This function repeatedly runs the ctree algorithm on random sub-samples of the underlying data to provide a measure of robustness for the individual ctree results.
1 2 | ctree.boot(df, nRun = 100, nSample = NULL, ctl = NULL, responseVar,
ignoreCols)
|
df |
data.frame with response variable to be explained and all explanatory variables for the ctree to run with |
nRun |
number of bootstrapping runs to perform |
nSample |
number of samples (with replacement) for each run |
ctl |
ctree control instance to be passed to ctree algorithm to control stopping criteria, etc. |
responseVar |
the name of the variable that the ctree is trying to explain |
ignoreCols |
an optional list of columns in df, but that should be excluded from the ctree modeling |
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