* using log directory 'd:/Rcompile/CRANpkg/local/4.6/partykit.Rcheck'
* using R version 4.6.1 (2026-06-24 ucrt)
* using platform: x86_64-w64-mingw32
* R was compiled by
    gcc.exe (GCC) 14.3.0
    GNU Fortran (GCC) 14.3.0
* running under: Windows Server 2022 x64 (build 20348)
* using session charset: UTF-8
* current time: 2026-08-12 06:51:40 UTC
* checking for file 'partykit/DESCRIPTION' ... OK
* this is package 'partykit' version '1.2-29'
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking whether package 'partykit' can be installed ... OK
* used C compiler: 'gcc.exe (GCC) 14.3.0'
* checking installed package size ... OK
* checking package directory ... OK
* checking 'build' directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... [2s] OK
* checking whether the package can be loaded with stated dependencies ... [2s] OK
* checking whether the package can be unloaded cleanly ... [2s] OK
* checking whether the namespace can be loaded with stated dependencies ... [2s] OK
* checking whether the namespace can be unloaded cleanly ... [2s] OK
* checking loading without being on the library search path ... [2s] OK
* checking whether startup messages can be suppressed ... [2s] OK
* checking use of S3 registration ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... [18s] OK
* checking Rd files ... [5s] OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of 'data' directory ... OK
* checking data for non-ASCII characters ... [0s] OK
* checking LazyData ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking line endings in shell scripts ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking line endings in Makefiles ... OK
* checking for GNU extensions in Makefiles ... OK
* checking include directives in Makefiles ... OK
* checking pragmas in C/C++ headers and code ... OK
* checking compiled code ... OK
* checking sizes of PDF files under 'inst/doc' ... OK
* checking installed files from 'inst/doc' ... OK
* checking files in 'vignettes' ... OK
* checking examples ... [8s] ERROR
Running examples in 'partykit-Ex.R' failed
The error most likely occurred in:

> ### Name: glmtree
> ### Title: Generalized Linear Model Trees
> ### Aliases: glmtree plot.glmtree predict.glmtree print.glmtree
> ### Keywords: tree
> 
> ### ** Examples
> 
> if(require("mlbench") && require("vcd")) {
+ 
+ ## Pima Indians diabetes data
+ data("PimaIndiansDiabetes", package = "mlbench")
+ 
+ ## recursive partitioning of a logistic regression model
+ pid_tree2 <- glmtree(diabetes ~ glucose | pregnant +
+   pressure + triceps + insulin + mass + pedigree + age,
+   data = PimaIndiansDiabetes, family = binomial)
+ 
+ ## printing whole tree or individual nodes
+ print(pid_tree2)
+ print(pid_tree2, node = 1)
+ 
+ ## visualization
+ plot(pid_tree2)
+ plot(pid_tree2, tp_args = list(cdplot = TRUE))
+ plot(pid_tree2, terminal_panel = NULL)
+ 
+ ## estimated parameters
+ coef(pid_tree2)
+ coef(pid_tree2, node = 5)
+ summary(pid_tree2, node = 5)
+ 
+ ## deviance, log-likelihood and information criteria
+ deviance(pid_tree2)
+ logLik(pid_tree2)
+ AIC(pid_tree2)
+ BIC(pid_tree2)
+ 
+ ## different types of predictions
+ pid <- head(PimaIndiansDiabetes)
+ predict(pid_tree2, newdata = pid, type = "node")
+ predict(pid_tree2, newdata = pid, type = "response")
+ predict(pid_tree2, newdata = pid, type = "link")
+ 
+ }
Loading required package: mlbench
Loading required package: vcd
Warning in data("PimaIndiansDiabetes", package = "mlbench") :
  data set 'PimaIndiansDiabetes' not found
Error in eval(mf, parent.frame()) : 
  object 'PimaIndiansDiabetes' not found
Calls: glmtree ... model.frame -> terms -> terms.Formula -> terms -> terms.formula
Execution halted
* checking for unstated dependencies in 'tests' ... OK
* checking tests ... [82s] ERROR
  Running 'bugfixes.R' [5s]
  Comparing 'bugfixes.Rout' to 'bugfixes.Rout.save' ... OK
  Running 'constparty.R' [5s]
  Comparing 'constparty.Rout' to 'constparty.Rout.save' ... OK
  Running 'regtest-MIA.R' [2s]
  Comparing 'regtest-MIA.Rout' to 'regtest-MIA.Rout.save' ... OK
  Running 'regtest-cforest.R' [8s]
  Comparing 'regtest-cforest.Rout' to 'regtest-cforest.Rout.save' ... OK
  Running 'regtest-ctree.R' [2s]
  Comparing 'regtest-ctree.Rout' to 'regtest-ctree.Rout.save' ... OK
  Running 'regtest-glmtree.R' [41s]
  Running 'regtest-honesty.R' [2s]
  Running 'regtest-lmtree.R' [3s]
  Running 'regtest-nmax.R' [2s]
  Comparing 'regtest-nmax.Rout' to 'regtest-nmax.Rout.save' ... OK
  Running 'regtest-node.R' [2s]
  Comparing 'regtest-node.Rout' to 'regtest-node.Rout.save' ... OK
  Running 'regtest-party-random.R' [2s]
  Running 'regtest-party.R' [4s]
  Comparing 'regtest-party.Rout' to 'regtest-party.Rout.save' ... OK
  Running 'regtest-split.R' [2s]
  Comparing 'regtest-split.Rout' to 'regtest-split.Rout.save' ... OK
  Running 'regtest-weights.R' [2s]
  Comparing 'regtest-weights.Rout' to 'regtest-weights.Rout.save' ... OK
Running the tests in 'tests/regtest-glmtree.R' failed.
Complete output:
  > suppressWarnings(RNGversion("3.5.2"))
  > 
  > library("partykit")
  Loading required package: grid
  Loading required package: libcoin
  Loading required package: mvtnorm
  > 
  > set.seed(29)
  > n <- 1000
  > x <- runif(n)
  > z <- runif(n)
  > y <- rnorm(n, mean = x * c(-1, 1)[(z > 0.7) + 1], sd = 3)
  > z_noise <- factor(sample(1:3, size = n, replace = TRUE))
  > d <- data.frame(y = y, x = x, z = z, z_noise = z_noise)
  > 
  > 
  > fmla <- as.formula("y ~ x | z + z_noise")
  > fmly <- gaussian()
  > fit <- partykit:::glmfit
  > 
  > # versions of the data
  > d1 <- d
  > d1$z <- signif(d1$z, digits = 1)
  > 
  > k <- 20
  > zs_noise <- matrix(rnorm(n*k), nrow = n)
  > colnames(zs_noise) <- paste0("z_noise_", 1:k)
  > d2 <- cbind(d, zs_noise)
  > fmla2 <- as.formula(paste("y ~ x | z + z_noise +",
  +                           paste0("z_noise_", 1:k, collapse = " + ")))
  > 
  > 
  > d3 <- d2
  > d3$z <- factor(sample(1:3, size = n, replace = TRUE, prob = c(0.1, 0.5, 0.4)))
  > d3$y <- rnorm(n, mean = x * c(-1, 1)[(d3$z == 2) + 1], sd = 3)
  > 
  > ## check weights
  > w <- rep(1, n)
  > w[1:10] <- 2
  > (mw1 <- glmtree(formula = fmla, data = d, weights = w))
  Generalized linear model tree (family: gaussian)
  
  Model formula:
  y ~ x | z + z_noise
  
  Fitted party:
  [1] root
  |   [2] z <= 0.70311: n = 706
  |       (Intercept)           x 
  |        -0.1447422  -0.8138701 
  |   [3] z > 0.70311: n = 304
  |       (Intercept)           x 
  |        0.07006626  0.73278593 
  
  Number of inner nodes:    1
  Number of terminal nodes: 2
  Number of parameters per node: 2
  Objective function (negative log-likelihood): 2551.48
  > (mw2 <- glmtree(formula = fmla, data = d, weights = w, caseweights = FALSE))
  Generalized linear model tree (family: gaussian)
  
  Model formula:
  y ~ x | z + z_noise
  
  Fitted party:
  [1] root
  |   [2] z <= 0.70311: n = 704
  |       (Intercept)           x 
  |        -0.1447422  -0.8138701 
  |   [3] z > 0.70311: n = 296
  |       (Intercept)           x 
  |        0.07006626  0.73278593 
  
  Number of inner nodes:    1
  Number of terminal nodes: 2
  Number of parameters per node: 2
  Objective function (negative log-likelihood): 2551.48
  > 
  > 
  > 
  > ## check dfsplit
  > (mmfluc2 <- mob(formula = fmla, data = d, fit = partykit:::glmfit))
  Model-based recursive partitioning (partykit:::glmfit)
  
  Model formula:
  y ~ x | z + z_noise
  
  Fitted party:
  [1] root
  |   [2] z <= 0.70311: n = 704
  |       (Intercept)           x 
  |        -0.1619978  -0.7896293 
  |   [3] z > 0.70311: n = 296
  |       (Intercept)           x 
  |        0.08683535  0.65598287 
  
  Number of inner nodes:    1
  Number of terminal nodes: 2
  Number of parameters per node: 2
  Objective function: 2551.673
  > (mmfluc3 <- glmtree(formula = fmla, data = d))
  Generalized linear model tree (family: gaussian)
  
  Model formula:
  y ~ x | z + z_noise
  
  Fitted party:
  [1] root
  |   [2] z <= 0.70311: n = 704
  |       (Intercept)           x 
  |        -0.1619978  -0.7896293 
  |   [3] z > 0.70311: n = 296
  |       (Intercept)           x 
  |        0.08683535  0.65598287 
  
  Number of inner nodes:    1
  Number of terminal nodes: 2
  Number of parameters per node: 2
  Objective function (negative log-likelihood): 2551.673
  > (mmfluc3_dfsplit <- glmtree(formula = fmla, data = d, dfsplit = 10))
  Generalized linear model tree (family: gaussian)
  
  Model formula:
  y ~ x | z + z_noise
  
  Fitted party:
  [1] root
  |   [2] z <= 0.70311: n = 704
  |       (Intercept)           x 
  |        -0.1619978  -0.7896293 
  |   [3] z > 0.70311: n = 296
  |       (Intercept)           x 
  |        0.08683535  0.65598287 
  
  Number of inner nodes:    1
  Number of terminal nodes: 2
  Number of parameters per node: 2
  Objective function (negative log-likelihood): 2551.673
  > 
  > 
  > ## check tests
  > if (require("strucchange"))
  +     print(sctest(mmfluc3, node = 1)) # does not yet work
  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
                       z   z_noise
  statistic 2.292499e+01 0.6165335
  p.value   7.780038e-04 0.9984952
  > 
  > x <- mmfluc3
  > (tst3 <- nodeapply(x, ids = nodeids(x), function(n) n$info$criterion))
  $`1`
  NULL
  
  $`2`
  NULL
  
  $`3`
  NULL
  
  > 
  > 
  > 
  > 
  > ## check logLik and AIC
  > logLik(mmfluc2)
  'log Lik.' -2551.673 (df=7)
  > logLik(mmfluc3)
  'log Lik.' -2551.673 (df=7)
  > logLik(mmfluc3_dfsplit)
  'log Lik.' -2551.673 (df=16)
  > logLik(glm(y ~ x, data = d))
  'log Lik.' -2563.694 (df=3)
  > 
  > AIC(mmfluc3)
  [1] 5117.347
  > AIC(mmfluc3_dfsplit)
  [1] 5135.347
  > 
  > ## check pruning
  > pr2 <- prune.modelparty(mmfluc2)
  > AIC(mmfluc2)
  [1] 5117.347
  > AIC(pr2)
  [1] 5117.347
  > 
  > mmfluc_dfsplit3 <- glmtree(formula = fmla, data = d, alpha = 0.5, dfsplit = 3)
  > mmfluc_dfsplit4 <- glmtree(formula = fmla, data = d, alpha = 0.5, dfsplit = 4)
  > pr_dfsplit3 <- prune.modelparty(mmfluc_dfsplit3)
  > pr_dfsplit4 <- prune.modelparty(mmfluc_dfsplit4)
  > AIC(mmfluc_dfsplit3)
  [1] 5142.774
  > AIC(mmfluc_dfsplit4)
  [1] 5156.774
  > AIC(pr_dfsplit3)
  [1] 5142.774
  > AIC(pr_dfsplit4)
  [1] 5124.456
  > 
  > width(mmfluc_dfsplit3)
  [1] 8
  > width(mmfluc_dfsplit4)
  [1] 8
  > width(pr_dfsplit3)
  [1] 8
  > width(pr_dfsplit4)
  [1] 3
  > 
  > ## check inner and terminal
  > options <- list(NULL,
  +                 "object",
  +                 "estfun",
  +                 c("object", "estfun"))
  > 
  > arguments <- list("inner",
  +                   "terminal",
  +                   c("inner", "terminal"))
  > 
  > 
  > for (o in options) {
  +   print(o)
  +   x <- glmtree(formula = fmla, data = d, inner = o)
  +   str(nodeapply(x, ids = nodeids(x), function(n) n$info[c("object", "estfun")]), 2)
  + }
  NULL
  List of 3
   $ 1:List of 2
    ..$ NA: NULL
    ..$ NA: NULL
   $ 2:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
   $ 3:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
  [1] "object"
  List of 3
   $ 1:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
   $ 2:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
   $ 3:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
  [1] "estfun"
  List of 3
   $ 1:List of 2
    ..$ NA    : NULL
    ..$ estfun: num [1:1000, 1:2] -0.1375 -0.0583 -0.0553 0.1043 -0.0744 ...
    .. ..- attr(*, "dimnames")=List of 2
   $ 2:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
   $ 3:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
  [1] "object" "estfun"
  List of 3
   $ 1:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ estfun: num [1:1000, 1:2] -0.1375 -0.0583 -0.0553 0.1043 -0.0744 ...
    .. ..- attr(*, "dimnames")=List of 2
   $ 2:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
   $ 3:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
  > 
  > for (o in options) {
  +   print(o)
  +   x <- glmtree(formula = fmla, data = d, terminal = o)
  +   str(nodeapply(x, ids = nodeids(x), function(n) n$info[c("object", "estfun")]), 2)
  + }
  NULL
  List of 3
   $ 1:List of 2
    ..$ NA: NULL
    ..$ NA: NULL
   $ 2:List of 2
    ..$ NA: NULL
    ..$ NA: NULL
   $ 3:List of 2
    ..$ NA: NULL
    ..$ NA: NULL
  [1] "object"
  List of 3
   $ 1:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
   $ 2:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
   $ 3:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
  [1] "estfun"
  List of 3
   $ 1:List of 2
    ..$ NA    : NULL
    ..$ estfun: num [1:1000, 1:2] -0.1375 -0.0583 -0.0553 0.1043 -0.0744 ...
    .. ..- attr(*, "dimnames")=List of 2
   $ 2:List of 2
    ..$ NA    : NULL
    ..$ estfun: num [1:704, 1:2] -0.1291 0.5104 -0.0603 -0.1868 -0.0981 ...
    .. ..- attr(*, "dimnames")=List of 2
   $ 3:List of 2
    ..$ NA    : NULL
    ..$ estfun: num [1:296, 1:2] -0.1053 -0.0877 0.0544 -0.1581 0.43 ...
    .. ..- attr(*, "dimnames")=List of 2
  [1] "object" "estfun"
  List of 3
   $ 1:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ estfun: num [1:1000, 1:2] -0.1375 -0.0583 -0.0553 0.1043 -0.0744 ...
    .. ..- attr(*, "dimnames")=List of 2
   $ 2:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ estfun: num [1:704, 1:2] -0.1291 0.5104 -0.0603 -0.1868 -0.0981 ...
    .. ..- attr(*, "dimnames")=List of 2
   $ 3:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ estfun: num [1:296, 1:2] -0.1053 -0.0877 0.0544 -0.1581 0.43 ...
    .. ..- attr(*, "dimnames")=List of 2
  > 
  > 
  > ## check model
  > m_mt <- glmtree(formula = fmla, data = d, model = TRUE)
  > m_mf <- glmtree(formula = fmla, data = d, model = FALSE)
  > 
  > dim(m_mt$data)
  [1] 1000    4
  > dim(m_mf$data)
  [1] 0 4
  > 
  > 
  > ## check multiway
  > (m_mult <- glmtree(formula = fmla2, data = d3, catsplit = "multiway", minsize = 80))
  Generalized linear model tree (family: gaussian)
  
  Model formula:
  y ~ x | z + z_noise + z_noise_1 + z_noise_2 + z_noise_3 + z_noise_4 + 
      z_noise_5 + z_noise_6 + z_noise_7 + z_noise_8 + z_noise_9 + 
      z_noise_10 + z_noise_11 + z_noise_12 + z_noise_13 + z_noise_14 + 
      z_noise_15 + z_noise_16 + z_noise_17 + z_noise_18 + z_noise_19 + 
      z_noise_20
  
  Fitted party:
  [1] root
  |   [2] z in 1: n = 76
  |       (Intercept)           x 
  |         0.9859847  -3.2600047 
  |   [3] z in 2: n = 537
  |       (Intercept)           x 
  |       -0.06970187  1.12305074 
  |   [4] z in 3: n = 387
  |       (Intercept)           x 
  |         0.3824392  -1.8337151 
  
  Number of inner nodes:    1
  Number of terminal nodes: 3
  Number of parameters per node: 2
  Objective function (negative log-likelihood): 2511.927
  > 
  > 
  > ## check parm
  > fmla_p <- as.formula("y ~ x + z_noise + z_noise_1 | z + z_noise_2")
  > (m_interc <- glmtree(formula = fmla_p, data = d2, parm = 1))
  Generalized linear model tree (family: gaussian)
  
  Model formula:
  y ~ x + z_noise + z_noise_1 | z + z_noise_2
  
  Fitted party:
  [1] root
  |   [2] z <= 0.65035: n = 644
  |       (Intercept)           x    z_noise2    z_noise3   z_noise_1 
  |       -0.05585503 -1.01257554  0.34044520 -0.16384987  0.24197601 
  |   [3] z > 0.65035: n = 356
  |       (Intercept)           x    z_noise2    z_noise3   z_noise_1 
  |        0.06411865  0.78733976 -0.67811149 -0.14240432 -0.01239154 
  
  Number of inner nodes:    1
  Number of terminal nodes: 2
  Number of parameters per node: 5
  Objective function (negative log-likelihood): 2548.32
  > 
  > (m_p3 <- glmtree(formula = fmla_p, data = d2, parm = 3))
  Generalized linear model tree (family: gaussian)
  
  Model formula:
  y ~ x + z_noise + z_noise_1 | z + z_noise_2
  
  Fitted party:
  [1] root: n = 1000
       (Intercept)            x     z_noise2     z_noise3    z_noise_1 
      -0.058855295 -0.340314311 -0.008404682 -0.109839080  0.154798281  
  
  Number of inner nodes:    0
  Number of terminal nodes: 1
  Number of parameters per node: 5
  Objective function (negative log-likelihood): 2562.32
  > 
  > 
  > ## check trim
  > (m_tt <- glmtree(formula = fmla, data = d, trim = 0.2))
  Generalized linear model tree (family: gaussian)
  
  Model formula:
  y ~ x | z + z_noise
  
  Fitted party:
  [1] root
  |   [2] z <= 0.70311: n = 704
  |       (Intercept)           x 
  |        -0.1619978  -0.7896293 
  |   [3] z > 0.70311: n = 296
  |       (Intercept)           x 
  |        0.08683535  0.65598287 
  
  Number of inner nodes:    1
  Number of terminal nodes: 2
  Number of parameters per node: 2
  Objective function (negative log-likelihood): 2551.673
  > 
  > (m_tf <- glmtree(formula = fmla, data = d, trim = 300, minsize = 300))
  Generalized linear model tree (family: gaussian)
  
  Model formula:
  y ~ x | z + z_noise
  
  Fitted party:
  [1] root
  |   [2] z <= 0.6892: n = 691
  |       (Intercept)           x 
  |        -0.1778199  -0.7692901 
  |   [3] z > 0.6892: n = 309
  |       (Intercept)           x 
  |         0.1065746   0.5562243 
  
  Number of inner nodes:    1
  Number of terminal nodes: 2
  Number of parameters per node: 2
  Objective function (negative log-likelihood): 2552.12
  > 
  > 
  > 
  > ## check breakties
  > m_bt <- glmtree(formula = fmla, data = d1, breakties = TRUE)
  > m_df <- glmtree(formula = fmla, data = d1, breakties = FALSE)
  > 
  > all.equal(m_bt, m_df, check.environment = FALSE)
  [1] "Component \"node\": Component \"kids\": Component 1: Component 5: Component 6: Mean relative difference: 0.1237503"
  [2] "Component \"node\": Component \"kids\": Component 2: Component 5: Component 5: Mean relative difference: 0.1746109"
  [3] "Component \"node\": Component \"kids\": Component 2: Component 5: Component 6: Mean relative difference: 0.0443985"
  [4] "Component \"node\": Component \"info\": Component \"p.value\": Mean relative difference: 1.100407"                 
  [5] "Component \"node\": Component \"info\": Component \"test\": Mean relative difference: 0.07721086"                  
  [6] "Component \"info\": Component \"call\": target, current do not match when deparsed"                                
  [7] "Component \"info\": Component \"control\": Component \"breakties\": 1 element mismatch"                            
  > 
  > unclass(m_bt)$node$info$criterion
  NULL
  > unclass(m_df)$node$info$criterion
  NULL
  > 
  > if (requireNamespace("mlbench")) {
  + 
  + ### example from mob vignette
  + data("PimaIndiansDiabetes", package = "mlbench")
  + 
  + logit <- function(y, x, start = NULL, weights = NULL, offset = NULL, ...) {
  +   glm(y ~ 0 + x, family = binomial, start = start, ...)
  + }
  + 
  + pid_formula <- diabetes ~ glucose | pregnant + pressure + triceps +
  +   insulin + mass + pedigree + age
  + 
  + pid_tree <- mob(pid_formula, data = PimaIndiansDiabetes, fit = logit)
  + print(pid_tree)
  + print(nodeapply(pid_tree, ids = nodeids(pid_tree), function(n) n$info$criterion))
  + 
  + }
  Loading required namespace: mlbench
  Error in eval(mf, parent.frame()) : 
    object 'PimaIndiansDiabetes' not found
  Calls: mob ... model.frame -> terms -> terms.Formula -> terms -> terms.formula
  In addition: Warning message:
  In data("PimaIndiansDiabetes", package = "mlbench") :
    data set 'PimaIndiansDiabetes' not found
  Execution halted
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... [3s] OK
  'partykit.Rnw' using 'UTF-8'... [2s] OK
* checking re-building of vignette outputs ... [97s] ERROR
Error(s) in re-building vignettes:
--- re-building 'constparty.Rnw' using knitr
--- finished re-building 'constparty.Rnw'

--- re-building 'ctree.Rnw' using knitr
--- finished re-building 'ctree.Rnw'

--- re-building 'mob.Rnw' using knitr

Quitting from mob.Rnw:443-445 [PimaIndiansDiabetes-mob]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error:
! object 'PimaIndiansDiabetes' not found
---
Backtrace:
    x
 1. +-stats::model.frame(...)
 2. \-Formula:::model.frame.Formula(...)
 3.   +-stats::model.frame(...)
 4.   +-stats::terms(formula, lhs = lhs, rhs = rhs, data = data, dot = dot)
 5.   \-Formula:::terms.Formula(...)
 6.     +-stats::terms(form, ...)
 7.     \-stats::terms.formula(form, ...)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Error: processing vignette 'mob.Rnw' failed with diagnostics:
object 'PimaIndiansDiabetes' not found
--- failed re-building 'mob.Rnw'

--- re-building 'partykit.Rnw' using knitr
--- finished re-building 'partykit.Rnw'

SUMMARY: processing the following file failed:
  'mob.Rnw'

Error: Vignette re-building failed.
Execution halted

* checking PDF version of manual ... [28s] OK
* checking HTML version of manual ... [12s] OK
* DONE
Status: 3 ERRORs

Check process probably crashed or hung up for 20 minutes ... killed
Most likely this happened in the example checks (?),
if not, ignore the following last lines of example output:
+ plot(pid_tree2)
+ plot(pid_tree2, tp_args = list(cdplot = TRUE))
+ plot(pid_tree2, terminal_panel = NULL)
+ 
+ ## estimated parameters
+ coef(pid_tree2)
+ coef(pid_tree2, node = 5)
+ summary(pid_tree2, node = 5)
+ 
+ ## deviance, log-likelihood and information criteria
+ deviance(pid_tree2)
+ logLik(pid_tree2)
+ AIC(pid_tree2)
+ BIC(pid_tree2)
+ 
+ ## different types of predictions
+ pid <- head(PimaIndiansDiabetes)
+ predict(pid_tree2, newdata = pid, type = "node")
+ predict(pid_tree2, newdata = pid, type = "response")
+ predict(pid_tree2, newdata = pid, type = "link")
+ 
+ }
Loading required package: mlbench
Loading required package: vcd
Warning in data("PimaIndiansDiabetes", package = "mlbench") :
  data set 'PimaIndiansDiabetes' not found
Error in eval(mf, parent.frame()) : 
  object 'PimaIndiansDiabetes' not found
Calls: glmtree ... model.frame -> terms -> terms.Formula -> terms -> terms.formula
Execution halted
======== End of example output (where/before crash/hang up occured ?) ========