Nothing
cv.HDtweedie <- function(x, y, group=NULL, p, weights, lambda = NULL, pred.loss = c("deviance",
"mae", "mse"), nfolds = 5, foldid, ...) {
if (missing(p)) p <- 1.5
if (missing(pred.loss))
pred.loss <- "default" else pred.loss <- match.arg(pred.loss)
N <- nrow(x)
if (missing(weights)) weights <- rep(1.0,N)
###Fit the model once to get dimensions etc of output
y <- drop(y)
tweediegrpnet.object <- HDtweedie(x, y, group, p, weights, lambda = lambda, ...)
lambda <- tweediegrpnet.object$lambda
# predict -> coef
if (missing(foldid))
foldid <- sample(rep(seq(nfolds), length = N)) else nfolds <- max(foldid)
if (nfolds < 3)
stop("nfolds must be bigger than 3; nfolds=10 recommended")
outlist <- as.list(seq(nfolds))
###Now fit the nfold models and store them
for (i in seq(nfolds)) {
which <- foldid == i
y_sub <- y[!which]
outlist[[i]] <- HDtweedie(x = x[!which, , drop = FALSE], y = y_sub, group = group, p = p, weights = weights[!which], lambda = lambda, ...)
}
###What to do depends on the pred.loss
cvstuff <- cv.tweediegrppath(outlist, lambda, x, y, p, weights, foldid, pred.loss)
cvm <- cvstuff$cvm
cvsd <- cvstuff$cvsd
cvname <- cvstuff$name
out <- list(lambda = lambda, cvm = cvm, cvsd = cvsd, cvupper = cvm + cvsd,
cvlo = cvm - cvsd, name = cvname, HDtweedie.fit = tweediegrpnet.object)
lamin <- getmin(lambda, cvm, cvsd)
obj <- c(out, as.list(lamin))
class(obj) <- "cv.HDtweedie"
obj
}
cv.tweediegrppath <- function(outlist, lambda, x, y, p, weights, foldid, pred.loss) {
typenames <- c(deviance = "Tweedie Deviance", mse = "Mean Square Error", mae = "Mean Absolute Error")
if (pred.loss == "default")
pred.loss <- "deviance"
if (!match(pred.loss, c("deviance", "mse", "mae"), FALSE)) {
warning("Only 'deviance', 'mse' and 'mae' available; 'deviance' used")
pred.loss <- "deviance"
}
predmat <- matrix(NA, length(y), length(lambda))
nfolds <- max(foldid)
nlams <- double(nfolds)
for (i in seq(nfolds)) {
which <- foldid==i
fitobj <- outlist[[i]]
preds <- predict(fitobj, x[which,], type = "response")
nlami <- length(outlist[[i]]$lambda)
predmat[which, seq(nlami)] <- preds
nlams[i] <- nlami
}
N <- length(y)
cvraw <- switch(pred.loss, "deviance"=devi(y, predmat, p), "mae"=abs(y-predmat), "mse"=(y-predmat)^2)
cvob <- cvcompute(cvraw, weights, foldid, nlams)
cvraw <- cvob$cvraw
weights <- cvob$weights
N <- cvob$N
cvm <- apply(cvraw, 2, weighted.mean, w=weights, na.rm=TRUE)
cvsd <- sqrt(apply(scale(cvraw, cvm, scale=FALSE)^2, 2, weighted.mean, w=weights, na.rm=TRUE)/(N-1))
list(cvm=cvm, cvsd=cvsd, name=typenames[pred.loss])
}
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