Last updated on 2026-07-24 20:49:05 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.3-21 | 17.26 | 175.21 | 192.47 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.3-21 | 13.86 | 128.69 | 142.55 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.3-21 | 28.00 | 239.26 | 267.26 | ERROR | |
| r-devel-linux-x86_64-fedora-gcc | 1.3-21 | 13.00 | 107.75 | 120.75 | ERROR | |
| r-devel-windows-x86_64 | 1.3-21 | 30.00 | 224.00 | 254.00 | OK | |
| r-patched-linux-x86_64 | 1.3-21 | 19.45 | 165.13 | 184.58 | OK | |
| r-release-linux-x86_64 | 1.3-21 | 16.05 | 167.30 | 183.35 | OK | |
| r-release-macos-arm64 | 1.3-21 | 5.00 | 51.00 | 56.00 | OK | |
| r-release-macos-x86_64 | 1.3-21 | 15.00 | 149.00 | 164.00 | OK | |
| r-release-windows-x86_64 | 1.3-21 | 32.00 | 220.00 | 252.00 | OK | |
| r-oldrel-macos-arm64 | 1.3-21 | 4.00 | 53.00 | 57.00 | OK | |
| r-oldrel-macos-x86_64 | 1.3-21 | 14.00 | 179.00 | 193.00 | OK | |
| r-oldrel-windows-x86_64 | 1.3-21 | 35.00 | 269.00 | 304.00 | OK |
Version: 1.3-21
Check: examples
Result: 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
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Version: 1.3-21
Check: tests
Result: ERROR
Running ‘Distributions.R’
Comparing ‘Distributions.Rout’ to ‘Distributions.Rout.save’ ... OK
Running ‘LinearStatistic-regtest.R’
Comparing ‘LinearStatistic-regtest.Rout’ to ‘LinearStatistic-regtest.Rout.save’ ... OK
Running ‘Predict-regtest.R’
Comparing ‘Predict-regtest.Rout’ to ‘Predict-regtest.Rout.save’ ... OK
Running ‘RandomForest-regtest.R’ [8s/11s]
Comparing ‘RandomForest-regtest.Rout’ to ‘RandomForest-regtest.Rout.save’ ... OK
Running ‘TestStatistic-regtest.R’
Comparing ‘TestStatistic-regtest.Rout’ to ‘TestStatistic-regtest.Rout.save’ ... OK
Running ‘TreeGrow-regtest.R’ [8s/11s]
Comparing ‘TreeGrow-regtest.Rout’ to ‘TreeGrow-regtest.Rout.save’ ... OK
Running ‘Utils-regtest.R’
Comparing ‘Utils-regtest.Rout’ to ‘Utils-regtest.Rout.save’ ... OK
Running ‘bugfixes.R’ [19s/25s]
Comparing ‘bugfixes.Rout’ to ‘bugfixes.Rout.save’ ... OK
Running ‘mob.R’
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
$`9`
Call:
NULL
Weighted Residuals:
Min 1Q Median 3Q Max
-10.56 0.00 0.00 0.00 24.28
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 68.29709 3.83284 17.819 < 2e-16 ***
lstat -16.35401 0.96577 -16.934 < 2e-16 ***
rm -0.14779 0.05047 -2.928 0.00394 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.689 on 150 degrees of freedom
Multiple R-squared: 0.6649, Adjusted R-squared: 0.6604
F-statistic: 148.8 on 2 and 150 DF, p-value: < 2.2e-16
>
> ### check for one-node tree
> fmBH <- try(mob(medv ~ lstat + rm | zn, control = mob_control(minsplit = 4000, verbose = TRUE),
+ data = BostonHousing, model = linearModel))
> stopifnot(class(fmBH) != "try-error")
>
>
> data("PimaIndiansDiabetes", package = "mlbench")
Warning message:
In data("PimaIndiansDiabetes", package = "mlbench") :
data set 'PimaIndiansDiabetes' not found
> fmPID <- mob(diabetes ~ glucose | pregnant + pressure + triceps + insulin + mass + pedigree + age,
+ control = mob_control(verbose = TRUE),
+ data = PimaIndiansDiabetes, model = glinearModel, family = binomial())
Error: object 'PimaIndiansDiabetes' not found
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.3-21
Check: running R code from vignettes
Result: ERROR
Errors in running code in vignettes:
when running code in ‘MOB.Rnw’
> require("party")
Loading required package: party
Loading required package: grid
Loading required package: mvtnorm
Loading required package: modeltools
Loading required package: stats4
Loading required package: strucchange
Loading required package: zoo
Attaching package: ‘zoo’
The following objects are masked from ‘package:base’:
as.Date, as.Date.numeric
Loading required package: sandwich
> options(useFancyQuotes = FALSE)
> library("party")
> data("BostonHousing", package = "mlbench")
> BostonHousing$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’... OK
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Version: 1.3-21
Check: re-building of vignette outputs
Result: 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 (function (cond) :
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
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Version: 1.3-21
Check: tests
Result: ERROR
Running ‘Distributions.R’
Comparing ‘Distributions.Rout’ to ‘Distributions.Rout.save’ ... OK
Running ‘LinearStatistic-regtest.R’
Comparing ‘LinearStatistic-regtest.Rout’ to ‘LinearStatistic-regtest.Rout.save’ ... OK
Running ‘Predict-regtest.R’
Comparing ‘Predict-regtest.Rout’ to ‘Predict-regtest.Rout.save’ ... OK
Running ‘RandomForest-regtest.R’
Comparing ‘RandomForest-regtest.Rout’ to ‘RandomForest-regtest.Rout.save’ ... OK
Running ‘TestStatistic-regtest.R’
Comparing ‘TestStatistic-regtest.Rout’ to ‘TestStatistic-regtest.Rout.save’ ... OK
Running ‘TreeGrow-regtest.R’
Comparing ‘TreeGrow-regtest.Rout’ to ‘TreeGrow-regtest.Rout.save’ ... OK
Running ‘Utils-regtest.R’
Comparing ‘Utils-regtest.Rout’ to ‘Utils-regtest.Rout.save’ ... OK
Running ‘bugfixes.R’
Comparing ‘bugfixes.Rout’ to ‘bugfixes.Rout.save’ ... OK
Running ‘mob.R’
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
Flavor: r-devel-linux-x86_64-fedora-gcc