designTreatmentsN: build all treatments for a data frame to predict a numeric outcome

Description

Function to design variable treatments for binary prediction of a numeric outcome. Data frame is assumed to have only atomic columns except for dates (which are converted to numeric). Note: each column is processed independently of all others.

Usage

designTreatmentsN(dframe, varlist, outcomename, ..., weights = c(),
  minFraction = 0.02, smFactor = 0, rareCount = 0, rareSig = 1,
  collarProb = 0, verbose = 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 (

verbose

if TRUE print progress.

parallelCluster

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

Value

treatment plan (for use with prepare)

Details

The main fields are mostly vectors with names (all with the same names in the same order):

- vars : (character array without names) names of variables (in same order as names on the other diagnostic vectors) - varMoves : logical TRUE if the variable varied during hold out scoring, only variables that move will be in the treated frame - sig : an estimate significance of effect

See the vtreat vignette for a bit more detail and a worked example.

Examples

Run this code

dTrainN <- data.frame(x=c('a','a','a','a','b','b','b'),
    z=c(1,2,3,4,5,6,7),y=c(0,0,0,1,0,1,1))
dTestN <- data.frame(x=c('a','b','c',NA),
    z=c(10,20,30,NA))
treatmentsN = designTreatmentsN(dTrainN,colnames(dTrainN),'y')
dTrainNTreated <- prepare(treatmentsN,dTrainN,pruneSig=0.99)
dTestNTreated <- prepare(treatmentsN,dTestN,pruneSig=0.99)

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