cv.lognet <- function(predmat,y,type.measure,weights,foldid,grouped){

prob_min = 1e-05

prob_max = 1 - prob_min

nc = dim(y)

if (is.null(nc)) {

y = as.factor(y)

ntab = table(y)

nc = as.integer(length(ntab))

y = diag(nc)[as.numeric(y), , drop=FALSE]

}

N = nrow(y)

nfolds = max(foldid)

if ((N/nfolds < 10) && type.measure == "auc") {

warning("Too few (< 10) observations per fold for type.measure='auc' in cv.lognet; changed to type.measure='deviance'. Alternatively, use smaller value for nfolds",

call. = FALSE)

type.measure = cvtype("deviance", "lognet")

}

predmat=1/(1+exp(-predmat))

nlambda=ncol(predmat)

nlams=rep(nlambda,nfolds)

if (type.measure == "auc") {

cvraw = matrix(NA, nfolds, nlambda)

good = matrix(0, nfolds, nlambda)

for (i in seq(nfolds)) {

good[i, seq(nlams[i])] = 1

which = foldid == i

for (j in seq(nlams[i])) {

cvraw[i, j] = auc.mat(y[which, ], predmat[which,

j], weights[which])

}

}

N = apply(good, 2, sum)

weights = tapply(weights, foldid, sum)

grouped=FALSE

}

else {

ywt = apply(y, 1, sum)

y = y/ywt

weights = weights * ywt

N = nrow(y) - apply(is.na(predmat), 2, sum)

cvraw = switch(type.measure,

mse = (y[, 1] - (1 - predmat))^2 + (y[, 2] - predmat)^2,

mae = abs(y[, 1] - (1 - predmat)) + abs(y[, 2] - predmat),

deviance = {

predmat = pmin(pmax(predmat, prob_min), prob_max)

lp = y[, 1] * log(1 - predmat) + y[, 2] * log(predmat)

ly = log(y)

ly[y == 0] = 0

ly = drop((y * ly) %*% c(1, 1))

2 * (ly - lp)

},

class = y[, 1] * (predmat > 0.5) + y[, 2] * (predmat <= 0.5)

)

}

list(cvraw=cvraw,weights=weights,N=N,type.measure=type.measure,grouped=grouped)

}