Value variables for prediction a categorical outcome.

Value variables for prediction a categorical outcome.

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