mkCrossFrameMExperiment(d, vars, y_name, ..., weights = c(),
  minFraction = 0.02, smFactor = 0, rareCount = 0, rareSig = 1,
  collarProb = 0, codeRestriction = NULL, customCoders = NULL,
  scale = FALSE, doCollar = FALSE, splitFunction = NULL,
  ncross = 3, forceSplit = FALSE, catScaling = FALSE,
  y_dependent_treatments = c("catB"), verbose = FALSE,
  parallelCluster = NULL, use_parallel = TRUE)

Arguments

d

data to learn from

vars

character, vector of indpendent variable column names.

y_name

character, name of outcome column.

...

not used, 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.multinomial_plan.

codeRestriction

what types of variables to produce (character array of level codes, NULL means no restriction).

customCoders

map from code names to custom categorical variable encoding functions (please see https://github.com/WinVector/vtreat/blob/master/extras/CustomLevelCoders.md).

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.

y_dependent_treatments

character what treatment types to build per-outcome level.

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.

Value

list(cross_frame, treatments_0, treatments_m)

See also