#'
selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma,
glambda, X, Y, thresh = 1e-08, eps, ncores = 3, fast = TRUE)
- {
- if (ncores > 1)
- {
+{
+ if (ncores > 1) {
cl <- parallel::makeCluster(ncores, outfile = "")
parallel::clusterExport(cl = cl, varlist = c("phiInit", "rhoInit", "gamInit",
"mini", "maxi", "glambda", "X", "Y", "thresh", "eps"), envir = environment())
}
-
+
# Computation for a fixed lambda
computeCoefs <- function(lambda)
{
params <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
X, Y, eps, fast)
-
+
p <- dim(phiInit)[1]
m <- dim(phiInit)[2]
-
+
# selectedVariables: list where element j contains vector of selected variables
# in [1,m]
- selectedVariables <- lapply(1:p, function(j)
- {
+ selectedVariables <- lapply(1:p, function(j) {
# from boolean matrix mxk of selected variables obtain the corresponding boolean
# m-vector, and finally return the corresponding indices
seq_len(m)[apply(abs(params$phi[j, , ]) > thresh, 1, any)]
})
-
+
list(selected = selectedVariables, Rho = params$rho, Pi = params$pi)
}
-
+
# For each lambda in the grid, we compute the coefficients
out <- if (ncores > 1)
parLapply(cl, glambda, computeCoefs) else lapply(glambda, computeCoefs)
ind_uniq <- which(!ind_dup)
out2 <- list()
for (l in 1:length(ind_uniq))
- {
out2[[l]] <- out[[ind_uniq[l]]]
- }
out2
}