Usage
cforest_unbiased(…)
cforest_classical(…)
cforest_control(teststat = "max",
testtype = "Teststatistic",
mincriterion = qnorm(0.9),
savesplitstats = FALSE,
ntree = 500, mtry = 5, replace = TRUE,
fraction = 0.632, trace = FALSE, …)
Arguments
teststat
a character specifying the type of the test statistic
to be applied.
testtype
a character specifying how to compute the distribution of
the test statistic.
mincriterion
the value of the test statistic (for testtype == "Teststatistic"
),
or 1 - p-value (for other values of testtype
) that
must be exceeded in order to implement a split.
mtry
number of input variables randomly sampled as candidates
at each node for random forest like algorithms. Bagging, as special case
of a random forest without random input variable sampling, can
be performed by setting mtry
either equal to NULL
or
manually equal to the number of input variables.
savesplitstats
a logical determining whether the process of standardized
two-sample statistics for split point estimate
is saved for each primary split.
ntree
number of trees to grow in a forest.
replace
a logical indicating whether sampling of observations is
done with or without replacement.
fraction
fraction of number of observations to draw without
replacement (only relevant if replace = FALSE
).
trace
a logical indicating if a progress bar shall be printed
while the forest grows.