Value variables for prediction a numeric outcome.

value_variables_N(dframe, varlist, outcomename, ..., weights = c(),
  minFraction = 0.02, smFactor = 0, rareCount = 0, rareSig = 1,
  collarProb = 0, scale = FALSE, doCollar = FALSE,
  splitFunction = NULL, ncross = 3, forceSplit = FALSE,
  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,
  c.spline.num = vtreat::spline_variablec, n.spline.num =
  vtreat::spline_variable), codeRestriction = c("PiecewiseV", "knearest",
  "spline", "clean", "isBAD", "catB", "catP"))

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 and there must be a cut such that dframe[[outcomename]] is both above the cut at least twice and below the cut 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.

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.

Value

table of variable valuations