##setGeneric("PcaClassic", function(x, ...) standardGeneric("PcaClassic"))
##setMethod("PcaClassic", "formula", PcaClassic.formula)
##setMethod("PcaClassic", "ANY", PcaClassic.default)
setMethod("getQuan", "PcaClassic", function(obj) obj@n.obs)
## The S3 version
PcaClassic <- function (x, ...) UseMethod("PcaClassic")
PcaClassic.formula <- function (formula, data = NULL, subset, na.action, ...)
{
cl <- match.call()
mt <- terms(formula, data = data)
if (attr(mt, "response") > 0)
stop("response not allowed in formula")
mf <- match.call(expand.dots = FALSE)
mf$... <- NULL
mf[[1]] <- as.name("model.frame")
mf <- eval.parent(mf)
## this is not a `standard' model-fitting function,
## so no need to consider contrasts or levels
if (.check_vars_numeric(mf))
stop("PCA applies only to numerical variables")
na.act <- attr(mf, "na.action")
mt <- attr(mf, "terms")
attr(mt, "intercept") <- 0
x <- model.matrix(mt, mf)
res <- PcaClassic.default(x, ...)
## fix up call to refer to the generic, but leave arg name as `formula'
cl[[1]] <- as.name("PcaClassic")
res@call <- cl
# if (!is.null(na.act)) {
# res$na.action <- na.act
# if (!is.null(sc <- res$x))
# res$x <- napredict(na.act, sc)
# }
res
}
PcaClassic.default <- function(x, k=0, kmax=ncol(x), scale=FALSE, signflip=TRUE, trace=FALSE, ...)
{
cl <- match.call()
if(missing(x)){
stop("You have to provide at least some data")
}
data <- as.matrix(x)
n <- nrow(data)
p <- ncol(data)
Xsvd <- kernelEVD(data, scale=scale, signflip=signflip)
if(Xsvd$rank == 0) {
stop("All data points collapse!")
}
if(trace)
{
cat("\nDimension of the input matrix x:\n", dim(x))
cat("\nInput parameters [k, kmax, rank(x)]: ", k, kmax, Xsvd$rank, "\n")
}
##
## verify and set the input parameters: k and kmax
##
kmax <- max(min(kmax, Xsvd$rank),1)
if((k <- floor(k)) < 0)
k <- 0
else if(k > kmax) {
warning(paste("The number of principal components k = ", k, " is larger then kmax = ", kmax, "; k is set to ", kmax,".", sep=""))
k <- kmax
}
if(k != 0)
k <- min(k, ncol(data))
else {
k <- min(kmax, ncol(data))
if(trace)
cat("The number of principal components is set to ", k, ".\n", sep="")
}
if(trace)
cat("\nTo be used [k, kmax, ncol(data), rank(data)]=",k, kmax, ncol(data), Xsvd$rank, "\n")
loadings <- Xsvd$loadings[, 1:k, drop=FALSE]
eigenvalues <- as.vector(Xsvd$eigenvalues[1:k])
center <- as.vector(Xsvd$center)
scores <- Xsvd$scores[, 1:k, drop=FALSE]
scale <- Xsvd$scale
if(is.list(dimnames(data)) && !is.null(dimnames(data)[[1]]))
{
dimnames(scores)[[1]] <- dimnames(data)[[1]]
} else {
dimnames(scores)[[1]] <- 1:n
}
dimnames(scores)[[2]] <- as.list(paste("PC", seq_len(ncol(scores)), sep = ""))
dimnames(loadings) <- list(colnames(data), paste("PC", seq_len(ncol(loadings)), sep = ""))
## fix up call to refer to the generic, but leave arg name as 'formula'
cl[[1]] <- as.name("PcaClassic")
res <- new("PcaClassic", call=cl,
loadings=loadings,
eigenvalues=eigenvalues,
center=center,
scale=scale,
scores=scores,
k=k,
n.obs=n)
## Compute distances and flags
res <- .distances(data, Xsvd$rank, res)
return(res)
}
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