value_variables_C {vtreat}

Value variables for prediction a categorical outcome.

Description

Value variables for prediction a categorical outcome.

Usage

value_variables_C(
  dframe,
  varlist,
  outcomename,
  outcometarget,
  ...,
  weights = c(),
  minFraction = 0.02,
  smFactor = 0,
  rareCount = 0,
  rareSig = 1,
  collarProb = 0,
  scale = FALSE,
  doCollar = FALSE,
  splitFunction = NULL,
  ncross = 3,
  forceSplit = FALSE,
  catScaling = TRUE,
  verbose = FALSE,
  parallelCluster = NULL,
  use_parallel = TRUE,
  customCoders = list(c.PiecewiseV.num = vtreat::solve_piecewisec, n.PiecewiseV.num =
    vtreat::solve_piecewise, c.knearest.num = vtreat::square_windowc, n.knearest.num =
    vtreat::square_window),
  codeRestriction = c("PiecewiseV", "knearest", "clean", "isBAD", "catB", "catP"),
  missingness_imputation = NULL,
  imputation_map = NULL
)

Arguments

dframe

Data frame to learn treatments from (training data), must have at least 1 row.

varlist

Names of columns to treat (effective variables).

outcomename

Name of column holding outcome variable. dframe[[outcomename]] must be only finite non-missing values.

outcometarget

Value/level of outcome to be considered "success", and there must be a cut such that dframe[[outcomename]]==outcometarget at least twice and dframe[[outcomename]]!=outcometarget at least twice.

...

no additional arguments, declared to forced named binding of later arguments

weights

optional training weights for each row

minFraction

optional minimum frequency a categorical level must have to be converted to an indicator column.

smFactor

optional smoothing factor for impact coding models.

rareCount

optional integer, allow levels with this count or below to be pooled into a shared rare-level. Defaults to 0 or off.

rareSig

optional numeric, suppress levels from pooling at this significance value greater. Defaults to NULL or off.

collarProb

what fraction of the data (pseudo-probability) to collar data at if doCollar is set during prepare.treatmentplan.

scale

optional if TRUE replace numeric variables with regression ("move to outcome-scale").

doCollar

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

splitFunction

(optional) see vtreat::buildEvalSets .

ncross

optional scalar>=2 number of cross-validation rounds to design.

forceSplit

logical, if TRUE force cross-validated significance calculations on all variables.

catScaling

optional, if TRUE use glm() linkspace, if FALSE use lm() for scaling.

verbose

if TRUE print progress.

parallelCluster

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

use_parallel

logical, if TRUE use parallel methods.

customCoders

additional coders to use for variable importance estimate.

codeRestriction

codes to restrict to for variable importance estimate.

missingness_imputation

function of signature f(values: numeric, weights: numeric), simple missing value imputer.

imputation_map

map from column names to functions of signature f(values: numeric, weights: numeric), simple missing value imputers.

Value

table of variable valuations