svdpc.fit.r

做主成分回归和偏最小二乘回归

### svdpc.fit.R: SVD PC fit algorithm
### $Id: svdpc.fit.R 108 2007-03-19 17:46:06Z bhm $

svdpc.fit <- function(X, Y, ncomp, stripped = FALSE, ...)
{
    Y <- as.matrix(Y)
    if (!stripped) {
        ## Save dimnames:
        dnX <- dimnames(X)
        dnY <- dimnames(Y)
    }
    ## Remove dimnames during calculation  (doesn't seem to matter; in fact,
    ## as far as it has any effect, it hurts a tiny bit in most situations).
    ## dimnames(X) <- dimnames(Y) <- NULL

    nobj <- dim(X)[1]
    npred <- dim(X)[2]
    nresp <- dim(Y)[2]

    B <- array(0, dim = c(npred, nresp, ncomp))
    if (!stripped) fitted <- array(0, dim = c(nobj, nresp, ncomp))

    ## Center variables:
    Xmeans <- colMeans(X)
    X <- X - rep(Xmeans, each = nobj)
    Ymeans <- colMeans(Y)
    Y <- Y - rep(Ymeans, each = nobj)

    huhn <- La.svd(X)
    D <- huhn$d[1:ncomp]
    TT <- huhn$u[,1:ncomp, drop=FALSE] %*% diag(D, nrow = ncomp)
    P <- t(huhn$vt[1:ncomp,, drop=FALSE])
    tQ <- crossprod(TT, Y) / D^2

    for (a in 1:ncomp) {
        B[,,a] <- P[,1:a, drop=FALSE] %*% tQ[1:a,]
        if (!stripped) fitted[,,a] <- TT[,1:a, drop=FALSE] %*% tQ[1:a,]
    }

    if (stripped) {
        ## Return as quickly as possible
        list(coefficients = B, Xmeans = Xmeans, Ymeans = Ymeans)
    } else {
        residuals <- c(Y) - fitted
        fitted <- fitted + rep(Ymeans, each = nobj) # Add mean

        ## Add dimnames and classes:
        objnames <- dnX[[1]]
        if (is.null(objnames)) objnames <- dnY[[1]]
        prednames <- dnX[[2]]
        respnames <- dnY[[2]]
        compnames <- paste("Comp", 1:ncomp)
        nCompnames <- paste(1:ncomp, "comps")
        dimnames(TT) <- list(objnames, compnames)
        dimnames(P) <- list(prednames, compnames)
        dimnames(tQ) <- list(compnames, respnames)
        dimnames(B) <- list(prednames, respnames, nCompnames)
        dimnames(fitted) <- dimnames(residuals) <-
            list(objnames, respnames, nCompnames)
        names(D) <- compnames
        class(TT) <- "scores"
        R <- P                          # To avoid class "loadings" on projection
        class(P) <- class(tQ) <- "loadings"

        list(coefficients = B,
             scores = TT, loadings = P,
             Yloadings = t(tQ),
             projection = R,
             Xmeans = Xmeans, Ymeans = Ymeans,
             fitted.values = fitted, residuals = residuals,
             Xvar = D^2, Xtotvar = sum(X * X))
    }
}