R/summaries.R

In bhmevik/pls: Partial Least Squares and Principal Component Regression

### summaries.R: print and summary methods.

## Print method for mvr objects:
#' @rdname summary.mvr
#' @export
print.mvr <- function(x, ...) {
    switch(x$method,
           kernelpls = {
               ana = "Partial least squares regression"
               alg = "kernel"
           },
           widekernelpls = {
               ana = "Partial least squares regression"
               alg = "wide kernel"
           },
           simpls = {
               ana = "Partial least squares regression"
               alg = "simpls"
           },
           oscorespls = {
               ana = "Partial least squares regression"
               alg = "orthogonal scores"
           },
           cppls = {
               ana = "Canonical powered partial least squares"
               alg = "cppls"
           },
           svdpc = {
               ana = "Principal component regression"
               alg = "singular value decomposition"
           },
           stop("Unknown fit method.")
           )
    cat(ana, ", fitted with the", alg, "algorithm.")
    if (!is.null(x$validation))
        cat("\nCross-validated using", length(x$validation$segments),
            attr(x$validation$segments, "type"), "segments.")
    cat("\nCall:\n", deparse(x$call), "\n", sep = "")
    invisible(x)
}

## Summary method for mvr objects


#' @name summary.mvr
#' @title Summary and Print Methods for PLSR and PCR objects
#'
#' @description Summary and print methods for \code{mvr} and \code{mvrVal} objects.
#'
#' @details If \code{what} is \code{"training"}, the explained variances are given; if
#' it is \code{"validation"}, the cross-validated RMSEPs (if available) are
#' given; if it is \code{"all"}, both are given.
#'
#' @aliases summary.mvr print.mvr print.mvrVal
#' @param x,object an \code{mvr} object
#' @param what one of \code{"all"}, \code{"validation"} or \code{"training"}
#' @param digits integer.  Minimum number of significant digits in the output.
#' Default is 4.
#' @param print.gap Integer.  Gap between coloumns of the printed tables.
#' @param \dots Other arguments sent to underlying methods.
#' @return \code{print.mvr} and \code{print.mvrVal} return the object
#' invisibly.
#' @author Ron Wehrens and Bjørn-Helge Mevik
#' @seealso \code{\link{mvr}}, \code{\link{pcr}}, \code{\link{plsr}},
#' \code{\link{RMSEP}}, \code{\link{MSEP}}
#' @keywords regression multivariate
#' @examples
#'
#' data(yarn)
#' nir.mvr <- mvr(density ~ NIR, ncomp = 8, validation = "LOO", data = yarn)
#' nir.mvr
#' summary(nir.mvr)
#' RMSEP(nir.mvr)
#'
#' @export
summary.mvr <- function(object, what = c("all", "validation", "training"),
                        digits = 4, print.gap = 2, ...)
{
    what <- match.arg(what)
    if (what == "all") what <- c("validation", "training")
    if (is.null(object$validation)) what <- "training"

    nobj <- nrow(object$scores)
    nresp <- length(object$Ymeans)
    yvarnames <- respnames(object)
    cat("Data: \tX dimension:", nobj, length(object$Xmeans),
        "\n\tY dimension:", nobj, nresp)
    cat("\nFit method:", object$method)
    cat("\nNumber of components considered:", object$ncomp)

    for (wh in what) {
        if (wh == "training") {
            cat("\nTRAINING: % variance explained\n")
            xve <- explvar(object)
            yve <- 100 * drop(R2(object, estimate = "train",
                                 intercept = FALSE)$val)
            tbl <- rbind(cumsum(xve), yve)
            dimnames(tbl) <- list(c("X", yvarnames),
                                  paste(1:object$ncomp, "comps"))
            print(tbl, digits = digits, print.gap = print.gap, ...)
        } else {
            cat("\n\nVALIDATION: RMSEP")
            cat("\nCross-validated using", length(object$validation$segments),
                attr(object$validation$segments, "type"), "segments.\n")
            print(RMSEP(object), digits = digits, print.gap = print.gap, ...)
        }
    }
}

## Print method for mvrVal objects:
#' @rdname summary.mvr
#' @export
print.mvrVal <- function(x, digits = 4, print.gap = 2, ...) {
    nresp <- dim(x$val)[2]
    yvarnames <- dimnames(x$val)[[2]]
    names(dimnames(x$val)) <- NULL
    for (i in 1:nresp) {
        if (nresp > 1) cat("\nResponse:", yvarnames[i], "\n")
        print(x$val[,i,], digits = digits, print.gap = print.gap, ...)
    }
    invisible(x)
}