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##' @title Statistical description before using function ispls
##'
##' @description The function describes the basic statistical information of the data, including sample mean, sample variance of X and Y, the first direction of partial least squares method, etc.
##'
##' @param x list of data matrices, L datasets of explanatory variables.
##' @param y list of data matrices, L datasets of dependent variables.
##' @param L numeric, number of datasets.
##' @param scale.x character, "TRUE" or "FALSE", whether or not to scale the variables x. The default is TRUE.
##' @param scale.y character, "TRUE" or "FALSE", whether or not to scale the variables y. The default is TRUE.
##'
##' @return A 'preview.pls' object that contains the list of the following items.
##' \itemize{
##' \item{x:}{ list of data matrices, L datasets of explanatory variables with centered columns. If scale.x is TRUE, the columns of L datasets are standardized to have mean 0 and standard deviation 1.}
##' \item{y:}{ list of data matrices, L datasets of dependent variables with centered columns. If scale.y is TRUE, the columns of L datasets are standardized to have mean 0 and standard deviation 1.}
##' \item{loading:}{ the estimated first direction vector.}
##' \item{meanx:}{ list of numeric vectors, column mean of the original datasets x.}
##' \item{normx:}{ list of numeric vectors, column standard deviation of the original datasets x.}
##' \item{meany:}{ list of numeric vectors, column mean of the original datasets y.}
##' \item{normy:}{ list of numeric vectors, column standard deviation of the original datasets y.}
##' }
##'
##' @seealso See Also as \code{\link{ispls}}.
##'
##' @import caret
##' @import irlba
##' @import graphics
##' @import stats
##' @importFrom grDevices rainbow
##' @export
##' @examples
##' library(iSFun)
##' data("simData.pls")
##' x <- simData.pls$x
##' y <- simData.pls$y
##' L <- length(x)
##'
##' prev_pls <- preview.pls(x = x, y = y, L = L, scale.x = TRUE, scale.y = TRUE)
preview.pls <- function(x, y, L, scale.x = TRUE, scale.y = TRUE){
if (class(x) != "list") { stop("x should be of list type.") }
if (class(y) != "list") { stop("y should be of list type.") }
# initialization
x <- lapply(x, as.matrix)
y <- lapply(y, as.matrix)
nl <- as.numeric(lapply(x, nrow))
pl <- as.numeric(lapply(x, ncol))
ql <- as.numeric(lapply(y, ncol))
p <- unique(pl)
q <- unique(ql)
if(length(p) > 1){ stop("The dimension of data x should be consistent among different datasets.")}
if(length(q) > 1){ stop("The dimension of data y should be consistent among different datasets.")}
ip <- c(1:p)
# center & scale x & y
meanx <- lapply(1:L, function(l) drop( matrix(1, 1, nl[l]) %*% x[[l]] / nl[l] ) )
meany <- lapply(1:L, function(l) drop( matrix(1, 1, nl[l]) %*% y[[l]] / nl[l] ) )
x <- lapply(1:L, function(l) scale(x[[l]], meanx[[l]], FALSE) )
y <- lapply(1:L, function(l) scale(y[[l]], meany[[l]], FALSE) )
x.scale <- function(l){
one <- matrix(1, 1, nl[l])
normx <- sqrt(drop(one %*% (x[[l]]^2)) / (nl[l] - 1))
if (any(normx < .Machine$double.eps)) {
stop("Some of the columns of the predictor matrix have zero variance.")
}
return(normx)
}
y.scale <- function(l){
one <- matrix(1, 1, nl[l])
normy <- sqrt(drop(one %*% (y[[l]]^2)) / (nl[l] - 1))
if (any(normy < .Machine$double.eps)) {
stop("Some of the columns of the response matrix have zero variance.")
}
return(normy)
}
if (scale.x) { normx <- lapply(1:L, x.scale ) } else { normx <- rep(list(rep(1,p)), L) }
if (scale.y) { normy <- lapply(1:L, y.scale ) } else { normy <- rep(list(rep(1,q)), L) }
if (scale.x) { x <- lapply(1:L, function(l) scale(x[[l]], FALSE, normx[[l]]) ) }
if (scale.y) { y <- lapply(1:L, function(l) scale(y[[l]], FALSE, normy[[l]]) ) }
fun.1 <- function(l) {
Z_l <- t(x[[l]]) %*% y[[l]]
Z_l <- Z_l / nl[l]
}
Z <- matrix(mapply(fun.1, c(1:L)), nrow = p)
c <- mapply(function(l) svd(Z[, ((l - 1) * q + 1):(l * q)] %*% t(Z[, ((l - 1) * q + 1):(l * q)] ), nu = 1)$u, 1:L)#/ (nl[l]^2)
# normalization
what <- c
listname <- mapply(function(l) paste("Dataset ", l), 1:L)
names(meanx) <- listname
names(meany) <- listname
names(normx) <- listname
names(normy) <- listname
names(x) <- listname
names(y) <- listname
for (l in 1:L) {
plot(x = 1:p, y = as.numeric(what[,l]),
main = paste("Dataset ", l, "\n", "The first direction vector"),
xlab = "Dimension", ylab = "Value", pch = 15)
}
# return objects
object <- list(
x = x, y = y, loading = what,
meanx = meanx, normx = normx, meany = meany, normy = normy
)
class(object) <- "preview.spls"
return(object)
}
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