kernelpls.fit.r

做主成分回归和偏最小二乘回归

### kernelpls.fit.R: Kernel PLS fit algorithm for tall data.
### $Id: kernelpls.fit.R 89 2006-09-20 15:41:09Z bhm $
###
### Implements an adapted version of the `algorithm 1' described in
###   Dayal, B. S. and MacGregor, J. F. (1997) Improved PLS algorithms.
###   \emph{Journal of Chemometrics}, \bold{11}, 73--85.
### (This is a modification of the algorithm described in
###   Lindgren F, Geladi P, Wold S (1993) The kernel algorithm for PLS.
###   J. Chemometrics 7, 45-59,
### incorporating the changes in
###   de Jong, S. and ter Braak,  C. J. F. (1994) Comments on the PLS kernel
###   algorithm.  \emph{Journal of Chemometrics}, \bold{8}, 169--174.

kernelpls.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 make a
    ## difference here (2.3.0).)
    dimnames(X) <- dimnames(Y) <- NULL

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

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

    ## Projection, loadings
    R <- P <- matrix(0, ncol = ncomp, nrow = npred)
    tQ <- matrix(0, ncol = nresp, nrow = ncomp)# Y loadings; transposed
    B <- array(0, c(npred, nresp, ncomp))

    if (!stripped) {
        W <- P                        # Loading weights
        U <- TT <- matrix(0, ncol = ncomp, nrow = nobj)# scores
        tsqs <- rep.int(1, ncomp)       # t't
        fitted <- array(0, c(nobj, nresp, ncomp))
    }

    ## 1.
    XtY <- crossprod(X, Y)

    for (a in 1:ncomp) {
        ## 2.
        if (nresp == 1) {
            w.a <- XtY / sqrt(c(crossprod(XtY)))
        } else {
            if (nresp < npred) {
                ## FIXME: is q proportional to q.a?
                q <- eigen(crossprod(XtY), symmetric = TRUE)$vectors[,1]
                w.a <- XtY %*% q
                w.a <- w.a / sqrt(c(crossprod(w.a)))
            } else {
                w.a <- eigen(XtY %*% t(XtY), symmetric = TRUE)$vectors[,1]
            }
        }

        ## 3.
        r.a <- w.a
        if (a > 5) {
            ## This is faster when a > 5:
            r.a <- r.a - colSums(crossprod(w.a, P[,1:(a-1), drop=FALSE]) %*%
                               t(R[,1:(a-1), drop=FALSE]))
        } else if (a > 1) {
            for (j in 1:(a - 1))
                r.a <- r.a - (P[,j] %*% w.a) * R[,j]
        }

        ## 4.
        t.a <- X %*% r.a
        tsq <- c(crossprod(t.a))
        p.a <- crossprod(X, t.a) / tsq
        q.a <- crossprod(XtY, r.a) / tsq

        ## 5.
        XtY <- XtY - (tsq * p.a) %*% t(q.a)

        ## 6.-8.
        R[,a] <- r.a
        P[,a] <- p.a
        tQ[a,] <- q.a
        B[,,a] <- R[,1:a, drop=FALSE] %*% tQ[1:a,, drop=FALSE]
        if (!stripped) {
            tsqs[a] <- tsq
            ## Extra step to calculate Y scores:
            u.a <- Y %*% q.a / c(crossprod(q.a)) # Ok for nresp == 1 ??
            ## make u orth to previous X scores:
            if (a > 1) u.a <- u.a - TT %*% (crossprod(TT, u.a) / tsqs)
            U[,a] <- u.a
            TT[,a] <- t.a
            W[,a] <- w.a
            ## (For very tall, slim X and Y, X %*% B[,,a] is slightly faster
            ## due to less overhead.)
            fitted[,,a] <- TT[,1:a] %*% tQ[1:a,, drop=FALSE]
        }
    }

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

        ## Add dimnames:
        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) <- dimnames(U) <- list(objnames, compnames)
        dimnames(R) <- dimnames(W) <- dimnames(P) <-
            list(prednames, compnames)
        dimnames(tQ) <- list(compnames, respnames)
        dimnames(B) <- list(prednames, respnames, nCompnames)
        dimnames(fitted) <- dimnames(residuals) <-
            list(objnames, respnames, nCompnames)
        class(TT) <- class(U) <- "scores"
        class(P) <- class(W) <- class(tQ) <- "loadings"

        list(coefficients = B,
             scores = TT, loadings = P,
             loading.weights = W,
             Yscores = U, Yloadings = t(tQ),
             projection = R,
             Xmeans = Xmeans, Ymeans = Ymeans,
             fitted.values = fitted, residuals = residuals,
             Xvar = colSums(P * P) * tsqs,
             Xtotvar = sum(X * X))
    }
}