37 lines
1023 B
R
37 lines
1023 B
R
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#' Sliced Inverse Regression
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#'
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#' @export
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SIR <- function(X, y, d, nr.slices = 10L, slice.method = c("cut", "ecdf")) {
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if (!(is.factor(y) || is.integer(y))) {
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slice.method <- match.arg(slice.method)
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if (slice.method == "ecdf") {
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y <- cut(ecdf(y)(y), nr.slices)
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} else {
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y <- cut(y, nr.slices)
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# ensure there are no empty slices
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if (any(table(y) == 0)) {
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y <- as.factor(as.integer(y))
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}
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}
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}
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# Center `X`
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Z <- scale(X, scale = FALSE)
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# Split `Z` into slices determined by `y`
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slices <- Map(function(i) Z[i, , drop = FALSE], split(seq_along(y), y))
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# Sizes and Means for each slice
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slice.sizes <- mapply(nrow, slices)
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slice.means <- Map(colMeans, slices)
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# Inbetween slice covariances
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sCov <- Reduce(`+`, Map(function(mean_s, n_s) {
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n_s * tcrossprod(mean_s)
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}, slice.means, slice.sizes)) / nrow(X)
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# Compute EDR directions
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La.svd(sCov, d, 0L)$u
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}
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