set.seed(290875)
readingSkills.cf <- cforest(score ~ ., data = readingSkills,
control = cforest_unbiased(mtry = 2, ntree = 50))
# standard importance
varimp(readingSkills.cf)
# the same modulo random variation
varimp(readingSkills.cf, pre1.0_0 = TRUE)
# conditional importance, may take a while...
varimp(readingSkills.cf, conditional = TRUE)
## Not run: ------------------------------------
# data("GBSG2", package = "TH.data")
# ### add a random covariate for sanity check
# set.seed(29)
# GBSG2$rand <- runif(nrow(GBSG2))
# object <- cforest(Surv(time, cens) ~ ., data = GBSG2,
# control = cforest_unbiased(ntree = 20))
# vi <- varimp(object)
# ### compare variable importances and absolute z-statistics
# layout(matrix(1:2))
# barplot(vi)
# barplot(abs(summary(coxph(Surv(time, cens) ~ ., data = GBSG2))$coeff[,"z"]))
# ### looks more or less the same
#
## ---------------------------------------------
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