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cvSCAD <- function(logX, groupings, DIR, params=list(seed=123, ncv=5, repeats=10,maxiter=1000, maxevals=500, fs.method="scad")) {
# output directory
#fs.method <- "scad"
fs.method <- params$fs.method
seed <- params$seed
ncv <- params$ncv
repeats <- params$repeats
maxiter <- params$maxiter
maxevals <- params$maxevals
jitter <- params$jitter
## introduce some minimal noise to make scaling etc. possible
if(jitter) {
logX <- jitter(logX)
}
if(!is.null(DIR)) {
SUBDIR <- paste(DIR,fs.method,sep="/")
if(!file.exists(SUBDIR))
dir.create(SUBDIR)
fnames <- paste(SUBDIR, "/", names(groupings), ".pdf", sep="")
} else {
SUBDIR <- NULL
fnames <- rep("-", length(groupings))
}
## grouping information
X <- lapply(1:length(groupings), function(i,groupings,fnames) list(groupings[[i]], fnames[i]), groupings=groupings, fnames=fnames)
names(X) <- names(groupings)
## use multicores if more than one group is to be classified
useparallel <- length(grep("package:(parallel|multicore)", search())>0)
if(length(X)>1 & useparallel) {
resSCAD <- mclapply(X, svmclass, logX=logX, ncv=ncv, repeats=repeats, maxiter=maxiter, maxevals=maxevals, fs.method=fs.method, seed=seed, mc.preschedule=TRUE, mc.cores=length(X))
} else {
resSCAD <- lapply(X, svmclass, logX=logX, ncv=ncv, repeats=repeats, maxiter=maxiter, maxevals=maxevals, fs.method=fs.method, seed=seed)
}
allpr <- colnames(logX)
ffmat <- extract_feature_rankings(resSCAD, allpr, write=ifelse(is.null(SUBDIR), FALSE, TRUE), DIR=SUBDIR)
## extract the performance objects
performance <- lapply(resSCAD, function(x) x$performance)
names(performance) <- names(X)
if(!is.null(SUBDIR)) {
save(resSCAD, allpr, ffmat, X, SUBDIR, logX, ncv, repeats, maxiter, maxevals, fs.method, file=paste(SUBDIR, "env.RData", sep="/"))
}
list(res=resSCAD, featlist=ffmat, performance=performance)
}
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