1.15028 0.43270 2.658 0.007852 **
InflMedium:TypeApartment:ContHigh 0.01178 0.56001 0.021 0.983224
InflHigh:TypeApartment:ContHigh 0.13235 0.59111 0.224 0.822837
InflMedium:TypeAtrium:ContHigh 0.14135 0.62032 0.228 0.819752
InflHigh:TypeAtrium:ContHigh 0.42364 0.65770 0.644 0.519496
InflMedium:TypeTerrace:ContHigh -0.51739 0.60483 -0.855 0.392307
InflHigh:TypeTerrace:ContHigh -0.49705 0.66522 -0.747 0.454946
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for Negative Binomial(11.589) family taken to be 1)
Null deviance: 275.558 on 71 degrees of freedom
Residual deviance: 67.887 on 46 degrees of freedom
AIC: 535.29
Number of Fisher Scoring iterations: 1
Theta: 11.59
Std. Err.: 2.92
2 x log-likelihood: -481.289
>
> r.log <- FitMod(log(Freq) ~ Infl*Type*Cont + Sat, data=housing, fitfn="lm")
> summary(r.log)
Call:
lm(formula = log(Freq) ~ Infl * Type * Cont + Sat, data = housing)
Residuals:
Min 1Q Median 3Q Max
-0.90539 -0.22284 0.01119 0.18857 0.96514
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.140416 0.267518 11.739 1.95e-15 ***
InflMedium 0.259891 0.378328 0.687 0.4956
InflHigh -0.379083 0.378328 -1.002 0.3216
TypeApartment 0.219444 0.378328 0.580 0.5647
TypeAtrium -0.785497 0.378328 -2.076 0.0435 *
TypeTerrace -0.931069 0.378328 -2.461 0.0177 *
ContHigh -0.075612 0.378328 -0.200 0.8425
Sat.L 0.199559 0.094582 2.110 0.0403 *
Sat.Q 0.199376 0.094582 2.108 0.0405 *
InflMedium:TypeApartment 0.048725 0.535036 0.091 0.9278
InflHigh:TypeApartment 0.398374 0.535036 0.745 0.4603
InflMedium:TypeAtrium -0.400214 0.535036 -0.748 0.4583
InflHigh:TypeAtrium 0.002462 0.535036 0.005 0.9963
InflMedium:TypeTerrace 0.143412 0.535036 0.268 0.7899
InflHigh:TypeTerrace 0.154151 0.535036 0.288 0.7746
InflMedium:ContHigh -0.105500 0.535036 -0.197 0.8446
InflHigh:ContHigh -0.737873 0.535036 -1.379 0.1745
TypeApartment:ContHigh 0.697935 0.535036 1.304 0.1986
TypeAtrium:ContHigh 0.763012 0.535036 1.426 0.1606
TypeTerrace:ContHigh 1.114096 0.535036 2.082 0.0429 *
InflMedium:TypeApartment:ContHigh -0.141229 0.756655 -0.187 0.8528
InflHigh:TypeApartment:ContHigh 0.087753 0.756655 0.116 0.9082
InflMedium:TypeAtrium:ContHigh 0.060731 0.756655 0.080 0.9364
InflHigh:TypeAtrium:ContHigh 0.503181 0.756655 0.665 0.5094
InflMedium:TypeTerrace:ContHigh -0.531148 0.756655 -0.702 0.4862
InflHigh:TypeTerrace:ContHigh -0.296310 0.756655 -0.392 0.6972
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4634 on 46 degrees of freedom
Multiple R-squared: 0.751, Adjusted R-squared: 0.6157
F-statistic: 5.551 on 25 and 46 DF, p-value: 2.796e-07
>
> r.ols <- FitMod(Freq ~ Infl*Type*Cont + Sat, data=housing, fitfn="lm")
> summary(r.ols)
Call:
lm(formula = Freq ~ Infl * Type * Cont + Sat, data = housing)
Residuals:
Min 1Q Median 3Q Max
-22.4861 -5.2361 -0.1944 3.5347 27.0556
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.333e+01 7.076e+00 3.298 0.00189 **
InflMedium 7.333e+00 1.001e+01 0.733 0.46737
InflHigh -4.333e+00 1.001e+01 -0.433 0.66700
TypeApartment 1.033e+01 1.001e+01 1.033 0.30717
TypeAtrium -1.267e+01 1.001e+01 -1.266 0.21195
TypeTerrace -1.300e+01 1.001e+01 -1.299 0.20037
ContHigh -1.816e-14 1.001e+01 0.000 1.00000
Sat.L 2.976e+00 2.502e+00 1.190 0.24034
Sat.Q 5.835e+00 2.502e+00 2.332 0.02412 *
InflMedium:TypeApartment -1.667e+00 1.415e+01 -0.118 0.90676
InflHigh:TypeApartment 3.333e+00 1.415e+01 0.236 0.81483
InflMedium:TypeAtrium -8.667e+00 1.415e+01 -0.612 0.54328
InflHigh:TypeAtrium 1.000e+00 1.415e+01 0.071 0.94397
InflMedium:TypeTerrace -4.000e+00 1.415e+01 -0.283 0.77871
InflHigh:TypeTerrace 1.667e+00 1.415e+01 0.118 0.90676
InflMedium:ContHigh -4.000e+00 1.415e+01 -0.283 0.77871
InflHigh:ContHigh -8.667e+00 1.415e+01 -0.612 0.54328
TypeApartment:ContHigh 2.200e+01 1.415e+01 1.555 0.12689
TypeAtrium:ContHigh 1.033e+01 1.415e+01 0.730 0.46897
TypeTerrace:ContHigh 2.067e+01 1.415e+01 1.460 0.15098
InflMedium:TypeApartment:ContHigh 2.333e+00 2.001e+01 0.117 0.90769
InflHigh:TypeApartment:ContHigh -1.200e+01 2.001e+01 -0.600 0.55171
InflMedium:TypeAtrium:ContHigh 3.000e+00 2.001e+01 0.150 0.88150
InflHigh:TypeAtrium:ContHigh 3.667e+00 2.001e+01 0.183 0.85544
InflMedium:TypeTerrace:ContHigh -8.667e+00 2.001e+01 -0.433 0.66700
InflHigh:TypeTerrace:ContHigh -1.167e+01 2.001e+01 -0.583 0.56278
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 12.26 on 46 degrees of freedom
Multiple R-squared: 0.6882, Adjusted R-squared: 0.5187
F-statistic: 4.061 on 25 and 46 DF, p-value: 1.939e-05
>
> r.gam <- FitMod(Freq ~ Infl*Type*Cont + Sat, data=housing, fitfn="gamma")
> summary(r.gam)
Call:
glm(formula = Freq ~ Infl * Type * Cont + Sat, family = "Gamma",
data = housing)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.355e-02 1.143e-02 3.809 0.000412 ***
InflMedium -1.005e-02 1.433e-02 -0.702 0.486503
InflHigh 9.653e-03 1.811e-02 0.533 0.596679
TypeApartment -1.288e-02 1.386e-02 -0.929 0.357851
TypeAtrium 5.053e-02 2.761e-02 1.830 0.073681 .
TypeTerrace 5.354e-02 2.835e-02 1.889 0.065241 .
ContHigh 7.502e-17 1.615e-02 0.000 1.000000
Sat.L -3.211e-03 3.155e-03 -1.018 0.314213
Sat.Q -9.144e-03 4.197e-03 -2.179 0.034492 *
InflMedium:TypeApartment 5.915e-03 1.766e-02 0.335 0.739147
InflHigh:TypeApartment -8.770e-03 2.134e-02 -0.411 0.683073
InflMedium:TypeAtrium 2.340e-02 4.077e-02 0.574 0.568768
InflHigh:TypeAtrium 3.286e-02 4.794e-02 0.685 0.496500
InflMedium:TypeTerrace -1.346e-02 3.553e-02 -0.379 0.706642
InflHigh:TypeTerrace 2.393e-02 4.718e-02 0.507 0.614453
InflMedium:ContHigh 4.784e-03 2.086e-02 0.229 0.819606
InflHigh:ContHigh 4.389e-02 3.364e-02 1.305 0.198497
TypeApartment:ContHigh -1.121e-02 1.856e-02 -0.604 0.548836
TypeAtrium:ContHigh -4.583e-02 3.247e-02 -1.412 0.164758
TypeTerrace:ContHigh -6.393e-02 3.174e-02 -2.014 0.049831 *
InflMedium:TypeApartment:ContHigh -1.765e-03 2.412e-02 -0.073 0.941979
InflHigh:TypeApartment:ContHigh -3.385e-02 3.662e-02 -0.924 0.360209
InflMedium:TypeAtrium:ContHigh -1.225e-02 4.752e-02 -0.258 0.797727
InflHigh:TypeAtrium:ContHigh -5.532e-02 6.091e-02 -0.908 0.368570
InflMedium:TypeTerrace:ContHigh 3.236e-02 4.143e-02 0.781 0.438774
InflHigh:TypeTerrace:ContHigh 1.462e-02 6.501e-02 0.225 0.823077
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for Gamma family taken to be 0.2163103)
Null deviance: 38.140 on 71 degrees of freedom
Residual deviance: 10.671 on 46 degrees of freedom
AIC: 538.18
Number of Fisher Scoring iterations: 6
>
> r.gami <- FitMod(Freq ~ Infl*Type*Cont + Sat, data=housing, fitfn="gamma", link="identity")
> summary(r.gami)
Call:
glm(formula = Freq ~ Infl * Type * Cont + Sat, family = function ()
Gamma(link = identity), data = housing)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.3635 5.7553 4.060 0.000189 ***
InflMedium 7.1563 9.5281 0.751 0.456438
InflHigh -7.2034 6.9307 -1.039 0.304079
TypeApartment 12.6892 10.6939 1.187 0.241486
TypeAtrium -11.1942 6.3987 -1.749 0.086883 .
TypeTerrace -10.9532 6.4273 -1.704 0.095097 .
ContHigh -1.6216 7.8406 -0.207 0.837058
Sat.L 4.9368 1.4858 3.323 0.001755 **
Sat.Q 3.2415 1.1421 2.838 0.006731 **
InflMedium:TypeApartment -3.4540 16.4721 -0.210 0.834835
InflHigh:TypeApartment 1.4504 13.6446 0.106 0.915810
InflMedium:TypeAtrium -9.2801 10.1778 -0.912 0.366629
InflHigh:TypeAtrium 3.5471 7.6884 0.461 0.646714
InflMedium:TypeTerrace -4.5638 10.5680 -0.432 0.667870
InflHigh:TypeTerrace 2.9450 7.6893 0.383 0.703488
InflMedium:ContHigh -3.5733 12.5840 -0.284 0.777715
InflHigh:ContHigh -7.1152 8.8705 -0.802 0.426610
TypeApartment:ContHigh 22.5209 18.6701 1.206 0.233888
TypeAtrium:ContHigh 11.5520 9.9375 1.162 0.251043
TypeTerrace:ContHigh 22.9328 11.8581 1.934 0.059288 .
InflMedium:TypeApartment:ContHigh 0.9018 27.5339 0.033 0.974015
InflHigh:TypeApartment:ContHigh -13.2180 21.9476 -0.602 0.549965
InflMedium:TypeAtrium:ContHigh 2.3032 14.8708 0.155 0.877591
InflHigh:TypeAtrium:ContHigh 0.3769 11.2392 0.034 0.973392
InflMedium:TypeTerrace:ContHigh -8.8401 16.8767 -0.524 0.602931
InflHigh:TypeTerrace:ContHigh -14.3587 12.7806 -1.123 0.267063
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for Gamma family taken to be 0.1923107)
Null deviance: 38.1400 on 71 degrees of freedom
Residual deviance: 9.0822 on 46 degrees of freedom
AIC: 526.3
Number of Fisher Scoring iterations: 14
>
> old <-options(digits=3)
> TMod(r.pois, r.nb, r.log, r.ols, r.gam, r.gami)
Waiting for profiling to be done...
Waiting for profiling to be done...
Waiting for profiling to be done...
Waiting for profiling to be done...
coef r.pois r.nb r.log
1 (Intercept) 3.136 *** 3.140 *** 3.140 ***
2 InflMedium 0.273 . 0.266 0.260
3 InflHigh -0.205 -0.253 -0.379
4 TypeApartment 0.367 * 0.395 0.219
5 TypeAtrium -0.783 *** -0.776 * -0.785 *
6 TypeTerrace -0.815 *** -0.807 * -0.931 *
7 ContHigh -2.151e-15 -0.024 -0.076
8 Sat.L 0.116 ** 0.169 * 0.200 *
9 Sat.Q 0.263 *** 0.257 *** 0.199 *
10 InflMedium:TypeApartment -0.118 -0.128 0.049
11 InflHigh:TypeApartment 0.175 0.151 0.398
12 InflMedium:TypeAtrium -0.407 -0.413 -0.400
13 InflHigh:TypeAtrium -0.169 -0.130 0.002
14 InflMedium:TypeTerrace 0.006 0.017 0.143
15 InflHigh:TypeTerrace -0.093 -0.070 0.154
16 InflMedium:ContHigh -0.140 -0.126 -0.105
17 InflHigh:ContHigh -0.609 * -0.591 -0.738
18 TypeApartment:ContHigh 0.503 * 0.522 0.698
19 TypeAtrium:ContHigh 0.677 * 0.716 0.763
20 TypeTerrace:ContHigh 1.099 *** 1.150 ** 1.114 *
21 InflMedium:TypeApartment:ContHigh 0.054 0.012 -0.141
22 InflHigh:TypeApartment:ContHigh 0.146 0.132 0.088
23 InflMedium:TypeAtrium:ContHigh 0.156 0.141 0.061
24 InflHigh:TypeAtrium:ContHigh 0.478 0.424 0.503
25 InflMedium:TypeTerrace:ContHigh -0.498 -0.517 -0.531
26 InflHigh:TypeTerrace:ContHigh -0.447 -0.497 -0.296
27 ---
28 r.squared - - 0.751
29 adj.r.squared - - 0.616
30 sigma - - 0.463
31 logLik -279.213 -240.645 -30.648
32 logLik0 -587.313 -290.899 -
33 G2 616.201 100.508 -
34 deviance - - 9.876
35 AIC 610.426 535.289 115.296
36 BIC 669.619 596.759 176.766
37 numdf 26 27 25
38 dendf - - 46
39 N 72 72 72
40 n vars 8 8 8
41 n coef 26 26 26
42 F - - 5.551
43 p - - 0.000
44 MAE 6.641 6.608 0.276
45 MAPE 0.354 0.345 0.104
46 MSE 92.754 97.134 0.137
47 RMSE 9.631 9.856 0.370
48 McFadden 0.525 0.173 -
49 McFaddenAdj 0.480 0.083 -
50 Nagelkerke 1.000 0.753 -
51 CoxSnell 1.000 0.752 -
r.ols r.gam r.gami
1 23.333 ** 0.044 *** 23.363 ***
2 7.333 -0.010 7.156
3 -4.333 0.010 -7.203
4 10.333 -0.013 12.689
5 -12.667 0.051 . -11.194 .
6 -13.000 0.054 . -10.953 .
7 -1.816e-14 0.000 -1.622
8 2.976 -0.003 4.937 **
9 5.835 * -0.009 * 3.241 **
10 -1.667 0.006 -3.454
11 3.333 -0.009 1.450
12 -8.667 0.023 -9.280
13 1.000 0.033 3.547
14 -4.000 -0.013 -4.564
15 1.667 0.024 2.945
16 -4.000 0.005 -3.573
17 -8.667 0.044 -7.115
18 22.000 -0.011 22.521
19 10.333 -0.046 11.552
20 20.667 -0.064 * 22.933 .
21 2.333 -0.002 0.902
22 -12.000 -0.034 -13.218
23 3.000 -0.012 2.303
24 3.667 -0.055 0.377
25 -8.667 0.032 -8.840
26 -11.667 0.015 -14.359
27
28 0.688 - -
29 0.519 - -
30 12.256 - -
31 -266.465 -242.088 -236.152
32 - -290.204 -290.204
33 - 96.232 108.104
34 6909.139 - -
35 586.930 538.176 526.304
36 648.400 599.646 587.774
37 25 27 27
38 46 - -
39 72 72 72
40 8 8 8
41 26 26 26
42 4.061 - -
43 0.000 - -
44 7.050 6.883 6.823
45 0.385 0.371 0.321
46 95.960 98.993 102.471
47 9.796 9.950 10.123
48 - 0.166 0.186
49 - 0.076 0.097
50 - 0.737 0.777
51 - 0.737 0.777
> options(old)
>
>
> ## Ordered Regression
>
> r.polr <- FitMod(Sat ~ Infl + Type + Cont, data=housing, fitfn="polr", weights = Freq)
>
> # multinomial Regression
> # r.mult <- FitMod(factor(Sat, ordered=FALSE) ~ Infl + Type + Cont, data=housing,
> # weights = housing$Freq, fitfn="multinom")
>
>
> # Regression tree
> r.rp <- FitMod(factor(Sat, ordered=FALSE) ~ Infl + Type + Cont, data=housing,
+ weights = housing$Freq, fitfn="rpart")
>
> # compare predictions
> d.p <- expand.grid(Infl=levels(housing$Infl), Type=levels(housing$Type), Cont=levels(housing$Cont))
> d.p$polr <- predict(r.polr, newdata=d.p)
> # ??
> # d.p$ols <- factor(round(predict(r.ols, newdata=d.p)^2), labels=levels(housing$Sat))
> # d.p$mult <- predict(r.mult, newdata=d.p)
> d.p$rp <- predict(r.rp, newdata=d.p, type="class")
>
> d.p
Infl Type Cont polr rp
1 Low Tower Low Low High
2 Medium Tower Low High High
3 High Tower Low High High
4 Low Apartment Low Low Low
5 Medium Apartment Low Low High
6 High Apartment Low High High
7 Low Atrium Low Low High
8 Medium Atrium Low High High
9 High Atrium Low High High
10 Low Terrace Low Low Low
11 Medium Terrace Low Low High
12 High Terrace Low High High
13 Low Tower High High High
14 Medium Tower High High High
15 High Tower High High High
16 Low Apartment High Low Low
17 Medium Apartment High High High
18 High Apartment High High High
19 Low Atrium High Low High
20 Medium Atrium High High High
21 High Atrium High High High
22 Low Terrace High Low Low
23 Medium Terrace High Low High
24 High Terrace High High High
>
>
> # Classification with 2 classes ***************
>
> r.pima <- FitMod(diabetes ~ ., d.pima, fitfn="logit")
> r.pima
Call: glm(formula = diabetes ~ ., family = "binomial", data = d.pima)
Coefficients:
(Intercept) pregnant glucose pressure triceps insulin
-8.404696 0.123182 0.035164 -0.013296 0.000619 -0.001192
mass pedigree age
0.089701 0.945180 0.014869
Degrees of Freedom: 767 Total (i.e. Null); 759 Residual
Null Deviance: 993.5
Residual Deviance: 723.4 AIC: 741.4
> Conf(r.pima)
Confusion Matrix and Statistics
Reference
Prediction pos neg
pos 156 55
neg 112 445
Total n : 768
Accuracy : 0.7826
95% CI : (0.7520, 0.8103)
No Information Rate : 0.6510
P-Value [Acc > NIR] : 1.37e-15
Kappa : 0.4966
Mcnemar's Test P-Value : 1.47e-05
Sensitivity : 0.5821
Specificity : 0.8900
Pos Pred Value : 0.7393
Neg Pred Value : 0.7989
Prevalence : 0.3490
Detection Rate : 0.2747
Detection Prevalence : 0.2031
Balanced Accuracy : 0.7360
F-val Accuracy : 0.6514
Matthews Cor.-Coef : 0.5041
'Positive' Class : pos
> plot(ROC(r.pima))
Setting levels: control = neg, case = pos
Setting direction: controls < cases
> OddsRatio(r.pima)
Call:
glm(formula = diabetes ~ ., family = "binomial", data = d.pima)
Odds Ratios:
or or.lci or.uci Pr(>|z|)
(Intercept) 0.000 0.000 0.001 < 2.2e-16 ***
pregnant 1.131 1.062 1.204 1.23e-04 ***
glucose 1.036 1.028 1.043 < 2.2e-16 ***
pressure 0.987 0.977 0.997 0.0111 *
triceps 1.001 0.987 1.014 0.9285
insulin 0.999 0.997 1.001 0.1861
mass 1.094 1.062 1.127 2.76e-09 ***
pedigree 2.573 1.432 4.625 0.0016 **
age 1.015 0.997 1.034 0.1112
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Brier Score: 0.153 Nagelkerke R2: 0.408
>
>
> # rpart tree
> rp.pima <- FitMod(diabetes ~ ., d.pima, fitfn="rpart")
> rp.pima
n= 768
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 768 268 neg (0.65104167 0.34895833)
2) glucose< 127.5 485 94 neg (0.80618557 0.19381443)
4) age< 28.5 271 23 neg (0.91512915 0.08487085) *
5) age>=28.5 214 71 neg (0.66822430 0.33177570)
10) mass< 26.35 41 2 neg (0.95121951 0.04878049) *
11) mass>=26.35 173 69 neg (0.60115607 0.39884393)
22) glucose< 99.5 55 10 neg (0.81818182 0.18181818) *
23) glucose>=99.5 118 59 neg (0.50000000 0.50000000)
46) pedigree< 0.561 84 34 neg (0.59523810 0.40476190)
92) pedigree< 0.2 21 4 neg (0.80952381 0.19047619) *
93) pedigree>=0.2 63 30 neg (0.52380952 0.47619048)
186) pregnant>=1.5 52 21 neg (0.59615385 0.40384615)
372) pressure>=67 40 12 neg (0.70000000 0.30000000) *
373) pressure< 67 12 3 pos (0.25000000 0.75000000) *
187) pregnant< 1.5 11 2 pos (0.18181818 0.81818182) *
47) pedigree>=0.561 34 9 pos (0.26470588 0.73529412) *
3) glucose>=127.5 283 109 pos (0.38515901 0.61484099)
6) mass< 29.95 76 24 neg (0.68421053 0.31578947)
12) glucose< 145.5 41 6 neg (0.85365854 0.14634146) *
13) glucose>=145.5 35 17 pos (0.48571429 0.51428571)
26) insulin< 14.5 21 8 neg (0.61904762 0.38095238) *
27) insulin>=14.5 14 4 pos (0.28571429 0.71428571) *
7) mass>=29.95 207 57 pos (0.27536232 0.72463768)
14) glucose< 157.5 115 45 pos (0.39130435 0.60869565)
28) age< 30.5 50 23 neg (0.54000000 0.46000000)
56) pressure>=61 40 13 neg (0.67500000 0.32500000)
112) mass< 41.8 31 7 neg (0.77419355 0.22580645) *
113) mass>=41.8 9 3 pos (0.33333333 0.66666667) *
57) pressure< 61 10 0 pos (0.00000000 1.00000000) *
29) age>=30.5 65 18 pos (0.27692308 0.72307692) *
15) glucose>=157.5 92 12 pos (0.13043478 0.86956522) *
> Conf(rp.pima)
Confusion Matrix and Statistics
Reference
Prediction neg pos
neg 449 72
pos 51 196
Total n : 768
Accuracy : 0.8398
95% CI : (0.8122, 0.8641)
No Information Rate : 0.6510
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.6410
Mcnemar's Test P-Value : 0.0713
Sensitivity : 0.8980
Specificity : 0.7313
Pos Pred Value : 0.8618
Neg Pred Value : 0.7935
Prevalence : 0.6510
Detection Rate : 0.6784
Detection Prevalence : 0.5846
Balanced Accuracy : 0.8147
F-val Accuracy : 0.8795
Matthews Cor.-Coef : 0.6422
'Positive' Class : neg
> lines(ROC(rp.pima), col=hblue)
Setting levels: control = neg, case = pos
Setting direction: controls < cases
> # to be improved
> plot(rp.pima, col=SetAlpha(c("blue","red"), 0.4), cex=0.7)
>
>
> # Random Forest
> rf.pima <- FitMod(diabetes ~ ., d.pima, method="class", fitfn="randomForest")
> rf.pima
Call:
randomForest(formula = diabetes ~ ., data = d.pima, method = "class", na.action = function (object, ...) UseMethod("na.omit"))
Type of random forest: classification
Number of trees: 500
No. of variables tried at each split: 2
OOB estimate of error rate: 23.18%
Confusion matrix:
neg pos class.error
neg 430 70 0.1400000
pos 108 160 0.4029851
> Conf(rf.pima)
Confusion Matrix and Statistics
Reference
Prediction neg pos
neg 430 108
pos 70 160
Total n : 768
Accuracy : 0.7682
95% CI : (0.7371, 0.7967)
No Information Rate : 0.6510
P-Value [Acc > NIR] : 1.29e-12
Kappa : 0.4726
Mcnemar's Test P-Value : 0.0055
Sensitivity : 0.8600
Specificity : 0.5970
Pos Pred Value : 0.7993
Neg Pred Value : 0.6957
Prevalence : 0.6510
Detection Rate : 0.7005
Detection Prevalence : 0.5599
Balanced Accuracy : 0.7285
F-val Accuracy : 0.8285
Matthews Cor.-Coef : 0.4756
'Positive' Class : neg
> lines(ROC(r.pima), col=hred)
Setting levels: control = neg, case = pos
Setting direction: controls < cases
>
>
>
> # more models to compare
>
> d.pim <- SplitTrainTest(d.pima, p = 0.2)
> mdiab <- formula(diabetes ~ pregnant + glucose + pressure + triceps
+ + insulin + mass + pedigree + age)
>
> r.glm <- FitMod(mdiab, data=d.pim$train, fitfn="logit")
> r.rp <- FitMod(mdiab, data=d.pim$train, fitfn="rpart")
> r.rf <- FitMod(mdiab, data=d.pim$train, fitfn="randomForest")
> r.svm <- FitMod(mdiab, data=d.pim$train, fitfn="svm")
> r.c5 <- FitMod(mdiab, data=d.pim$train, fitfn="C5.0")
> r.nn <- FitMod(mdiab, data=d.pim$train, fitfn="nnet")
> r.nb <- FitMod(mdiab, data=d.pim$train, fitfn="naive_bayes")
> r.lda <- FitMod(mdiab, data=d.pim$train, fitfn="lda")
> r.qda <- FitMod(mdiab, data=d.pim$train, fitfn="qda")
> r.lb <- FitMod(mdiab, data=d.pim$train, fitfn="lb")
>
> mods <- list(glm=r.glm, rp=r.rp, rf=r.rf, svm=r.svm, c5=r.c5
+ , nn=r.nn, nb=r.nb, lda=r.lda, qda=r.qda, lb=r.lb)
>
> # insight in the Regression tree
> plot(r.rp, box.palette = as.list(Pal("Helsana", alpha = 0.5)))
>
> # Insample accuracy ...
> TModC(mods, ord="auc")
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Setting levels: control = neg, case = pos
Setting direction: controls < cases
Error in if (attr(attr(data, "terms"), "response")) { :
argument is of length zero
Calls: TModC ... sapply -> lapply -> FUN -> model.extract -> model.response
Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-release-windows-x86_64, r-oldrel-windows-x86_64