# S3 method for multinomial_plan prepare( treatmentplan, dframe, ..., pruneSig = NULL, scale = FALSE, doCollar = FALSE, varRestriction = NULL, codeRestriction = NULL, trackedValues = NULL, extracols = NULL, parallelCluster = NULL, use_parallel = TRUE, check_for_duplicate_frames = TRUE )
multinomial_plan from mkCrossFrameMExperiment.
new data to process.
not used, declared to forced named binding of later arguments
suppress variables with significance above this level
optional if TRUE replace numeric variables with single variable model regressions ("move to outcome-scale"). These have mean zero and (for variables with significant less than 1) slope 1 when regressed (lm for regression problems/glm for classification problems) against outcome.
optional if TRUE collar numeric variables by cutting off after a tail-probability specified by collarProb during treatment design.
optional list of treated variable names to restrict to
optional list of treated variable codes to restrict to
optional named list mapping variables to know values, allows warnings upon novel level appearances (see
extra columns to copy.
(optional) a cluster object created by package parallel or package snow.
logical, if TRUE use parallel methods.
logical, if TRUE check if we called prepare on same data.frame as design step.
prepared data frame.