vtreat (version 0.5.23)

mkCrossFrameNExperiment: Run numeric cross frame experiment.

Description

Builds a designTreatmentsC 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.

Usage

mkCrossFrameNExperiment(dframe, varlist, outcomename, ..., weights = c(),
  minFraction = 0.02, smFactor = 0, rareCount = 0, rareSig = 1,
  collarProb = 0, scale = FALSE, doCollar = TRUE,
  parallelCluster = 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 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, suppress direct effects of level of this count or less.

rareSig

optional numeric, suppress direct effects of level of this significance value greater. Set to one to turn off effect.

collarProb

what fraction of the data (pseudo-probability) to collar data at (

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.

parallelCluster

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

Value

• treatment plan (for use with prepare)

See Also

designTreatmentsC designTreatmentsN prepare

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)
cor(cN$crossFrame$y,cN$crossFrame$zip_catN)  # poor
cor(cN$crossFrame$y,cN$crossFrame$zip2_catN) # better
treatments <- cN$treatments
dTrainV <- cN$crossFrame