Nothing
test_Scale <- function() {
library('vtreat')
dTrainC <- data.frame(x=c('a','a','a','b','b',NA),
z=c(1,2,3,4,NA,6),
y=c(FALSE,FALSE,TRUE,FALSE,TRUE,TRUE))
treatmentsC <- designTreatmentsC(dTrainC,colnames(dTrainC),'y',TRUE,
catScaling=TRUE,
verbose=FALSE)
dTrainCTreatedUnscaled <- prepare(treatmentsC,dTrainC,pruneSig=c(),scale=FALSE, check_for_duplicate_frames=FALSE)
dTrainCTreatedScaled <- prepare(treatmentsC,dTrainC,pruneSig=c(),scale=TRUE, check_for_duplicate_frames=FALSE)
slopeFrame <- data.frame(varName=treatmentsC$scoreFrame$varName,
stringsAsFactors = FALSE)
slopeFrame$mean <- vapply(dTrainCTreatedScaled[,slopeFrame$varName,drop=FALSE],mean,
numeric(1))
slopeFrame$slope <- vapply(slopeFrame$varName,
function(c) {
glm(paste('y',c,sep='~'),family=binomial,
data=dTrainCTreatedScaled)$coefficients[[2]]
},
numeric(1))
slopeFrame$sig <- vapply(slopeFrame$varName,
function(c) {
treatmentsC$scoreFrame[treatmentsC$scoreFrame$varName==c,'sig']
},
numeric(1))
slopeFrame$badSlope <- ifelse(is.na(slopeFrame$slope),TRUE,abs(slopeFrame$slope-1)>1.e-8)
expect_true(!any(is.na(dTrainCTreatedUnscaled)))
expect_true(!any(is.na(dTrainCTreatedScaled)))
expect_true(!any(is.na(slopeFrame$mean)))
expect_true(!any(is.infinite(slopeFrame$mean)))
expect_true(max(abs(slopeFrame$mean))<=1.0e-8)
expect_true(!any(slopeFrame$badSlope & (slopeFrame$sig<1)))
invisible(NULL)
}
test_Scale()
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