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# DESP/R/desp.cv.R by A. S. Dalalyan and S. Balmand Copyright (C) 2015-
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License (version 3) as published by
# the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# A copy of the GNU General Public License is available at
# http://www.r-project.org/Licenses/
#
desp.cv <- function(X, v, lambda.range, gamma.range, settings=NULL){
settings$outParCorrB <- TRUE
n = nrow(X)
p = ncol(X)
minCrit <- Inf
sn <- sample(n)
subsets <- split(sn[1:(v*floor(n/v))], rep(1:v, each = floor(n/v)))
for(lambda in lambda.range){
for(gamma in gamma.range){
crit <- 0
for(i in 1:v){
Xt <- X[setdiff(sn, subsets[[i]]),] # training
Xv <- X[subsets[[i]],] # validation
sSize <- length(subsets[[i]])
pX <- desp(Xt, lambda, gamma, settings)
crit <- crit + sum(sqrt(apply((Xv %*% pX$B - tcrossprod(rep(1,sSize),
crossprod(pX$B, pX$mu)))^2,1,sum)))/sSize
}
if(crit<minCrit){
minCrit <- crit
best <- c(lambda, gamma)
}
}
}
pX <- desp(X, best[1], best[2], settings)
res <- list(Omega=pX$Omega, mu=pX$mu, Theta=pX$Theta, lambda=best[1], gamma=best[2])
class(res) <- "desp.cv"
return(res)
}
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