Builds a designTreatmentsN treatment plan and a data frame prepared from dframe that is "cross" in the sense each row is treated using a treatment plan built from a subset of dframe disjoint from the given row. The goal is to try to and supply a method of breaking nested model bias other than splitting into calibration, training, test sets.

mkCrossFrameNExperiment(dframe, varlist, outcomename, ..., 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, verbose = TRUE,
  parallelCluster = NULL, use_parallel = TRUE)

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.

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.

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

treatment plan (for use with prepare)

See also

Examples

set.seed(23525) zip <- paste('z',1:100) N <- 200 d <- data.frame(zip=sample(zip,N,replace=TRUE), zip2=sample(zip,N,replace=TRUE), y=runif(N)) del <- runif(length(zip)) names(del) <- zip d$y <- d$y + del[d$zip2] d$yc <- d$y>=mean(d$y) cN <- mkCrossFrameNExperiment(d,c('zip','zip2'),'y', rareCount=2,rareSig=0.9)
#> [1] "vtreat 1.3.2 start initial treatment design Mon Oct 1 14:32:55 2018" #> [1] " start cross frame work Mon Oct 1 14:32:55 2018" #> [1] " vtreat::mkCrossFrameNExperiment done Mon Oct 1 14:32:56 2018"
cor(cN$crossFrame$y,cN$crossFrame$zip_catN) # poor
#> [1] 0.02114663
cor(cN$crossFrame$y,cN$crossFrame$zip2_catN) # better
#> [1] 0.2013498
treatments <- cN$treatments dTrainV <- cN$crossFrame