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rsc_cv <- function(x, cv.type = "kfold", R = 10, K = 10, threshold = seq(0.05, 0.95,
by = 0.025), even.correction = FALSE, na.rm = FALSE, ncores = NULL, monitor = TRUE) {
dat <- .check_input_data_matrix(x = x, y = NULL, na.rm = na.rm)
colnames_original <- colnames(dat)
storage.mode(dat) <- "double"
n <- as.integer(nrow(dat))
p <- as.integer(ncol(dat))
nc <- as.integer({
p^2 - p
}/2)
if ({
cv.type != "random"
} & {
cv.type != "kfold"
}) {
stop("\"cv.type\" must be either \"random\" (default) or \"kfold\"")
}
if (!is.numeric(R)) {
stop("\"R\" must be an integer > 1")
}
else if (R < 1) {
stop("\"R\" must be an integer > 1")
}
if (!is.numeric(K)) {
stop("\"K\" must be an integer > 1")
}
else if (R < 1) {
stop("\"K\" must be an integer > 1")
}
if (length(threshold) == 1) {
if (threshold <= 0 | threshold >= 1) {
stop("\"threshold\" value does not belong to the interval (0,1).")
}
}
else if (length(threshold) > 1) {
if (any(threshold < 0) | any(threshold >= 1)) {
stop("Some of the \"threshold\" values do not belong to the interval (0,1).")
}
grid.length <- length(threshold)
}
if (even.correction) {
evencorrection <- 1L
}
else {
evencorrection <- 0L
}
if (is.null(ncores)) {
DetectedCores <- detectCores()
if (DetectedCores <= 2) {
ncores <- 1
}
else {
ncores <- {
DetectedCores - 1
}
}
}
else {
ncores <- as.integer(ncores)
if (ncores <= 0) {
stop("\"ncores\" must be an integer larger or equal to 1.")
}
}
if (monitor) {
cat("\n")
message("Computing the RMAD matrix")
t0 <- Sys.time()
}
rmad_vec <- .Fortran("cormadvecdp", matrix = dat, nrow = n, ncol = p, res = numeric(nc),
ressize = nc, correcteven = evencorrection, PACKAGE = "RSC")$res
if (monitor) {
t1 <- Sys.time()
dt01 <- difftime(t1, t0, units = "auto")
message("* RMAD computing time:...... ", round(dt01, 2), " [", attributes(dt01)$units,
"]")
}
if (cv.type == "random") {
if (monitor) {
cat("\n")
message("Performing cross-validation")
t_hat <- round(1.2 * {
{
dt01 * R * 2
}/ncores
}, 2)
message("* predicted end time (worst case):...... ", Sys.time() + t_hat)
}
n1 <- n - floor(n/log(n))
IDX <- array(FALSE, dim = c(R, n))
for (r in 1:R) {
IDX[r, ][sample(1:n, size = n1, replace = FALSE)] <- TRUE
}
registerDoParallel(ncores)
U <- foreach(r = 1:R) %dopar% {
.cv_loss(idx = IDX[r, ], dat = dat, evencorrection = evencorrection,
threshold = threshold, grid.length = grid.length, p = p, nc = nc)
}
stopImplicitCluster()
LOSS <- array(0, dim = c(R, grid.length))
for (r in 1:R) {
LOSS[r, ] <- U[[r]]
}
avg_loss <- apply(LOSS, 2, mean)
se_loss <- apply(LOSS, 2, sd)/sqrt(R)
}
if (cv.type == "kfold") {
if (monitor) {
cat("\n")
message("Performing cross-validation")
t_hat <- round(1.2 * {
{
dt01 * R * K * 2
}/ncores
}, 2)
message("* predicted end time (worst case):...... ", Sys.time() + t_hat)
}
idx_fold <- cut(1:n, breaks = K, labels = FALSE)
IDX <- array(TRUE, dim = c(R * K, n))
row_count <- 1L
for (r in 1:R) {
idx_fold_shuffle <- sample(idx_fold, size = n, replace = FALSE)
for (k in 1:K) {
IDX[row_count, ][idx_fold_shuffle == k] <- FALSE
row_count <- 1L + row_count
}
}
registerDoParallel(ncores)
U <- foreach(r = 1:{
R * K
}) %dopar% {
.cv_loss(idx = IDX[r, ], dat = dat, evencorrection = evencorrection,
threshold = threshold, grid.length = grid.length, p = p, nc = nc)
}
stopImplicitCluster()
if (R == 1) {
LOSS <- array(0, dim = c(K, grid.length))
for (k in 1:K) {
LOSS[k, ] <- U[[k]]
}
}
else {
LOSS <- array(0, dim = c(K, grid.length, R))
dimnames(LOSS)[[3]] <- paste0("r", 1:R)
dimnames(LOSS)[[1]] <- paste0("k", 1:K)
row_count <- 1L
for (r in 1:R) {
for (k in 1:K) {
LOSS[k, , r] <- U[[row_count]]
row_count <- 1L + row_count
}
}
}
avg_loss_r <- sd_loss_r <- matrix(0, nrow = R, ncol = grid.length)
for (r in 1:R) {
avg_loss_r[r, ] <- apply(LOSS[, , r], 2, mean)
sd_loss_r[r, ] <- apply(LOSS[, , r], 2, sd)/sqrt(K)
}
avg_loss <- apply(avg_loss_r, 2, mean)
se_loss <- apply(sd_loss_r, 2, mean)
if (monitor) {
t2 <- Sys.time()
dt02 <- difftime(t2, t0, units = "auto")
}
}
if (monitor) {
message("* finished on:.......................... ", Sys.time())
}
tstar <- which.min(avg_loss)
flags <- rep("", grid.length)
a <- avg_loss[tstar] - se_loss[tstar]
b <- avg_loss[tstar] + se_loss[tstar]
flags[{
avg_loss >= a
} & {
avg_loss <= b
}] <- "*"
flags[tstar] <- "minimum"
res <- data.frame(Threshold = threshold, Average = avg_loss, SE = se_loss, Flag = flags)
ans <- list(rmadvec = rmad_vec, varnames = colnames_original, loss = res, minimum = threshold[tstar],
minimum1se = max(threshold[avg_loss >= a & avg_loss <= b]))
class(ans) <- "rsc_cv"
return(ans)
}
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