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Last updated on 2026-08-10 10:50:37 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.3-21 | 17.79 | 154.60 | 172.39 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 1.3-21 | 13.97 | 114.75 | 128.72 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.3-21 | 13.00 | 105.79 | 118.79 | ERROR | |
| r-devel-linux-x86_64-fedora-gcc | 1.3-21 | 13.00 | 111.56 | 124.56 | ERROR | |
| r-devel-windows-x86_64 | 1.3-21 | 30.00 | 178.00 | 208.00 | ERROR | |
| r-patched-linux-x86_64 | 1.3-21 | 17.54 | 145.17 | 162.71 | ERROR | |
| r-release-linux-x86_64 | 1.3-21 | 16.95 | 145.08 | 162.03 | ERROR | |
| 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 | 31.00 | 188.00 | 219.00 | ERROR | |
| 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 | 37.00 | 218.00 | 255.00 | ERROR |
Version: 1.3-21
Check: examples
Result: ERROR
Running examples in ‘party-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### 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-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-release-linux-x86_64
Version: 1.3-21
Check: tests
Result: ERROR
Running ‘Distributions.R’ [2s/3s]
Comparing ‘Distributions.Rout’ to ‘Distributions.Rout.save’ ... OK
Running ‘LinearStatistic-regtest.R’ [2s/3s]
Comparing ‘LinearStatistic-regtest.Rout’ to ‘LinearStatistic-regtest.Rout.save’ ... OK
Running ‘Predict-regtest.R’ [4s/5s]
Comparing ‘Predict-regtest.Rout’ to ‘Predict-regtest.Rout.save’ ... OK
Running ‘RandomForest-regtest.R’ [5s/6s]
Comparing ‘RandomForest-regtest.Rout’ to ‘RandomForest-regtest.Rout.save’ ...
5,6c5,6
< Warning messages:
< 1: In RNGkind("Mersenne-Twister", "Inversion", "Rounding", "Buggy BTPE") :
---
> Warning message:
> In RNGkind("Mersenne-Twister", "Inversion", "Rounding") :
8,9d7
< 2: In RNGkind("Mersenne-Twister", "Inversion", "Rounding", "Buggy BTPE") :
< Buggy BTPE algorithm used for rbinom()
Running ‘TestStatistic-regtest.R’ [2s/3s]
Comparing ‘TestStatistic-regtest.Rout’ to ‘TestStatistic-regtest.Rout.save’ ... OK
Running ‘TreeGrow-regtest.R’ [5s/6s]
Comparing ‘TreeGrow-regtest.Rout’ to ‘TreeGrow-regtest.Rout.save’ ... OK
Running ‘Utils-regtest.R’ [3s/3s]
Comparing ‘Utils-regtest.Rout’ to ‘Utils-regtest.Rout.save’ ... OK
Running ‘bugfixes.R’ [12s/15s]
Comparing ‘bugfixes.Rout’ to ‘bugfixes.Rout.save’ ...
5,6c5,6
< Warning messages:
< 1: In RNGkind("Mersenne-Twister", "Inversion", "Rounding", "Buggy BTPE") :
---
> Warning message:
> In RNGkind("Mersenne-Twister", "Inversion", "Rounding") :
8,9d7
< 2: In RNGkind("Mersenne-Twister", "Inversion", "Rounding", "Buggy BTPE") :
< Buggy BTPE algorithm used for rbinom()
Running ‘mob.R’ [3s/3s]
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
$`9`
Call:
NULL
Weighted Residuals:
Min 1Q Median 3Q Max
-10.56 0.00 0.00 0.00 24.28
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 68.29709 3.83284 17.819 < 2e-16 ***
lstat -16.35401 0.96577 -16.934 < 2e-16 ***
rm -0.14779 0.05047 -2.928 0.00394 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.689 on 150 degrees of freedom
Multiple R-squared: 0.6649, Adjusted R-squared: 0.6604
F-statistic: 148.8 on 2 and 150 DF, p-value: < 2.2e-16
>
> ### check for one-node tree
> fmBH <- try(mob(medv ~ lstat + rm | zn, control = mob_control(minsplit = 4000, verbose = TRUE),
+ data = BostonHousing, model = linearModel))
> stopifnot(class(fmBH) != "try-error")
>
>
> data("PimaIndiansDiabetes", package = "mlbench")
Warning message:
In data("PimaIndiansDiabetes", package = "mlbench") :
data set 'PimaIndiansDiabetes' not found
> fmPID <- mob(diabetes ~ glucose | pregnant + pressure + triceps + insulin + mass + pedigree + age,
+ control = mob_control(verbose = TRUE),
+ data = PimaIndiansDiabetes, model = glinearModel, family = binomial())
Error: object 'PimaIndiansDiabetes' not found
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 1.3-21
Check: running R code from vignettes
Result: ERROR
Errors in running code in vignettes:
when running code in ‘MOB.Rnw’
...
> require("party")
Loading required package: party
Loading required package: grid
Loading required package: mvtnorm
Loading required package: modeltools
Loading required package: stats4
Loading required package: strucchange
Loading required package: zoo
Attaching package: ‘zoo’
The following objects are masked from ‘package:base’:
as.Date, as.Date.numeric
Loading required package: sandwich
> options(useFancyQuotes = FALSE)
> library("party")
> data("BostonHousing", package = "mlbench")
> BostonHousing$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’... [3s/4s] OK
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-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-debian-clang, r-devel-linux-x86_64-debian-gcc
Version: 1.3-21
Check: tests
Result: ERROR
Running ‘Distributions.R’ [2s/2s]
Comparing ‘Distributions.Rout’ to ‘Distributions.Rout.save’ ... OK
Running ‘LinearStatistic-regtest.R’ [2s/2s]
Comparing ‘LinearStatistic-regtest.Rout’ to ‘LinearStatistic-regtest.Rout.save’ ... OK
Running ‘Predict-regtest.R’ [2s/3s]
Comparing ‘Predict-regtest.Rout’ to ‘Predict-regtest.Rout.save’ ... OK
Running ‘RandomForest-regtest.R’ [4s/4s]
Comparing ‘RandomForest-regtest.Rout’ to ‘RandomForest-regtest.Rout.save’ ...
5,6c5,6
< Warning messages:
< 1: In RNGkind("Mersenne-Twister", "Inversion", "Rounding", "Buggy BTPE") :
---
> Warning message:
> In RNGkind("Mersenne-Twister", "Inversion", "Rounding") :
8,9d7
< 2: In RNGkind("Mersenne-Twister", "Inversion", "Rounding", "Buggy BTPE") :
< Buggy BTPE algorithm used for rbinom()
Running ‘TestStatistic-regtest.R’ [2s/2s]
Comparing ‘TestStatistic-regtest.Rout’ to ‘TestStatistic-regtest.Rout.save’ ... OK
Running ‘TreeGrow-regtest.R’ [4s/4s]
Comparing ‘TreeGrow-regtest.Rout’ to ‘TreeGrow-regtest.Rout.save’ ... OK
Running ‘Utils-regtest.R’ [2s/2s]
Comparing ‘Utils-regtest.Rout’ to ‘Utils-regtest.Rout.save’ ... OK
Running ‘bugfixes.R’ [8s/9s]
Comparing ‘bugfixes.Rout’ to ‘bugfixes.Rout.save’ ...
5,6c5,6
< Warning messages:
< 1: In RNGkind("Mersenne-Twister", "Inversion", "Rounding", "Buggy BTPE") :
---
> Warning message:
> In RNGkind("Mersenne-Twister", "Inversion", "Rounding") :
8,9d7
< 2: In RNGkind("Mersenne-Twister", "Inversion", "Rounding", "Buggy BTPE") :
< Buggy BTPE algorithm used for rbinom()
Running ‘mob.R’ [2s/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
$`9`
Call:
NULL
Weighted Residuals:
Min 1Q Median 3Q Max
-10.56 0.00 0.00 0.00 24.28
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 68.29709 3.83284 17.819 < 2e-16 ***
lstat -16.35401 0.96577 -16.934 < 2e-16 ***
rm -0.14779 0.05047 -2.928 0.00394 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.689 on 150 degrees of freedom
Multiple R-squared: 0.6649, Adjusted R-squared: 0.6604
F-statistic: 148.8 on 2 and 150 DF, p-value: < 2.2e-16
>
> ### check for one-node tree
> fmBH <- try(mob(medv ~ lstat + rm | zn, control = mob_control(minsplit = 4000, verbose = TRUE),
+ data = BostonHousing, model = linearModel))
> stopifnot(class(fmBH) != "try-error")
>
>
> data("PimaIndiansDiabetes", package = "mlbench")
Warning message:
In data("PimaIndiansDiabetes", package = "mlbench") :
data set 'PimaIndiansDiabetes' not found
> fmPID <- mob(diabetes ~ glucose | pregnant + pressure + triceps + insulin + mass + pedigree + age,
+ control = mob_control(verbose = TRUE),
+ data = PimaIndiansDiabetes, model = glinearModel, family = binomial())
Error: object 'PimaIndiansDiabetes' not found
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.3-21
Check: examples
Result: ERROR
Running examples in ‘party-Ex.R’ failed
The error most likely occurred in:
> ### Name: mob
> ### Title: Model-based Recursive Partitioning
> ### Aliases: mob mob-class coef.mob deviance.mob fitted.mob logLik.mob
> ### predict.mob print.mob residuals.mob sctest.mob summary.mob
> ### weights.mob
> ### Keywords: tree
>
> ### ** Examples
>
>
> set.seed(290875)
>
> if(require("mlbench")) {
+
+ ## recursive partitioning of a linear regression model
+ ## load data
+ data("BostonHousing", package = "mlbench")
+ ## and transform variables appropriately (for a linear regression)
+ BostonHousing$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, r-devel-windows-x86_64, r-release-windows-x86_64, r-oldrel-windows-x86_64
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’ ...
5,6c5,6
< Warning messages:
< 1: In RNGkind("Mersenne-Twister", "Inversion", "Rounding", "Buggy BTPE") :
---
> Warning message:
> In RNGkind("Mersenne-Twister", "Inversion", "Rounding") :
8,9d7
< 2: In RNGkind("Mersenne-Twister", "Inversion", "Rounding", "Buggy BTPE") :
< Buggy BTPE algorithm used for rbinom()
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’ ...
5,6c5,6
< Warning messages:
< 1: In RNGkind("Mersenne-Twister", "Inversion", "Rounding", "Buggy BTPE") :
---
> Warning message:
> In RNGkind("Mersenne-Twister", "Inversion", "Rounding") :
8,9d7
< 2: In RNGkind("Mersenne-Twister", "Inversion", "Rounding", "Buggy BTPE") :
< Buggy BTPE algorithm used for rbinom()
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
$`9`
Call:
NULL
Weighted Residuals:
Min 1Q Median 3Q Max
-10.56 0.00 0.00 0.00 24.28
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 68.29709 3.83284 17.819 < 2e-16 ***
lstat -16.35401 0.96577 -16.934 < 2e-16 ***
rm -0.14779 0.05047 -2.928 0.00394 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.689 on 150 degrees of freedom
Multiple R-squared: 0.6649, Adjusted R-squared: 0.6604
F-statistic: 148.8 on 2 and 150 DF, p-value: < 2.2e-16
>
> ### check for one-node tree
> fmBH <- try(mob(medv ~ lstat + rm | zn, control = mob_control(minsplit = 4000, verbose = TRUE),
+ data = BostonHousing, model = linearModel))
> stopifnot(class(fmBH) != "try-error")
>
>
> data("PimaIndiansDiabetes", package = "mlbench")
Warning message:
In data("PimaIndiansDiabetes", package = "mlbench") :
data set 'PimaIndiansDiabetes' not found
> fmPID <- mob(diabetes ~ glucose | pregnant + pressure + triceps + insulin + mass + pedigree + age,
+ control = mob_control(verbose = TRUE),
+ data = PimaIndiansDiabetes, model = glinearModel, family = binomial())
Error: object 'PimaIndiansDiabetes' not found
Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Version: 1.3-21
Check: running R code from vignettes
Result: ERROR
Errors in running code in vignettes:
when running code in ‘MOB.Rnw’
> require("party")
Loading required package: party
Loading required package: grid
Loading required package: mvtnorm
Loading required package: modeltools
Loading required package: stats4
Loading required package: strucchange
Loading required package: zoo
Attaching package: ‘zoo’
The following objects are masked from ‘package:base’:
as.Date, as.Date.numeric
Loading required package: sandwich
> options(useFancyQuotes = FALSE)
> library("party")
> data("BostonHousing", package = "mlbench")
> BostonHousing$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, r-devel-windows-x86_64
Version: 1.3-21
Check: tests
Result: 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' ...
5,6c5,6
< Warning messages:
< 1: In RNGkind("Mersenne-Twister", "Inversion", "Rounding", "Buggy BTPE") :
---
> Warning message:
> In RNGkind("Mersenne-Twister", "Inversion", "Rounding") :
8,9d7
< 2: In RNGkind("Mersenne-Twister", "Inversion", "Rounding", "Buggy BTPE") :
< Buggy BTPE algorithm used for rbinom()
Running 'TestStatistic-regtest.R' [2s]
Comparing 'TestStatistic-regtest.Rout' to 'TestStatistic-regtest.Rout.save' ... OK
Running 'TreeGrow-regtest.R' [5s]
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' [11s]
Comparing 'bugfixes.Rout' to 'bugfixes.Rout.save' ...
5,6c5,6
< Warning messages:
< 1: In RNGkind("Mersenne-Twister", "Inversion", "Rounding", "Buggy BTPE") :
---
> Warning message:
> In RNGkind("Mersenne-Twister", "Inversion", "Rounding") :
8,9d7
< 2: In RNGkind("Mersenne-Twister", "Inversion", "Rounding", "Buggy BTPE") :
< Buggy BTPE algorithm used for rbinom()
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
$`9`
Call:
NULL
Weighted Residuals:
Min 1Q Median 3Q Max
-10.56 0.00 0.00 0.00 24.28
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 68.29709 3.83284 17.819 < 2e-16 ***
lstat -16.35401 0.96577 -16.934 < 2e-16 ***
rm -0.14779 0.05047 -2.928 0.00394 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.689 on 150 degrees of freedom
Multiple R-squared: 0.6649, Adjusted R-squared: 0.6604
F-statistic: 148.8 on 2 and 150 DF, p-value: < 2.2e-16
>
> ### check for one-node tree
> fmBH <- try(mob(medv ~ lstat + rm | zn, control = mob_control(minsplit = 4000, verbose = TRUE),
+ data = BostonHousing, model = linearModel))
> stopifnot(class(fmBH) != "try-error")
>
>
> data("PimaIndiansDiabetes", package = "mlbench")
Warning message:
In data("PimaIndiansDiabetes", package = "mlbench") :
data set 'PimaIndiansDiabetes' not found
> fmPID <- mob(diabetes ~ glucose | pregnant + pressure + triceps + insulin + mass + pedigree + age,
+ control = mob_control(verbose = TRUE),
+ data = PimaIndiansDiabetes, model = glinearModel, family = binomial())
Error: object 'PimaIndiansDiabetes' not found
Execution halted
Flavor: r-devel-windows-x86_64
Version: 1.3-21
Check: running R code from vignettes
Result: ERROR
Errors in running code in vignettes:
when running code in 'MOB.Rnw'
> require("party")
Loading required package: party
Loading required package: grid
Loading required package: mvtnorm
Loading required package: modeltools
Loading required package: stats4
Loading required package: strucchange
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: sandwich
> options(useFancyQuotes = FALSE)
> library("party")
> data("BostonHousing", package = "mlbench")
> BostonHousing$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'... [3s] OK
Flavors: r-devel-windows-x86_64, r-release-windows-x86_64
Version: 1.3-21
Check: tests
Result: ERROR
Running ‘Distributions.R’ [2s/3s]
Comparing ‘Distributions.Rout’ to ‘Distributions.Rout.save’ ... OK
Running ‘LinearStatistic-regtest.R’ [2s/3s]
Comparing ‘LinearStatistic-regtest.Rout’ to ‘LinearStatistic-regtest.Rout.save’ ... OK
Running ‘Predict-regtest.R’ [4s/5s]
Comparing ‘Predict-regtest.Rout’ to ‘Predict-regtest.Rout.save’ ... OK
Running ‘RandomForest-regtest.R’ [5s/7s]
Comparing ‘RandomForest-regtest.Rout’ to ‘RandomForest-regtest.Rout.save’ ... OK
Running ‘TestStatistic-regtest.R’ [2s/3s]
Comparing ‘TestStatistic-regtest.Rout’ to ‘TestStatistic-regtest.Rout.save’ ... OK
Running ‘TreeGrow-regtest.R’ [5s/6s]
Comparing ‘TreeGrow-regtest.Rout’ to ‘TreeGrow-regtest.Rout.save’ ... OK
Running ‘Utils-regtest.R’ [2s/3s]
Comparing ‘Utils-regtest.Rout’ to ‘Utils-regtest.Rout.save’ ... OK
Running ‘bugfixes.R’ [11s/14s]
Comparing ‘bugfixes.Rout’ to ‘bugfixes.Rout.save’ ... OK
Running ‘mob.R’ [3s/3s]
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
$`9`
Call:
NULL
Weighted Residuals:
Min 1Q Median 3Q Max
-10.56 0.00 0.00 0.00 24.28
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 68.29709 3.83284 17.819 < 2e-16 ***
lstat -16.35401 0.96577 -16.934 < 2e-16 ***
rm -0.14779 0.05047 -2.928 0.00394 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.689 on 150 degrees of freedom
Multiple R-squared: 0.6649, Adjusted R-squared: 0.6604
F-statistic: 148.8 on 2 and 150 DF, p-value: < 2.2e-16
>
> ### check for one-node tree
> fmBH <- try(mob(medv ~ lstat + rm | zn, control = mob_control(minsplit = 4000, verbose = TRUE),
+ data = BostonHousing, model = linearModel))
> stopifnot(class(fmBH) != "try-error")
>
>
> data("PimaIndiansDiabetes", package = "mlbench")
Warning message:
In data("PimaIndiansDiabetes", package = "mlbench") :
data set 'PimaIndiansDiabetes' not found
> fmPID <- mob(diabetes ~ glucose | pregnant + pressure + triceps + insulin + mass + pedigree + age,
+ control = mob_control(verbose = TRUE),
+ data = PimaIndiansDiabetes, model = glinearModel, family = binomial())
Error: object 'PimaIndiansDiabetes' not found
Execution halted
Flavor: r-patched-linux-x86_64
Version: 1.3-21
Check: running R code from vignettes
Result: ERROR
Errors in running code in vignettes:
when running code in ‘MOB.Rnw’
...
> require("party")
Loading required package: party
Loading required package: grid
Loading required package: mvtnorm
Loading required package: modeltools
Loading required package: stats4
Loading required package: strucchange
Loading required package: zoo
Attaching package: ‘zoo’
The following objects are masked from ‘package:base’:
as.Date, as.Date.numeric
Loading required package: sandwich
> options(useFancyQuotes = FALSE)
> library("party")
> data("BostonHousing", package = "mlbench")
> BostonHousing$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’... [4s/4s] OK
Flavor: r-patched-linux-x86_64
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 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
Flavors: r-patched-linux-x86_64, r-release-linux-x86_64
Version: 1.3-21
Check: tests
Result: ERROR
Running ‘Distributions.R’ [2s/3s]
Comparing ‘Distributions.Rout’ to ‘Distributions.Rout.save’ ... OK
Running ‘LinearStatistic-regtest.R’ [2s/3s]
Comparing ‘LinearStatistic-regtest.Rout’ to ‘LinearStatistic-regtest.Rout.save’ ... OK
Running ‘Predict-regtest.R’ [4s/4s]
Comparing ‘Predict-regtest.Rout’ to ‘Predict-regtest.Rout.save’ ... OK
Running ‘RandomForest-regtest.R’ [5s/6s]
Comparing ‘RandomForest-regtest.Rout’ to ‘RandomForest-regtest.Rout.save’ ... OK
Running ‘TestStatistic-regtest.R’ [2s/3s]
Comparing ‘TestStatistic-regtest.Rout’ to ‘TestStatistic-regtest.Rout.save’ ... OK
Running ‘TreeGrow-regtest.R’ [5s/5s]
Comparing ‘TreeGrow-regtest.Rout’ to ‘TreeGrow-regtest.Rout.save’ ... OK
Running ‘Utils-regtest.R’ [2s/4s]
Comparing ‘Utils-regtest.Rout’ to ‘Utils-regtest.Rout.save’ ... OK
Running ‘bugfixes.R’ [11s/12s]
Comparing ‘bugfixes.Rout’ to ‘bugfixes.Rout.save’ ... OK
Running ‘mob.R’ [3s/3s]
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
$`9`
Call:
NULL
Weighted Residuals:
Min 1Q Median 3Q Max
-10.56 0.00 0.00 0.00 24.28
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 68.29709 3.83284 17.819 < 2e-16 ***
lstat -16.35401 0.96577 -16.934 < 2e-16 ***
rm -0.14779 0.05047 -2.928 0.00394 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.689 on 150 degrees of freedom
Multiple R-squared: 0.6649, Adjusted R-squared: 0.6604
F-statistic: 148.8 on 2 and 150 DF, p-value: < 2.2e-16
>
> ### check for one-node tree
> fmBH <- try(mob(medv ~ lstat + rm | zn, control = mob_control(minsplit = 4000, verbose = TRUE),
+ data = BostonHousing, model = linearModel))
> stopifnot(class(fmBH) != "try-error")
>
>
> data("PimaIndiansDiabetes", package = "mlbench")
Warning message:
In data("PimaIndiansDiabetes", package = "mlbench") :
data set 'PimaIndiansDiabetes' not found
> fmPID <- mob(diabetes ~ glucose | pregnant + pressure + triceps + insulin + mass + pedigree + age,
+ control = mob_control(verbose = TRUE),
+ data = PimaIndiansDiabetes, model = glinearModel, family = binomial())
Error: object 'PimaIndiansDiabetes' not found
Execution halted
Flavor: r-release-linux-x86_64
Version: 1.3-21
Check: running R code from vignettes
Result: ERROR
Errors in running code in vignettes:
when running code in ‘MOB.Rnw’
...
> require("party")
Loading required package: party
Loading required package: grid
Loading required package: mvtnorm
Loading required package: modeltools
Loading required package: stats4
Loading required package: strucchange
Loading required package: zoo
Attaching package: ‘zoo’
The following objects are masked from ‘package:base’:
as.Date, as.Date.numeric
Loading required package: sandwich
> options(useFancyQuotes = FALSE)
> library("party")
> data("BostonHousing", package = "mlbench")
> BostonHousing$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’... [3s/5s] OK
Flavor: r-release-linux-x86_64
Version: 1.3-21
Check: tests
Result: 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' [5s]
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' [3s]
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-release-windows-x86_64
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 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
Flavors: r-release-windows-x86_64, r-oldrel-windows-x86_64
Version: 1.3-21
Check: tests
Result: ERROR
Running 'Distributions.R' [3s]
Comparing 'Distributions.Rout' to 'Distributions.Rout.save' ... OK
Running 'LinearStatistic-regtest.R' [3s]
Comparing 'LinearStatistic-regtest.Rout' to 'LinearStatistic-regtest.Rout.save' ... OK
Running 'Predict-regtest.R' [5s]
Comparing 'Predict-regtest.Rout' to 'Predict-regtest.Rout.save' ... OK
Running 'RandomForest-regtest.R' [7s]
Comparing 'RandomForest-regtest.Rout' to 'RandomForest-regtest.Rout.save' ... OK
Running 'TestStatistic-regtest.R' [3s]
Comparing 'TestStatistic-regtest.Rout' to 'TestStatistic-regtest.Rout.save' ... OK
Running 'TreeGrow-regtest.R' [6s]
Comparing 'TreeGrow-regtest.Rout' to 'TreeGrow-regtest.Rout.save' ... OK
Running 'Utils-regtest.R' [3s]
Comparing 'Utils-regtest.Rout' to 'Utils-regtest.Rout.save' ... OK
Running 'bugfixes.R' [13s]
Comparing 'bugfixes.Rout' to 'bugfixes.Rout.save' ... OK
Running 'mob.R' [3s]
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
$`9`
Call:
NULL
Weighted Residuals:
Min 1Q Median 3Q Max
-10.56 0.00 0.00 0.00 24.28
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 68.29709 3.83284 17.819 < 2e-16 ***
lstat -16.35401 0.96577 -16.934 < 2e-16 ***
rm -0.14779 0.05047 -2.928 0.00394 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.689 on 150 degrees of freedom
Multiple R-squared: 0.6649, Adjusted R-squared: 0.6604
F-statistic: 148.8 on 2 and 150 DF, p-value: < 2.2e-16
>
> ### check for one-node tree
> fmBH <- try(mob(medv ~ lstat + rm | zn, control = mob_control(minsplit = 4000, verbose = TRUE),
+ data = BostonHousing, model = linearModel))
> stopifnot(class(fmBH) != "try-error")
>
>
> data("PimaIndiansDiabetes", package = "mlbench")
Warning message:
In data("PimaIndiansDiabetes", package = "mlbench") :
data set 'PimaIndiansDiabetes' not found
> fmPID <- mob(diabetes ~ glucose | pregnant + pressure + triceps + insulin + mass + pedigree + age,
+ control = mob_control(verbose = TRUE),
+ data = PimaIndiansDiabetes, model = glinearModel, family = binomial())
Error: object 'PimaIndiansDiabetes' not found
Execution halted
Flavor: r-oldrel-windows-x86_64
Version: 1.3-21
Check: running R code from vignettes
Result: ERROR
Errors in running code in vignettes:
when running code in 'MOB.Rnw'
> require("party")
Loading required package: party
Loading required package: grid
Loading required package: mvtnorm
Loading required package: modeltools
Loading required package: stats4
Loading required package: strucchange
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: sandwich
> options(useFancyQuotes = FALSE)
> library("party")
> data("BostonHousing", package = "mlbench")
> BostonHousing$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'... [4s] OK
Flavor: r-oldrel-windows-x86_64
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.