R Under development (unstable) (2025-11-17 r89032 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> 
> suppressPackageStartupMessages(library("tbm"))
> options(warn = -1L)
> library("tram")
Loading required package: mvtnorm
> library("partykit")
Loading required package: grid

Attaching package: 'grid'

The following object is masked from 'package:variables':

    unit

Loading required package: libcoin

Attaching package: 'partykit'

The following object is masked from 'package:mboost':

    varimp

> 
> set.seed(29)
> 
> data("bodyfat", package = "TH.data")
> 
> mf <- as.mlt(Colr(DEXfat ~ 1, data = bodyfat, order = 5))
> logLik(mf)
'log Lik.' -265.4476 (df=6)
> 
> Mstop <- 50
> 
> fd <- cv(rep(1, NROW(bodyfat)), type = "kfold", B = 2)
> 
> bctrl <- boost_control(nu = .1, trace = FALSE, mstop = Mstop)
> 
> tctrl <- ctree_control(minsplit = 8, minbucket = 5, mincriterion = 0,
+                        maxdepth = 4, splittest = FALSE, 
+                        testtype = "Teststatistic")
> 
> bf_t <- ctmboost(model = mf, formula = DEXfat ~ ., data = bodyfat, 
+                  method = quote(mboost::blackboost), control = bctrl, 
+                  tree_control = tctrl)
> logLik(bf_t)
'log Lik.' -209.2997 (df=NULL)
> 
> bf_ctm <- ctmboost(model = mf, formula = DEXfat ~ ., data = bodyfat, 
+                    control = bctrl)
> logLik(bf_ctm)
'log Lik.' -211.3087 (df=NULL)
> table(selected(bf_ctm))

 2  3  5  6  7  8 
 5  9  5  2 12 17 
> 
> bf_dr <- ctmboost(model = mf, formula = DEXfat ~ ., data = bodyfat,
+                   baselearner = "bols", control = bctrl)
> logLik(bf_dr)
'log Lik.' -215.4619 (df=NULL)
> table(selected(bf_dr))

 2  3  5  6  7  8 
 4  5  6  2 21 12 
> 
> bf_st <- stmboost(model = mf, formula = DEXfat ~ ., data = bodyfat, 
+                   method = quote(mboost::blackboost), tree_control = tctrl)
> round(logLik(bf_st), 0)
'log Lik.' -177 (df=NULL)
> 
> bf_shift <- stmboost(model = mf, formula = DEXfat ~ ., data = bodyfat, 
+                      method = quote(mboost::gamboost))
> logLik(bf_shift)
'log Lik.' -189.614 (df=NULL)
> table(selected(bf_shift))

 2  3  5  7  8  9 
20 35  4 26 10  5 
> 
> bf_lin <- stmboost(model = mf, formula = DEXfat ~ . - 1, data = bodyfat, 
+                    method = quote(mboost:::glmboost.formula))
> logLik(bf_lin)
'log Lik.' -191.0065 (df=NULL)
> table(selected(bf_lin))

 2  3  5  6  7  8  9 
21 33  3  1 26 11  5 
> 
> mf2 <- Lm(DEXfat ~ 1, data = bodyfat)
> 
> bf_lin2 <- ctmboost(model = mf2, formula = DEXfat ~ ., data = bodyfat)
> logLik(bf_lin2$model, parm = coef(bf_lin2))
'log Lik.' -263.1077 (df=NULL)
> table(selected(bf_lin2))

 1  2  3  4  5  6  7  8  9 
 6  9 12 14 11  9 21 12  6 
> 
> ### test against L_2 glmboost
> m <- Lm(DEXfat ~ 1, data = bodyfat, fixed = c("DEXfat" = 1))
> bf_1 <- stmboost(model = m, formula = DEXfat ~ 0 + ., data = bodyfat, 
+                   control = bctrl,
+                   method = quote(mboost:::glmboost.formula), 
+                   mltargs = list(fixed = c("DEXfat" = 1)))
> bf_2 <- glmboost(DEXfat ~ ., data = bodyfat, offset = mean(bodyfat$DEXfat),
+                   control = bctrl)
> stopifnot(max(abs(mboost:::coef.glmboost(bf_1) - coef(bf_2)[-1])) < 
+           sqrt(.Machine$double.eps))
> r <- risk(bf_1)
> stopifnot(r[length(r)] + logLik(bf_1) < sqrt(.Machine$double.eps))
> 
> stopifnot(max(abs(-nuisance(bf_1) + mboost:::predict.glmboost(bf_1) - predict(bf_2))) < 
+           sqrt(.Machine$double.eps))
> 
> 
> proc.time()
   user  system elapsed 
   9.00    0.37    9.37