prepare.multinomial_plan: Function to apply mkCrossFrameMExperiment treatemnts.

In vtreat: A Statistically Sound 'data.frame' Processor/Conditioner

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

vtreat documentation built on June 11, 2021, 5:13 p.m.