R/PcaClassic.R

In armstrtw/rrcov: Scalable Robust Estimators with High Breakdown Point

##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)
}