This function repeatedly randomly sub-samples the passed data.frame to provide points for classificaiton by the passed ctree model and calculates the resulting spread score for classifications of each subset and returns a vector of the resulting scores. It is useful for determining the robustness of the spread score for a particular data set.
1 | spread.boot(df, df.ct, nRun = 100, nSample = NULL, responseVar, ignoreCols)
|
df |
data.frame with response variable to be explained and all explanatory variables to be used in ctree classificaiton |
df.ct |
ctree already trained on data, that is used to classify the data points in each sub sample. |
nRun |
number of bootstrapping runs to perform |
nSample |
number of samples (with replacement) for each run |
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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