62 lines
2.2 KiB
R
62 lines
2.2 KiB
R
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#' Compute eigenvalue problem equiv. to method.
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#'
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#' Computes the matrices \eqn{A, B} of a generalized eigenvalue problem
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#' \deqn{A X = B Lambda} with \eqn{X} the matrix of eigenvectors and
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#' \eqn{Lambda} diagonal matrix of eigenvalues.
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#'
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#' @param X predictor matrix.
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#' @param y responses (see details).
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#' @param method One of "pca", "pfc", ... TODO: finish, add more, ...
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#'
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#' @returns list of matrices \code{lhs} for \eqn{A} and \code{rhs} for \eqn{B}.
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#'
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#' @seealso section 2.1 of "Coordinate-Independent Sparse Sufficient Dimension
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#' Reduction and Variable Selection" By Xin Chen, Changliang Zou and
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#' R. Dennis Cook.
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#'
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GEP <- function(X, y, method = c('pfc', 'pca', 'sir', 'save'), ...,
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nr.slices = 10, ensamble = list(abs, identity, function(x) x^2)
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) {
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method <- match.arg(method)
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if (method == 'pca') {
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lhs <- cov(X) # covariance
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rhs <- diag(ncol(X)) # identity
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} else if (method == 'pfc') {
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X <- scale(X, scale = FALSE, center = TRUE)
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Fy <- sapply(ensamble, do.call, list(y))
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Fy <- scale(Fy, scale = FALSE, center = TRUE)
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# Compute Sigma_fit (the sample covariance matrix of the fitted vectors).
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P_Fy <- Fy %*% solve(crossprod(Fy), t(Fy))
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lhs <- crossprod(X, P_Fy %*% X) / nrow(X)
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# Estimate Sigma (the MLE sample covariance matrix).
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rhs <- crossprod(X) / nrow(X)
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} else if (method == 'sir') {
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if (NCOL(y) != 1) {
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stop('For SIR only univariate response suported.')
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}
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# Build slices (if not categorical)
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if (is.factor(y)) {
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slice.index <- y
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} else {
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# TODO: make this proper, just for a bit of playing!!!
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slice.size <- round(length(y) / nr.slices)
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slice.index <- factor((rank(y) - 1) %/% slice.size)
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}
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# Covariance of slice means, Cov(E[X - E X | y])
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lhs <- cov(t(sapply(levels(slice.index), function(i) {
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colMeans(X[slice.index == i, , drop = FALSE])
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})))
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# Sample covariance
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rhs <- cov(X)
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} else {
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stop('Not implemented!')
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}
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# Return left- and right-hand-side of GEP equation system.
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list(lhs = lhs, rhs = rhs)
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}
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