* using log directory 'd:/Rcompile/CRANpkg/local/4.6/party.Rcheck' * using R version 4.6.1 (2026-06-24 ucrt) * using platform: x86_64-w64-mingw32 * R was compiled by gcc.exe (GCC) 14.3.0 GNU Fortran (GCC) 14.3.0 * running under: Windows Server 2022 x64 (build 20348) * using session charset: UTF-8 * current time: 2026-08-12 06:51:38 UTC * checking for file 'party/DESCRIPTION' ... OK * this is package 'party' version '1.3-21' * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking whether package 'party' can be installed ... OK * used C compiler: 'gcc.exe (GCC) 14.3.0' * checking installed package size ... OK * checking package directory ... OK * checking 'build' directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking code files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... [2s] OK * checking whether the package can be loaded with stated dependencies ... [2s] OK * checking whether the package can be unloaded cleanly ... [2s] OK * checking whether the namespace can be loaded with stated dependencies ... [2s] OK * checking whether the namespace can be unloaded cleanly ... [2s] OK * checking loading without being on the library search path ... [2s] OK * checking whether startup messages can be suppressed ... [2s] OK * checking use of S3 registration ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... [11s] OK * checking Rd files ... [3s] OK * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking contents of 'data' directory ... OK * checking data for non-ASCII characters ... [0s] OK * checking LazyData ... OK * checking data for ASCII and uncompressed saves ... OK * checking line endings in shell scripts ... OK * checking line endings in C/C++/Fortran sources/headers ... OK * checking line endings in Makefiles ... OK * checking compilation flags in Makevars ... OK * checking for GNU extensions in Makefiles ... OK * checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK * checking use of PKG_*FLAGS in Makefiles ... OK * checking pragmas in C/C++ headers and code ... OK * checking compiled code ... 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[6s] ERROR Running examples in 'party-Ex.R' failed The error most likely occurred in: > ### Name: mob > ### Title: Model-based Recursive Partitioning > ### Aliases: mob mob-class coef.mob deviance.mob fitted.mob logLik.mob > ### predict.mob print.mob residuals.mob sctest.mob summary.mob > ### weights.mob > ### Keywords: tree > > ### ** Examples > > > set.seed(290875) > > if(require("mlbench")) { + + ## recursive partitioning of a linear regression model + ## load data + data("BostonHousing", package = "mlbench") + ## and transform variables appropriately (for a linear regression) + BostonHousing$lstat <- log(BostonHousing$lstat) + BostonHousing$rm <- BostonHousing$rm^2 + ## as well as partitioning variables (for fluctuation testing) + BostonHousing$chas <- factor(BostonHousing$chas, levels = 0:1, + labels = c("no", "yes")) + BostonHousing$rad <- factor(BostonHousing$rad, ordered = TRUE) + + ## partition the linear regression model medv ~ lstat + rm + ## with respect to all remaining variables: + fmBH <- mob(medv ~ lstat + rm | zn + indus + chas + nox + age + + dis + rad + tax + crim + b + ptratio, + control = mob_control(minsplit = 40), data = BostonHousing, + model = linearModel) + + ## print the resulting tree + fmBH + ## or better visualize it + plot(fmBH) + + ## extract coefficients in all terminal nodes + coef(fmBH) + ## look at full summary, e.g., for node 7 + summary(fmBH, node = 7) + ## results of parameter stability tests for that node + sctest(fmBH, node = 7) + ## -> no further significant instabilities (at 5% level) + + ## compute mean squared error (on training data) + mean((BostonHousing$medv - fitted(fmBH))^2) + mean(residuals(fmBH)^2) + deviance(fmBH)/sum(weights(fmBH)) + + ## evaluate logLik and AIC + logLik(fmBH) + AIC(fmBH) + ## (Note that this penalizes estimation of error variances, which + ## were treated as nuisance parameters in the fitting process.) + + + ## recursive partitioning of a logistic regression model + ## load data + data("PimaIndiansDiabetes", package = "mlbench") + ## partition logistic regression diabetes ~ glucose + ## wth respect to all remaining variables + fmPID <- mob(diabetes ~ glucose | pregnant + pressure + triceps + + insulin + mass + pedigree + age, + data = PimaIndiansDiabetes, model = glinearModel, + family = binomial()) + + ## fitted model + coef(fmPID) + plot(fmPID) + plot(fmPID, tp_args = list(cdplot = TRUE)) + } Loading required package: mlbench Warning in data("PimaIndiansDiabetes", package = "mlbench") : data set 'PimaIndiansDiabetes' not found Error: object 'PimaIndiansDiabetes' not found Execution halted * checking for unstated dependencies in 'tests' ... OK * checking tests ... [35s] ERROR Running 'Distributions.R' [2s] Comparing 'Distributions.Rout' to 'Distributions.Rout.save' ... OK Running 'LinearStatistic-regtest.R' [2s] Comparing 'LinearStatistic-regtest.Rout' to 'LinearStatistic-regtest.Rout.save' ... OK Running 'Predict-regtest.R' [3s] Comparing 'Predict-regtest.Rout' to 'Predict-regtest.Rout.save' ... OK Running 'RandomForest-regtest.R' [4s] Comparing 'RandomForest-regtest.Rout' to 'RandomForest-regtest.Rout.save' ... OK Running 'TestStatistic-regtest.R' [2s] Comparing 'TestStatistic-regtest.Rout' to 'TestStatistic-regtest.Rout.save' ... OK Running 'TreeGrow-regtest.R' [4s] Comparing 'TreeGrow-regtest.Rout' to 'TreeGrow-regtest.Rout.save' ... OK Running 'Utils-regtest.R' [2s] Comparing 'Utils-regtest.Rout' to 'Utils-regtest.Rout.save' ... OK Running 'bugfixes.R' [10s] Comparing 'bugfixes.Rout' to 'bugfixes.Rout.save' ... OK Running 'mob.R' [2s] Running the tests in 'tests/mob.R' failed. Complete output: > library("party") Loading required package: grid Loading required package: mvtnorm Loading required package: modeltools Loading required package: stats4 Loading required package: strucchange Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich > > data("BostonHousing", package = "mlbench") > BostonHousing$lstat <- log(BostonHousing$lstat) > BostonHousing$rm <- BostonHousing$rm^2 > BostonHousing$chas <- factor(BostonHousing$chas, levels = 0:1, labels = c("no", "yes")) > BostonHousing$rad <- factor(BostonHousing$rad, ordered = TRUE) > fmBH <- mob(medv ~ lstat + rm | zn + indus + chas + nox + age + dis + rad + tax + crim + b + ptratio, + control = mob_control(minsplit = 40, verbose = TRUE), + data = BostonHousing, model = linearModel) ------------------------------------------- Fluctuation tests of splitting variables: zn indus chas nox age statistic 3.363356e+01 6.532322e+01 2.275635e+01 8.136281e+01 3.675850e+01 p.value 1.023987e-04 1.363602e-11 4.993053e-04 3.489797e-15 2.263798e-05 dis rad tax crim b statistic 6.848533e+01 1.153641e+02 9.068440e+01 8.655065e+01 3.627629e+01 p.value 2.693904e-12 7.087680e-13 2.735524e-17 2.356348e-16 2.860686e-05 ptratio statistic 7.221524e+01 p.value 3.953623e-13 Best splitting variable: tax Perform split? yes ------------------------------------------- Node properties: tax <= 432; criterion = 1, statistic = 115.364 ------------------------------------------- Fluctuation tests of splitting variables: zn indus chas nox age statistic 27.785009791 21.3329346 8.0272421 23.774323202 11.9204284 p.value 0.001494064 0.0285193 0.4005192 0.009518732 0.7666366 dis rad tax crim b statistic 24.268011081 50.481593270 3.523250e+01 3.276813e+01 9.0363245 p.value 0.007601532 0.003437763 4.275527e-05 1.404487e-04 0.9871502 ptratio statistic 4.510680e+01 p.value 3.309747e-07 Best splitting variable: ptratio Perform split? yes ------------------------------------------- Node properties: ptratio <= 15.2; criterion = 1, statistic = 50.482 ------------------------------------------- Fluctuation tests of splitting variables: zn indus chas nox age statistic 3.233350e+01 22.26864036 12.93407112 22.10510234 20.41295354 p.value 1.229678e-04 0.01504788 0.05259509 0.01622098 0.03499731 dis rad tax crim b statistic 17.7204735 5.526565e+01 2.879128e+01 20.28503194 6.5549665 p.value 0.1091769 7.112214e-04 6.916307e-04 0.03706934 0.9999522 ptratio statistic 4.789850e+01 p.value 4.738855e-08 Best splitting variable: ptratio Perform split? yes ------------------------------------------- Node properties: ptratio <= 19.6; criterion = 1, statistic = 55.266 ------------------------------------------- Fluctuation tests of splitting variables: zn indus chas nox age dis statistic 14.971474 14.6477733 7.1172962 14.3455158 8.2176363 16.1112185 p.value 0.280361 0.3134649 0.5405005 0.3467974 0.9906672 0.1847818 rad tax crim b ptratio statistic 43.17824350 3.447271e+01 9.340075 8.7773142 10.8469969 p.value 0.03281124 4.281939e-05 0.952996 0.9772696 0.8202694 Best splitting variable: tax Perform split? yes ------------------------------------------- Node properties: tax <= 265; criterion = 1, statistic = 43.178 ------------------------------------------- Fluctuation tests of splitting variables: zn indus chas nox age dis statistic 11.998039 7.3971233 7.227770 9.2936189 14.3023962 8.9239826 p.value 0.574642 0.9931875 0.522447 0.9119621 0.2886603 0.9389895 rad tax crim b ptratio statistic 33.1746444 16.6666129 11.7143758 9.9050903 11.5927528 p.value 0.3926249 0.1206412 0.6153455 0.8539893 0.6328381 Best splitting variable: tax Perform split? no ------------------------------------------- ------------------------------------------- Fluctuation tests of splitting variables: zn indus chas nox age dis statistic 10.9187926 9.0917078 2.754081e+01 17.39203006 4.6282349 11.9581600 p.value 0.7091039 0.9172303 4.987667e-05 0.08922543 0.9999992 0.5607267 rad tax crim b ptratio statistic 0.2557803 10.9076165 3.711175 3.158329 9.8865054 p.value 1.0000000 0.7106612 1.000000 1.000000 0.8410064 Best splitting variable: chas Perform split? yes ------------------------------------------- Splitting factor variable, objective function: no Inf No admissable split found in 'chas' > fmBH 1) tax <= 432; criterion = 1, statistic = 115.364 2) ptratio <= 15.2; criterion = 1, statistic = 50.482 3)* weights = 72 Terminal node model Linear model with coefficients: (Intercept) lstat rm 9.2349 -4.9391 0.6859 2) ptratio > 15.2 4) ptratio <= 19.6; criterion = 1, statistic = 55.266 5) tax <= 265; criterion = 1, statistic = 43.178 6)* weights = 63 Terminal node model Linear model with coefficients: (Intercept) lstat rm 3.9637 -2.7663 0.6881 5) tax > 265 7)* weights = 162 Terminal node model Linear model with coefficients: (Intercept) lstat rm -1.7984 -0.2677 0.6539 4) ptratio > 19.6 8)* weights = 56 Terminal node model Linear model with coefficients: (Intercept) lstat rm 17.5865 -4.6190 0.3387 1) tax > 432 9)* weights = 153 Terminal node model Linear model with coefficients: (Intercept) lstat rm 68.2971 -16.3540 -0.1478 > summary(fmBH) $`3` Call: NULL Weighted Residuals: Min 1Q Median 3Q Max -7.910 0.000 0.000 0.000 6.632 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 9.23488 3.95128 2.337 0.0223 * lstat -4.93910 0.88285 -5.595 4.14e-07 *** rm 0.68591 0.05136 13.354 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.413 on 69 degrees of freedom Multiple R-squared: 0.922, Adjusted R-squared: 0.9197 F-statistic: 407.8 on 2 and 69 DF, p-value: < 2.2e-16 $`6` Call: NULL Weighted Residuals: Min 1Q Median 3Q Max -4.614 0.000 0.000 0.000 12.473 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.96372 5.00781 0.792 0.43177 lstat -2.76629 1.00406 -2.755 0.00776 ** rm 0.68813 0.07716 8.918 1.36e-12 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.2 on 60 degrees of freedom Multiple R-squared: 0.8176, Adjusted R-squared: 0.8115 F-statistic: 134.5 on 2 and 60 DF, p-value: < 2.2e-16 $`7` Call: NULL Weighted Residuals: Min 1Q Median 3Q Max -9.092 0.000 0.000 0.000 10.236 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.79839 2.84702 -0.632 0.529 lstat -0.26771 0.69581 -0.385 0.701 rm 0.65389 0.03757 17.404 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.652 on 159 degrees of freedom Multiple R-squared: 0.8173, Adjusted R-squared: 0.815 F-statistic: 355.6 on 2 and 159 DF, p-value: < 2.2e-16 $`8` Call: NULL Weighted Residuals: Min 1Q Median 3Q Max -8.466 0.000 0.000 0.000 4.947 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 17.58649 4.21666 4.171 0.000113 *** lstat -4.61897 0.84025 -5.497 1.13e-06 *** rm 0.33867 0.07574 4.472 4.13e-05 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.197 on 53 degrees of freedom Multiple R-squared: 0.6446, Adjusted R-squared: 0.6312 F-statistic: 48.07 on 2 and 53 DF, p-value: 1.238e-12 $`9` Call: NULL Weighted Residuals: Min 1Q Median 3Q Max -10.56 0.00 0.00 0.00 24.28 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 68.29709 3.83284 17.819 < 2e-16 *** lstat -16.35401 0.96577 -16.934 < 2e-16 *** rm -0.14779 0.05047 -2.928 0.00394 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 4.689 on 150 degrees of freedom Multiple R-squared: 0.6649, Adjusted R-squared: 0.6604 F-statistic: 148.8 on 2 and 150 DF, p-value: < 2.2e-16 > > ### check for one-node tree > fmBH <- try(mob(medv ~ lstat + rm | zn, control = mob_control(minsplit = 4000, verbose = TRUE), + data = BostonHousing, model = linearModel)) > stopifnot(class(fmBH) != "try-error") > > > data("PimaIndiansDiabetes", package = "mlbench") Warning message: In data("PimaIndiansDiabetes", package = "mlbench") : data set 'PimaIndiansDiabetes' not found > fmPID <- mob(diabetes ~ glucose | pregnant + pressure + triceps + insulin + mass + pedigree + age, + control = mob_control(verbose = TRUE), + data = PimaIndiansDiabetes, model = glinearModel, family = binomial()) Error: object 'PimaIndiansDiabetes' not found Execution halted * checking for unstated dependencies in vignettes ... OK * checking package vignettes ... OK * checking running R code from vignettes ... [6s] ERROR Errors in running code in vignettes: when running code in 'MOB.Rnw' > require("party") Loading required package: party Loading required package: grid Loading required package: mvtnorm Loading required package: modeltools Loading required package: stats4 Loading required package: strucchange Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich > options(useFancyQuotes = FALSE) > library("party") > data("BostonHousing", package = "mlbench") > BostonHousing$lstat <- log(BostonHousing$lstat) > BostonHousing$rm <- BostonHousing$rm^2 > BostonHousing$chas <- factor(BostonHousing$chas, levels = 0:1, + labels = c("no", "yes")) > BostonHousing$rad <- factor(BostonHousing$rad, ordered = TRUE) > ctrl <- mob_control(alpha = 0.05, bonferroni = TRUE, + minsplit = 40, objfun = deviance, verbose = TRUE) > fmBH <- mob(medv ~ lstat + rm | zn + indus + chas + + nox + age + dis + rad + tax + crim + b + ptratio, data = BostonHousing, + control = .... [TRUNCATED] ------------------------------------------- Fluctuation tests of splitting variables: zn indus chas nox age statistic 3.363356e+01 6.532322e+01 2.275635e+01 8.136281e+01 3.675850e+01 p.value 1.023987e-04 1.363602e-11 4.993053e-04 3.489797e-15 2.263798e-05 dis rad tax crim b statistic 6.848533e+01 1.153641e+02 9.068440e+01 8.655065e+01 3.627629e+01 p.value 2.693904e-12 7.087680e-13 2.735524e-17 2.356348e-16 2.860686e-05 ptratio statistic 7.221524e+01 p.value 3.953623e-13 Best splitting variable: tax Perform split? yes ------------------------------------------- Node properties: tax <= 432; criterion = 1, statistic = 115.364 ------------------------------------------- Fluctuation tests of splitting variables: zn indus chas nox age statistic 27.785009791 21.3329346 8.0272421 23.774323202 11.9204284 p.value 0.001494064 0.0285193 0.4005192 0.009518732 0.7666366 dis rad tax crim b statistic 24.268011081 50.481593270 3.523250e+01 3.276813e+01 9.0363245 p.value 0.007601532 0.003437763 4.275527e-05 1.404487e-04 0.9871502 ptratio statistic 4.510680e+01 p.value 3.309747e-07 Best splitting variable: ptratio Perform split? yes ------------------------------------------- Node properties: ptratio <= 15.2; criterion = 1, statistic = 50.482 ------------------------------------------- Fluctuation tests of splitting variables: zn indus chas nox age statistic 3.233350e+01 22.26864036 12.93407112 22.10510234 20.41295354 p.value 1.229678e-04 0.01504788 0.05259509 0.01622098 0.03499731 dis rad tax crim b statistic 17.7204735 5.526565e+01 2.879128e+01 20.28503194 6.5549665 p.value 0.1091769 7.112214e-04 6.916307e-04 0.03706934 0.9999522 ptratio statistic 4.789850e+01 p.value 4.738855e-08 Best splitting variable: ptratio Perform split? yes ------------------------------------------- Node properties: ptratio <= 19.6; criterion = 1, statistic = 55.266 ------------------------------------------- Fluctuation tests of splitting variables: zn indus chas nox age dis statistic 14.971474 14.6477733 7.1172962 14.3455158 8.2176363 16.1112185 p.value 0.280361 0.3134649 0.5405005 0.3467974 0.9906672 0.1847818 rad tax crim b ptratio statistic 43.17824350 3.447271e+01 9.340075 8.7773142 10.8469969 p.value 0.03281124 4.281939e-05 0.952996 0.9772696 0.8202694 Best splitting variable: tax Perform split? yes ------------------------------------------- Node properties: tax <= 265; criterion = 1, statistic = 43.178 ------------------------------------------- Fluctuation tests of splitting variables: zn indus chas nox age dis statistic 11.998039 7.3971233 7.227770 9.2936189 14.3023962 8.9239826 p.value 0.574642 0.9931875 0.522447 0.9119621 0.2886603 0.9389895 rad tax crim b ptratio statistic 33.1746444 16.6666129 11.7143758 9.9050903 11.5927528 p.value 0.3926249 0.1206412 0.6153455 0.8539893 0.6328381 Best splitting variable: tax Perform split? no ------------------------------------------- ------------------------------------------- Fluctuation tests of splitting variables: zn indus chas nox age dis statistic 10.9187926 9.0917078 2.754081e+01 17.39203006 4.6282349 11.9581600 p.value 0.7091039 0.9172303 4.987667e-05 0.08922543 0.9999992 0.5607267 rad tax crim b ptratio statistic 0.2557803 10.9076165 3.711175 3.158329 9.8865054 p.value 1.0000000 0.7106612 1.000000 1.000000 0.8410064 Best splitting variable: chas Perform split? yes ------------------------------------------- Splitting factor variable, objective function: no Inf No admissable split found in 'chas' > fmBH 1) tax <= 432; criterion = 1, statistic = 115.364 2) ptratio <= 15.2; criterion = 1, statistic = 50.482 3)* weights = 72 Terminal node model Linear model with coefficients: (Intercept) lstat rm 9.2349 -4.9391 0.6859 2) ptratio > 15.2 4) ptratio <= 19.6; criterion = 1, statistic = 55.266 5) tax <= 265; criterion = 1, statistic = 43.178 6)* weights = 63 Terminal node model Linear model with coefficients: (Intercept) lstat rm 3.9637 -2.7663 0.6881 5) tax > 265 7)* weights = 162 Terminal node model Linear model with coefficients: (Intercept) lstat rm -1.7984 -0.2677 0.6539 4) ptratio > 19.6 8)* weights = 56 Terminal node model Linear model with coefficients: (Intercept) lstat rm 17.5865 -4.6190 0.3387 1) tax > 432 9)* weights = 153 Terminal node model Linear model with coefficients: (Intercept) lstat rm 68.2971 -16.3540 -0.1478 > plot(fmBH) > coef(fmBH) (Intercept) lstat rm 3 9.234880 -4.939096 0.6859136 6 3.963720 -2.766287 0.6881287 7 -1.798387 -0.267707 0.6538864 8 17.586490 -4.618975 0.3386744 9 68.297087 -16.354006 -0.1477939 > summary(fmBH, node = 7) Call: NULL Weighted Residuals: Min 1Q Median 3Q Max -9.092 0.000 0.000 0.000 10.236 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.79839 2.84702 -0.632 0.529 lstat -0.26771 0.69581 -0.385 0.701 rm 0.65389 0.03757 17.404 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.652 on 159 degrees of freedom Multiple R-squared: 0.8173, Adjusted R-squared: 0.815 F-statistic: 355.6 on 2 and 159 DF, p-value: < 2.2e-16 > sctest(fmBH, node = 7) zn indus chas nox age dis statistic 11.998039 7.3971233 7.227770 9.2936189 14.3023962 8.9239826 p.value 0.574642 0.9931875 0.522447 0.9119621 0.2886603 0.9389895 rad tax crim b ptratio statistic 33.1746444 16.6666129 11.7143758 9.9050903 11.5927528 p.value 0.3926249 0.1206412 0.6153455 0.8539893 0.6328381 > mean(residuals(fmBH)^2) [1] 12.03518 > logLik(fmBH) 'log Lik.' -1310.506 (df=24) > AIC(fmBH) [1] 2669.013 > nt <- NROW(coef(fmBH)) > nk <- NCOL(coef(fmBH)) > data("PimaIndiansDiabetes2", package = "mlbench") Warning in data("PimaIndiansDiabetes2", package = "mlbench") : data set 'PimaIndiansDiabetes2' not found > PimaIndiansDiabetes <- na.omit(PimaIndiansDiabetes2[, + -c(4, 5)]) When sourcing 'MOB.R': Error: error in evaluating the argument 'object' in selecting a method for function 'na.omit': object 'PimaIndiansDiabetes2' not found Execution halted 'MOB.Rnw'... failed 'party.Rnw'... [3s] OK * checking re-building of vignette outputs ... [31s] ERROR Error(s) in re-building vignettes: --- re-building 'MOB.Rnw' using Sweave Loading required package: party Loading required package: grid Loading required package: mvtnorm Loading required package: modeltools Loading required package: stats4 Loading required package: strucchange Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich Warning in data("PimaIndiansDiabetes2", package = "mlbench") : data set 'PimaIndiansDiabetes2' not found Error: processing vignette 'MOB.Rnw' failed with diagnostics: chunk 16 Error in h(simpleError(msg, call)) : error in evaluating the argument 'object' in selecting a method for function 'na.omit': object 'PimaIndiansDiabetes2' not found --- failed re-building 'MOB.Rnw' --- re-building 'party.Rnw' using Sweave Loading required package: party Loading required package: grid Loading required package: mvtnorm Loading required package: modeltools Loading required package: stats4 Loading required package: strucchange Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich Loading required package: coin Loading required package: survival --- finished re-building 'party.Rnw' SUMMARY: processing the following file failed: 'MOB.Rnw' Error: Vignette re-building failed. Execution halted * checking PDF version of manual ... [21s] OK * checking HTML version of manual ... [8s] OK * DONE Status: 4 ERRORs Check process probably crashed or hung up for 20 minutes ... killed Most likely this happened in the example checks (?), if not, ignore the following last lines of example output: + mean(residuals(fmBH)^2) + deviance(fmBH)/sum(weights(fmBH)) + + ## evaluate logLik and AIC + logLik(fmBH) + AIC(fmBH) + ## (Note that this penalizes estimation of error variances, which + ## were treated as nuisance parameters in the fitting process.) + + + ## recursive partitioning of a logistic regression model + ## load data + data("PimaIndiansDiabetes", package = "mlbench") + ## partition logistic regression diabetes ~ glucose + ## wth respect to all remaining variables + fmPID <- mob(diabetes ~ glucose | pregnant + pressure + triceps + + insulin + mass + pedigree + age, + data = PimaIndiansDiabetes, model = glinearModel, + family = binomial()) + + ## fitted model + coef(fmPID) + plot(fmPID) + plot(fmPID, tp_args = list(cdplot = TRUE)) + } Loading required package: mlbench Warning in data("PimaIndiansDiabetes", package = "mlbench") : data set 'PimaIndiansDiabetes' not found Error: object 'PimaIndiansDiabetes' not found Execution halted ======== End of example output (where/before crash/hang up occured ?) ========