Nothing
cvFunc <-
function (data, dimens, interactionModels, modelsInMasterList, regPostProb, k, alpha) {
newOrder <- sample (x = rep(1:dim(data)[1]), size = dim(data)[1], replace = F)
data <- data[newOrder,]
partitionSize <- dim(data)[1] %/% k
confusionMatrix <- list()
for (i in 1:k) {
first <- 1 + (i-1) * partitionSize
if (i == k)
last <- dim(data)[1]
else
last <- i * partitionSize
test.indices <- rep(first:last)
test.data <- data[test.indices,,drop = F]
train.data <- data[-test.indices,,drop = F]
fitted.values <- fitInteractionModels (train.data, dimens, interactionModels, modelsInMasterList, alpha)
diseaseProb <- array (0, dim(test.data)[1])
prediction <- array (0, dim(test.data)[1])
for (r in 1:dim(test.data)[1]) {
for (s in 1:dim(modelsInMasterList)[1]) {
obsVector <- test.data[r, na.omit(modelsInMasterList[s,])]
obsVector[length(obsVector)] <- 0
v0 <- fitted.values[[s]][findIndex(obsVector, dimens[na.omit(modelsInMasterList[s,])])]
obsVector[length(obsVector)] <- 1
v1 <- fitted.values[[s]][findIndex(obsVector, dimens[na.omit(modelsInMasterList[s,])])]
diseaseProb[r] <- diseaseProb[r] + regPostProb[s] * v1 / (v0 + v1)
}
prediction[r] <- round(diseaseProb[r])
}
actual <- test.data[,dim(test.data)[2]]
actual <- factor(actual, levels = c(0,1))
prediction <- factor(prediction, levels = c(0,1))
confusionMatrix[[i]] <- data.frame(prediciton = prediction, actual = actual)
confusionMatrix[[i]] <- as.data.frame.table(table(confusionMatrix[[i]]))
colnames(confusionMatrix[[i]])[dim(confusionMatrix[[i]])[2]] <- "prop"
confusionMatrix[[i]]$prop <- confusionMatrix[[i]]$prop / dim(test.data)[1]
}
avgConfusionMatrix <- confusionMatrix[[1]]
for (i in 2:k)
avgConfusionMatrix$prop <- avgConfusionMatrix$prop + confusionMatrix[[i]]$prop
avgConfusionMatrix$prop <- avgConfusionMatrix$prop / k
return(avgConfusionMatrix)
}
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