prepare.multinomial_plan {vtreat}

Function to apply mkCrossFrameMExperiment treatemnts.

Description

Please see vignette("MultiClassVtreat", package = "vtreat") https://winvector.github.io/vtreat/articles/MultiClassVtreat.html.

Usage

## S3 method for class '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
)

Arguments

treatmentplan

multinomial_plan from mkCrossFrameMExperiment.

dframe

new data to process.

...

not used, declared to forced named binding of later arguments

pruneSig

suppress variables with significance above this level

scale

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.

doCollar

optional if TRUE collar numeric variables by cutting off after a tail-probability specified by collarProb during treatment design.

varRestriction

optional list of treated variable names to restrict to

codeRestriction

optional list of treated variable codes to restrict to

trackedValues

optional named list mapping variables to know values, allows warnings upon novel level appearances (see track_values)

extracols

extra columns to copy.

parallelCluster

(optional) a cluster object created by package parallel or package snow.

use_parallel

logical, if TRUE use parallel methods.

check_for_duplicate_frames

logical, if TRUE check if we called prepare on same data.frame as design step.

Value

prepared data frame.

See Also

mkCrossFrameMExperiment, prepare