l <- readRDS("debug.rds")
do.call(bigstatsr::big_CMSA, args = l)
ind.train <- sort(unique(as.integer(unlist(
lapply(l, function(li) rownames(li$scores))
))))
feval <- bigstatsr::AUC
for (i in 1:1000) {
y <- sample(0:1, size = 911, replace = TRUE)
y.train <- y[ind.train]
betas <- sapply(l, function(x) {
tmp <- x$scores
ind <- as.numeric(rownames(tmp))
stopifnot(all(ind %in% ind.train))
ind2 <- match(ind, ind.train)
seval <- apply(tmp, 2, feval, target = y.train[ind2])
x$betas[, which.max(seval)]
})
}
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