R/SL.logreg.R

In ecpolley/SuperLearner_Old: Super Learner Prediction

# logreg {LogicReg}

# currently uses CV to select model size
SL.logreg <- function(Y.temp, X.temp, newX.temp, family, ntrees=c(1,3), nleaves=c(1,7), kfold=10,...){
	tryCatch(require(LogicReg), warning = function(...){ stop("you have selected logreg as a library algorithm but do not have the LogicReg package installed")})

	if(family$family=="gaussian"){
		fit.cv.logreg <- logreg(resp=Y.temp, bin=X.temp, type=2, select=3, ntrees=ntrees, nleaves=nleaves, kfold=kfold)
		uu <- fit.cv.logreg$cvscores
		uu1 <- uu[, 1] * 1000 + uu[, 2]
		uu2 <- unique(uu1)
		vv <- uu[uu[, 3] == uu[, 4], c(1, 2, 6, 5, 8, 7)]
		for (i in 1:length(uu2)) {
		    vv[i, 4] <- sqrt(var(uu[uu1 == uu2[i], 5]))
		    vv[i, 6] <- sqrt(var(uu[uu1 == uu2[i], 7]))
		}
		names(vv)[4:6] <- c("train.sd", "cv/test", "cv/test.sd")
		min.ntree <- vv[which.min(vv[,5]),1]
		min.nleaves <- vv[which.min(vv[,5]),2]
		fit.logreg <- logreg(resp=Y.temp, bin=X.temp, type=2, select = 1, ntrees = min.ntree, nleaves=min.nleaves)
	}
	if(family$family=="binomial"){
		fit.cv.logreg <- logreg(resp=Y.temp, bin=X.temp, type=3, select=3, ntrees=ntrees, nleaves=nleaves, kfold=kfold)
		uu <- fit.cv.logreg$cvscores
		uu1 <- uu[, 1] * 1000 + uu[, 2]
		uu2 <- unique(uu1)
		vv <- uu[uu[, 3] == uu[, 4], c(1, 2, 6, 5, 8, 7)]
		for (i in 1:length(uu2)) {
		    vv[i, 4] <- sqrt(var(uu[uu1 == uu2[i], 5]))
		    vv[i, 6] <- sqrt(var(uu[uu1 == uu2[i], 7]))
		}
		names(vv)[4:6] <- c("train.sd", "cv/test", "cv/test.sd")
		min.ntree <- vv[which.min(vv[, 5]), 1]
		min.nleaves <- vv[which.min(vv[, 5]), 2]
		fit.logreg <- logreg(resp=Y.temp, bin=X.temp, type=3, select = 1, ntrees = min.ntree, nleaves=min.nleaves)
	}
	out <- predict(fit.logreg, newbin=newX.temp)
	fit <- list(object=fit.logreg)
	foo <- list(out=out, fit=fit)
	class(foo$fit) <- c("SL.logreg")
	return(foo)
}

# 
predict.SL.logreg <- function(object, newdata, family, X=NULL, Y=NULL,...) {
	tryCatch(require(LogicReg), warning = function(...){ stop("you have selected logreg as a library algorithm but do not have the LogicReg package installed")})
	out <- predict(object$object, newbin=newdata)
	out
}