R/trainind.cv.R

In wilsontom/FIEmspro: Flow In-jection Electrospray Mass Spectrometry Processing: \\ data processing, classification modelling and variable selection in metabolite fingerprinting

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#' Constrained Generation of Training Samples Indices
#' 
#' Special case of cross-validation where the fold are defined by the user.
#' Data are partitioned according to values contained in \code{cv}.
#' 
#' 
#' @usage trainind.cv(cv)
#' @param cv Vector containing the fold information
#' @return Returns a list of training index.
#' @author David Enot \email{dle@@aber.ac.uk}
#' @seealso \code{\link{trainind}},\code{\link{valipars}}, \code{\link{accest}}
#' @keywords manip
#' @examples
#' 
#' ## Load abr1
#' data(abr1)
#' 
#' ## cl is the class of interest
#' cl  <- factor(abr1$fact$class)
#' dat <- preproc(abr1$pos , y=cl, method=c("log10","TICnorm"),add=1)[,110:500]  
#' 
#' ## For illustration, we use sample replicates id to form the CV folds
#' re  <- factor(abr1$fact$rep)
#' 
#' ## Check representativity of cl in each fold
#' table(re,cl)
#' 
#' ## Generate a trainind object using re
#' tr.idx <- trainind.cv(re)
#' pars   <- valipars(sampling="cv", niter=1, nreps=5)
#' 
#' ## for fold num. 1
#' ## check sample indices and replicates ids in the training set
#' tmp    <- tr.idx[[1]]
#' cl[tmp[[1]]];table(re[tmp[[1]]])     ## train idx
#' ## and in the test set
#' cl[-tmp[[1]]];table(re[-tmp[[1]]])   ## test idx
#' 
#' ##  accuracy estimation with constrained CV
#' acc  <- accest(dat, cl, pars = pars, clmeth = "nlda", tr.idx=tr.idx)
#' acc
#' ## compare it with random CV
#' pars.rnd   <- valipars(sampling="cv", niter=10, nreps=5)
#' tr.idx.rnd <- trainind(cl,pars.rnd)
#' acc.rnd  <- accest(dat, cl, pars = pars.rnd, clmeth = "nlda", tr.idx=tr.idx.rnd)
#' 
#' ## plot the histogram of the accuracies at each iteration
#' hist(acc.rnd$acc.iter)
#' 
#' 
trainind.cv <- function(cv)
{
  tmp <- NULL
  k = 1
  for(i in unique(cv)){
    tmp[[k]]=which(cv!=i)
    k = k+1
  }
  list(Iter_1=tmp)
}