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cv.spcr <- function(x, y, k, w=0.1, xi=0.01, nfolds=5, adaptive=FALSE, center=TRUE, scale=FALSE, lambda.B.length=10, lambda.gamma.length=10, lambda.B=NULL, lambda.gamma=NULL){
if( !is.matrix(x) ) stop("x must be a matrix.")
if( mode(x)!="numeric" ) stop("x must be numeric.")
if ( !is.vector(y) ) stop("y must be a vector.")
if( mode(y)!="numeric" ) stop("y must be numeric.")
if( k < 1 ) stop("k is an integer and larger than one.")
n <- length(y)
ini.lambda.beta <- ini.lambda.gamma <- ini.lambda( x=x, y=y, k=k, w=w, xi=xi )
lambda.beta.candidate <- rev( seq( n*0.005, ini.lambda.beta, length=lambda.B.length ) )
lambda.gamma.candidate <- rev( seq(n* 0.005, ini.lambda.gamma, length=lambda.gamma.length ) )
if( is.null(lambda.B) != TRUE ) lambda.beta.candidate <- sort(lambda.B, decreasing=TRUE)
if( is.null(lambda.gamma) != TRUE ) lambda.gamma.candidate <- sort(lambda.gamma, decreasing=TRUE)
A.ini <- as.matrix(eigen(var(x))$vectors[ ,1:k])
gamma0.ini <- mean(y)
gamma.ini <- rep(0, k)
Beta.ini <- matrix( 0, nrow(A.ini), k )
### CV_mat : estimated CV errors
CV.mat <- matrix( 0, length(lambda.gamma.candidate), length(lambda.beta.candidate) )
foldid <- sample(rep(seq(nfolds),length=n))
x.all <- x
y.all <- y
for(i in seq(nfolds))
{
num.foldid <- which(foldid==i)
x <- x.all[ -num.foldid, ]
y <- y.all[ -num.foldid ]
x.test.cv <- x.all[ num.foldid, ]
y.test.cv <- y.all[ num.foldid ]
if( center==TRUE ){
x_ori <- x
x <- sweep(x_ori, 2, apply(x_ori,2,mean))
x.test.cv <- sweep(x.test.cv, 2, apply(x_ori,2,mean))
}
if( scale==TRUE ){
x_ori <- x
x <- scale(x_ori)
x.test.cv <- sweep(sweep(x.test.cv, 2, apply(x_ori, 2, mean)), 2, apply(x_ori, 2, sd), FUN="/")
}
####### START Estimate parameters (gamma_0, gamma, A, Beta)
for( itr.lambda.gamma in 1:length(lambda.gamma.candidate) )
{
lambda.gamma <- lambda.gamma.candidate[itr.lambda.gamma]
A <- A.ini
gamma0 <- gamma0.ini
gamma <- gamma.ini
Beta <- Beta.ini
for( itr.lambda.beta in 1:length(lambda.beta.candidate) )
{
lambda.beta <- lambda.beta.candidate[itr.lambda.beta]
para_old <- c(gamma0, gamma, matrix(Beta, 1, nrow(Beta)*ncol(Beta)))
para_new <- para_old + 10
if( adaptive==FALSE ){
# spcr.object = myfunc(x, y, A, Beta, gamma, gamma0, lambda.beta, lambda.gamma, xi, w)
spcr.object <- .Call( "spcr", x, y, A, Beta, gamma, gamma0, lambda.beta, lambda.gamma, xi, w )
Beta <- spcr.object[[1]]
gamma <- spcr.object[[2]]
gamma0 <- spcr.object[[3]]
A <- spcr.object[[4]]
} else {
spcr.object <- .Call( "spcr", x, y, A, Beta, gamma, gamma0, lambda.beta, lambda.gamma, xi, w )
Beta <- spcr.object[[1]]
gamma <- spcr.object[[2]]
gamma0 <- spcr.object[[3]]
A <- spcr.object[[4]]
BetaWeight <- Beta/sum(abs(Beta))
adaspcr.object <- .Call( "adaspcr", x, y, A, Beta, gamma, gamma0, lambda.beta, lambda.gamma, xi, w , BetaWeight)
Beta <- adaspcr.object[[1]]
gamma <- adaspcr.object[[2]]
gamma0 <- adaspcr.object[[3]]
A <- adaspcr.object[[4]]
}
para_old <- para_new
para_new <- c(gamma0, gamma, c(Beta))
if( mean(abs(para_new-para_old)) == 0 ) break
### CV-error
s_cv <- mean( ( y.test.cv - gamma0 - t(gamma) %*% t(Beta) %*% t(x.test.cv) )^2 )
### Strock of CV-error
CV.mat[ itr.lambda.gamma, itr.lambda.beta ] <- CV.mat[ itr.lambda.gamma, itr.lambda.beta ] + s_cv/nrow(x.test.cv)
}
}
}
CV.mat <- CV.mat/nfolds
### START Search of min CV
# minCandi.col <- whichiminCandi.col <- rep(0, nrow(CV.mat))
# for(i in 1:nrow(CV.mat))
# {
# whichiminCandi.col[i] <- which.min(CV.mat[i, ])
# minCandi.col[i] <- min(CV.mat[i, ])
# }
# minCandi.row <- whichiminCandi.row <- rep(0, ncol(CV.mat))
# for(i in 1:ncol(CV.mat))
# {
# whichiminCandi.row[i] <- which.min(CV.mat[ ,i])
# minCandi.row[i] <- min(CV.mat[ ,i])
# }
### END Search of min CV
### Selected tuning parameters by CV
# lambda.gamma.cv <- lambda.gamma.candidate[ whichiminCandi.row[ which.min(minCandi.row) ] ]
# lambda.beta.cv <- lambda.beta.candidate[ whichiminCandi.col[ which.min(minCandi.col) ] ]
### START Search of min CV
minCandi.col <- whichiminCandi.col <- rep(0, nrow(CV.mat))
for(i in 1:nrow(CV.mat))
{
whichiminCandi.col[i] <- which.min(CV.mat[i, ])
minCandi.col[i] <- min(CV.mat[i, ])
}
whichiminCandi.row <- which.min( CV.mat[ , whichiminCandi.col[ which.min(minCandi.col) ]] )
minCandi.row <- min( CV.mat[ , whichiminCandi.col[ which.min(minCandi.col) ]] )
### END Search of min CV
### Selected tuning parameters by CV
lambda.gamma.cv <- lambda.gamma.candidate[ whichiminCandi.row ]
lambda.beta.cv <- lambda.beta.candidate[ whichiminCandi.col[ which.min(minCandi.col) ] ]
ans <- list( lambda.gamma.seq=lambda.gamma.candidate, lambda.B.seq=lambda.beta.candidate, CV.mat=CV.mat, lambda.gamma.cv=lambda.gamma.cv, lambda.B.cv=lambda.beta.cv, cvm=min(minCandi.row), call=match.call() )
class(ans) <- "cv.spcr"
ans
}
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