Nothing
test_ZW <- function() {
# categorical example
set.seed(235256)
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))
dTrainC <- rbind(dTrainC,dTrainC)
trainCWeights <- numeric(nrow(dTrainC))
trainCWeights[1:(length(trainCWeights)/2)] <- 1
dTestC <- data.frame(x=c('a','b','c',NA),z=c(10,20,30,NA))
treatmentsC <- designTreatmentsC(dTrainC,colnames(dTrainC),'y',TRUE,
catScaling = TRUE,
weights=trainCWeights,verbose=FALSE)
dTrainCTreated <- prepare(treatmentsC,dTrainC,pruneSig=c(),scale=TRUE, check_for_duplicate_frames=FALSE)
varsC <- setdiff(colnames(dTrainCTreated),'y')
# all input variables should be mean 0
sapply(dTrainCTreated[,varsC,drop=FALSE],mean)
# all slopes should be 1
sapply(varsC,function(c) { glm(paste('y',c,sep='~'),family='binomial',
data=dTrainCTreated)$coefficients[[2]]})
dTestCTreated <- prepare(treatmentsC,dTestC,pruneSig=c(),scale=TRUE, check_for_duplicate_frames=FALSE)
# categorical example indicator mode
set.seed(235256)
treatmentsC <- designTreatmentsC(dTrainC,colnames(dTrainC),'y',TRUE,
catScaling = FALSE,
weights=trainCWeights,verbose=FALSE)
dTrainCTreated <- prepare(treatmentsC,dTrainC,pruneSig=c(),scale=TRUE, check_for_duplicate_frames=FALSE)
varsC <- setdiff(colnames(dTrainCTreated),'y')
# all input variables should be mean 0
sapply(dTrainCTreated[,varsC,drop=FALSE],mean)
# all slopes should be 1
sapply(varsC,function(c) { lm(paste('y',c,sep='~'),
data=dTrainCTreated)$coefficients[[2]]})
dTestCTreated <- prepare(treatmentsC,dTestC,pruneSig=c(),scale=TRUE, check_for_duplicate_frames=FALSE)
# numeric example
set.seed(235256)
dTrainN <- data.frame(x=c('a','a','a','a','b','b',NA),
z=c(1,2,3,4,5,NA,7),y=c(0,0,0,1,0,1,1))
dTrainN <- rbind(dTrainN,dTrainN)
trainNWeights <- numeric(nrow(dTrainN))
trainNWeights[1:(length(trainNWeights)/2)] <- 1
dTestN <- data.frame(x=c('a','b','c',NA),z=c(10,20,30,NA))
treatmentsN = designTreatmentsN(dTrainN,colnames(dTrainN),'y',
weights=trainNWeights,
verbose=FALSE)
dTrainNTreated <- prepare(treatmentsN,dTrainN,pruneSig=c(),scale=TRUE, check_for_duplicate_frames=FALSE)
varsN <- setdiff(colnames(dTrainNTreated),'y')
# all input variables should be mean 0
sapply(dTrainNTreated[,varsN,drop=FALSE],mean)
# all slopes should be 1
sapply(varsN,function(c) { lm(paste('y',c,sep='~'),
data=dTrainNTreated)$coefficients[[2]]})
dTestNTreated <- prepare(treatmentsN,dTestN,pruneSig=c(),scale=TRUE, check_for_duplicate_frames=FALSE)
expect_true(!is.null(dTestNTreated))
invisible(NULL)
}
test_ZW()
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