## ----german-------------------------------------------------------------------
data("german", package = "rchallenge")

## ----germansample-------------------------------------------------------------
set.seed(2409)
dat <- droplevels(german[sample(seq_len(NROW(german)), size = 500), ])

## ----germanstr2, eval = FALSE-------------------------------------------------
# str(dat)

## ----germanstr3, echo = FALSE-------------------------------------------------
str(dat, width = 80, strict.width = "cut")

## ----ctree--------------------------------------------------------------------
set.seed(2906)
ct_partykit <- partykit::ctree(credit_risk ~ ., data = dat,
  control = partykit::ctree_control(mtry = 5, teststat = "quadratic",
    testtype = "Univariate", mincriterion = 0, saveinfo = FALSE))

## ----stablelearner_cforest----------------------------------------------------
set.seed(2907)
cf_stablelearner <- stablelearner::stabletree(ct_partykit,
  sampler = stablelearner::subsampling, savetrees = TRUE, B = 100, v = 0.632)

## ----stablelearner_methods----------------------------------------------------
summary(cf_stablelearner, original = FALSE)

## ----stablelearner_barplot, fig.height = 4, fig.width = 8---------------------
barplot(cf_stablelearner, original = FALSE, cex.names = 0.6)

## ----stablelearner_image, fig.height = 4, fig.width = 8-----------------------
image(cf_stablelearner, original = FALSE, cex.names = 0.6)

## ----stablelearner_plot, fig.height = 12, fig.width = 8-----------------------
plot(cf_stablelearner, original = FALSE,
  select = c("status", "present_residence", "duration"))

## ----stablelearner_trees, eval = FALSE----------------------------------------
# cf_stablelearner$tree[[1]]

## ----partykit_cforest---------------------------------------------------------
set.seed(2908)
cf_partykit <- partykit::cforest(credit_risk ~ ., data = dat,
  ntree = 100, mtry = 5)

## ----as.stabletree_partykit, eval = FALSE-------------------------------------
# cf_partykit_st <- stablelearner::as.stabletree(cf_partykit)
# summary(cf_partykit_st, original = FALSE)
# barplot(cf_partykit_st)
# image(cf_partykit_st)
# plot(cf_partykit_st, select = c("status", "present_residence", "duration"))

## ----partykit_varimp----------------------------------------------------------
partykit::varimp(cf_partykit)

## ----party_cforest, eval = FALSE----------------------------------------------
# set.seed(2909)
# cf_party <- party::cforest(credit_risk ~ ., data = dat,
#   control = party::cforest_unbiased(ntree = 100, mtry = 5))

## ----as.stabletree_party, eval = FALSE----------------------------------------
# cf_party_st <- stablelearner::as.stabletree(cf_party)
# summary(cf_party_st, original = FALSE)
# barplot(cf_party_st)
# image(cf_party_st)
# plot(cf_party_st, select = c("status", "present_residence", "duration"))

## ----randomForest-------------------------------------------------------------
set.seed(2910)
rf <- randomForest::randomForest(credit_risk ~ ., data = dat,
  ntree = 100, mtry = 5)

## ----as.stabletree_randomForest-----------------------------------------------
rf_st <- stablelearner::as.stabletree(rf)
summary(rf_st, original = FALSE)

## ----rf_barplot, fig.height = 4, fig.width = 8--------------------------------
barplot(rf_st, cex.names = 0.6)

## ----rf_image, fig.height = 4, fig.width = 8----------------------------------
image(rf_st, cex.names = 0.6)

## ----rf_plot, fig.height = 12, fig.width = 8----------------------------------
plot(rf_st, select = c("status", "present_residence", "duration"))

## ----ranger, eval = FALSE-----------------------------------------------------
# set.seed(2911)
# rf_ranger <- ranger::ranger(credit_risk ~ ., data = dat,
#   num.trees = 100, mtry = 5)

## ----as.stabletree_ranger, eval = FALSE---------------------------------------
# rf_ranger_st <- stablelearner::as.stabletree(rf_ranger)
# summary(rf_ranger_st, original = FALSE)
# barplot(rf_ranger_st)
# image(rf_ranger_st)
# plot(rf_ranger_st, select = c("status", "present_residence", "duration"))