#' @export
constructionModelesLassoRank <- function(S, k, mini, maxi, X, Y, eps, rank.min, rank.max,
ncores, fast = TRUE, verbose = FALSE)
- {
+{
n <- dim(X)[1]
p <- dim(X)[2]
m <- dim(Y)[2]
L <- length(S)
-
+
# Possible interesting ranks
deltaRank <- rank.max - rank.min + 1
Size <- deltaRank^k
each = deltaRank^(k - r)), each = L)
}
RankLambda[, k + 1] <- rep(1:L, times = Size)
-
+
if (ncores > 1)
{
cl <- parallel::makeCluster(ncores, outfile = "")
"Pi", "Rho", "mini", "maxi", "X", "Y", "eps", "Rank", "m", "phi", "ncores",
"verbose"))
}
-
+
computeAtLambda <- function(index)
{
lambdaIndex <- RankLambda[index, k + 1]
rankIndex <- RankLambda[index, 1:k]
if (ncores > 1)
require("valse") #workers start with an empty environment
-
+
# 'relevant' will be the set of relevant columns
selected <- S[[lambdaIndex]]$selected
relevant <- c()
for (j in 1:p)
{
if (length(selected[[j]]) > 0)
- {
relevant <- c(relevant, j)
- }
}
if (max(rankIndex) < length(relevant))
{
{
res <- EMGrank(S[[lambdaIndex]]$Pi, S[[lambdaIndex]]$Rho, mini, maxi,
X[, relevant], Y, eps, rankIndex, fast)
- llh <- c(res$LLF, sum(rankIndex * (length(relevant) - rankIndex +
- m)))
+ llh <- c(res$LLF, sum(rankIndex * (length(relevant) - rankIndex + m)))
phi[relevant, , ] <- res$phi
}
list(llh = llh, phi = phi, pi = S[[lambdaIndex]]$Pi, rho = S[[lambdaIndex]]$Rho)
}
}
-
+
# For each lambda in the grid we compute the estimators
- out <- if (ncores > 1)
- {
- parLapply(cl, seq_len(length(S) * Size), computeAtLambda)
- } else
- {
- lapply(seq_len(length(S) * Size), computeAtLambda)
- }
-
+ out <-
+ if (ncores > 1) {
+ parLapply(cl, seq_len(length(S) * Size), computeAtLambda)
+ } else {
+ lapply(seq_len(length(S) * Size), computeAtLambda)
+ }
+
if (ncores > 1)
parallel::stopCluster(cl)
-
+
out
}