CRAN Package Check Results for Package ModTools

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

Check Details

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