#' Ensemble Partial Least Squares Regression
#'
#' This function performs ensemble partial least squares regression.
#'
#' This function performs ensemble partial least squares regression.
#'
#' @param x predictor matrix
#' @param y response vector
#' @param maxcomp Maximum number of components included within the models,
#' if not specified, default is the variable (column) numbers in x.
#' @param MCtimes times of Monte-Carlo
#' @param method \code{"mc"} or \code{"bootstrap"}. Default is \code{"mc"}.
#' @param ratio sample ratio used when \code{method = "mc"}
#' @param parallel Integer. Number of parallel processes to use.
#' Default is \code{1}, which means run serially.
#'
#' @return A list containing all PLS model objects.
#'
#' @author Min-feng Zhu <\email{wind2zhu@@163.com}>,
#' Nan Xiao <\email{road2stat@@gmail.com}>
#'
#'
#' @seealso See \code{\link{enpls.fs}} for feature selection with ensemble PLS.
#' See \code{\link{enpls.od}} for outlier detection with ensemble PLS. See
#' \code{\link{enpls.ad}} for applicability domain with ensemble PLS.
#'
#' @export enpls.en
#'
#' @importFrom doParallel registerDoParallel
#' @importFrom foreach foreach "%dopar%"
#'
#' @references
#' Dongsheng Cao, Yizeng Liang, Qingsong Xu, Yifeng Yun, and Hongdong Li.
#' "Toward better QSAR/QSPR modeling: simultaneous outlier detection and
#' variable selection using distribution of model features."
#' \emph{Journal of computer-aided molecular design} 25, no. 1 (2011): 67--80.
#'
#' @examples
#' data(alkanes)
#' x = alkanes$x
#' y = alkanes$y
#'
#' set.seed(42)
#' enpls.fit = enpls.en(x, y, MCtimes = 10)
#' print(enpls.fit)
#' predict(enpls.fit, newx = x)
enpls.en = function(x, y,
maxcomp = NULL,
MCtimes = 500L,
method = c('mc', 'bootstrap'), ratio = 0.8,
parallel = 1L) {
if (is.null(maxcomp)) maxcomp = ncol(x)
method = match.arg(method)
x.row = nrow(x)
samp.idx = vector('list', MCtimes)
if (method == 'mc') {
for (i in 1L:MCtimes) samp.idx[[i]] = sample(1L:x.row, floor(x.row * ratio))
}
if (method == 'bootstrap') {
for (i in 1L:MCtimes) samp.idx[[i]] = sample(1L:x.row, x.row, replace = TRUE)
}
if (parallel < 1.5) {
modellist = vector('list', MCtimes)
for (i in 1L:MCtimes) {
plsdf.x = x[samp.idx[[i]], ]
plsdf.y = y[samp.idx[[i]]]
modellist[[i]] = suppressWarnings(enpls.en.core(plsdf.x, plsdf.y, maxcomp))
}
} else {
registerDoParallel(parallel)
modellist = foreach(i = 1L:MCtimes) %dopar% {
x = x[samp.idx[[i]], ]
y = y[samp.idx[[i]]]
enpls.en.core(x, y, maxcomp)
}
}
class(modellist) = 'enpls.en'
return(modellist)
}
#' core function for enpls.en
#'
#' select the best ncomp with cross-validation and
#' use it to fit the complete training set again.
#' scale = TRUE
#'
#' @return the coefficients
#'
#' @keywords internal
enpls.en.core = function(plsdf.x, plsdf.y, maxcomp) {
num = apply(plsdf.x, 2L, function(x) sum(x == 0))
cv.ratio = num/dim(plsdf.x)[1]
weight = which(cv.ratio > 0.7)
if(length(weight) > 0) {
plsdf.x = plsdf.x[, -weight]
}
if(maxcomp > ncol(plsdf.x)) maxcomp = ncol(plsdf.x)
plsr.cvfit = plsr(plsdf.y ~ ., data = data.frame(plsdf.x, plsdf.y),
ncomp = maxcomp,
scale = TRUE,
method = 'simpls',
validation = 'CV', segments = 5L)
# choose best component number using adjusted CV
cv.bestcomp = which.min(RMSEP(plsr.cvfit)[['val']][2L, 1L, -1L])
plsr.fit = plsr(plsdf.y ~ ., data = data.frame(plsdf.x, plsdf.y),
ncomp = cv.bestcomp,
scale = TRUE,
method = 'simpls',
validation = 'none')
enpls.core.fit = list(plsr.fit, cv.bestcomp) # save cv.bestcomp for predict.enpls
return(enpls.core.fit)
}
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