[Analogue-commits] r275 - pkg/R

[noreply at r-forge.r-project.org]

Author: gsimpson
Date: 2012-07-30 21:29:19 +0200 (Mon, 30 Jul 2012)
New Revision: 275

Added:
   pkg/R/crossval.wa.R
Log:
break out crossval wa method into own file

Added: pkg/R/crossval.wa.R
===================================================================
--- pkg/R/crossval.wa.R	                        (rev 0)
+++ pkg/R/crossval.wa.R	2012-07-30 19:29:19 UTC (rev 275)
@@ -0,0 +1,129 @@
+## crossval method for wa()
+`crossval.wa` <- function(obj, method = c("LOO","kfold","bootstrap"),
+                          nboot = 100, nfold = 10, folds = 5,
+                          verbose = getOption("verbose"), ...) {
+    method <- match.arg(method)
+    X <- obj$orig.x
+    ENV <- obj$orig.env
+    N <- NROW(X)
+    M <- NCOL(X)
+    tolOpts <- obj$options.tol
+    Dtype <- obj$deshrink
+    if(identical(method, "LOO")) {
+        pred <- numeric(N)
+        nr <- N-1 ## number of rows - 1 for LOO
+        if(verbose) {
+            writeLines("\n  LOO Cross-validation:")
+            pb <- txtProgressBar(min = 0, max = nr, style = 3)
+            on.exit(close(pb))
+            on.exit(cat("\n"), add = TRUE)
+        }
+        for(i in seq_along(pred)) {
+            if(verbose)
+                setTxtProgressBar(pb, i)
+            opt <- w.avg(X[-i, ], ENV[-i])
+            if(obj$tol.dw)
+                pred[i] <- predWAT(X, ENV, i, opt, tolOpts, nr, M,
+                                   Dtype)
+            else
+                pred[i] <- predWA(X, ENV, i, opt, Dtype)
+        }
+    }
+    if(identical(method, "kfold")) {
+        oob.pred <- matrix(NA, ncol = folds, nrow = N)
+        if(verbose) {
+            writeLines("\n   n k-fold Cross-validation:")
+            pb <- txtProgressBar(min = 0, max = folds, style = 3)
+            on.exit(close(pb))
+            on.exit(cat("\n"), add = TRUE)
+        }
+        ind <- rep(seq_len(nfold), length = N) ## k-fold group indicator
+        ## n k-fold s
+        for(i in seq_len(folds)) {
+            if(verbose)
+                setTxtProgressBar(pb, i)
+            ## do a k-fold CV
+            pind <- ind[sample.int(N, N, replace = FALSE)] ## sure this should be replace = FALSE
+            for(k in seq_len(nfold)) {
+                sel <- pind == k   ## sel is samples in leave out group
+                N.oob <- sum(sel) ## N in leave out group
+                N.mod <- sum(!sel)  ## N in the model
+                sel <- which(sel) ## convert to indices
+                opt <- w.avg(X[-sel, , drop = FALSE], ENV[-sel])
+                if(obj$tol.dw) {
+                    oob.pred[sel, i] <- predWAT(X, ENV, sel, opt, tolOpts,
+                                                N.mod, M, Dtype)
+                } else {
+                    oob.pred[sel, i] <- predWA(X, ENV, sel, opt, Dtype)
+                }
+            }
+        }
+        pred <- rowMeans(oob.pred, na.rm = TRUE)
+    }
+    if(identical(method, "bootstrap")) {
+        oob.pred <- matrix(NA, ncol = nboot, nrow = N)
+        if(verbose) {
+            writeLines("\n   Bootstrap Cross-validation:")
+            pb <- txtProgressBar(min = 0, max = nboot, style = 3)
+            on.exit(close(pb))
+            on.exit(cat("\n"), add = TRUE)
+        }
+        ind <- seq_len(N) ## indicator for samples
+        for(i in seq_len(nboot)) {
+            if(verbose)
+                setTxtProgressBar(pb, i)
+            bSamp <- sample.int(N, N, replace = TRUE)
+            sel <- which(!ind %in% bSamp) ## need indices!!!
+            N.oob <- NROW(X[sel, , drop = FALSE])
+            N.mod <- N - N.oob
+            opt <- w.avg(X[-sel, , drop = FALSE], ENV[-sel])
+            if(obj$tol.dw)
+                oob.pred[sel, i] <- predWAT(X, ENV, sel, opt, tolOpts,
+                                             N.mod, M, Dtype)
+            else
+                oob.pred[sel, i] <- predWA(X, ENV, sel, opt, Dtype)
+        }
+        pred <- rowMeans(oob.pred, na.rm = TRUE)
+    }
+    resid <- ENV - pred
+    out <- list(fitted.values = pred, residuals = resid)
+    performance <- data.frame(R2 = cor(pred, ENV)^2,
+                              avgBias = mean(resid),
+                              maxBias = unname(maxBias(resid, ENV)),
+                              RMSEP = sqrt(mean(resid^2)),
+                              RMSEP2 = NA,
+                              s1 = NA,
+                              s2 = NA)
+    if(identical(method, "bootstrap") ||
+       (identical(method, "kfold") && folds > 1)) {
+        performance$s1 <- sqrt(mean(apply(oob.pred, 1, sd, na.rm = TRUE)^2))
+        performance$s2 <- sqrt(mean(resid^2))
+        performance$RMSEP2 <- sqrt(performance$s1^2 + performance$s2^2)
+    }
+    out$performance <- performance
+    .call <- match.call()
+    .call[[1]] <- as.name("crossval")
+    out$call <- .call
+    out$CVparams <- list(method = method, nboot = nboot, nfold = nfold,
+                         folds = folds)
+    class(out) <- "crossval"
+    out
+}
+
+`predWAT` <- function(X, ENV, i, optima, tolOpts, nr, nc,
+                      deSh) {
+    tol <- w.tol(X[-i, ], ENV[-i], optima, tolOpts$useN2)
+    tol <- fixUpTol(tol, tolOpts$na.tol, tolOpts$small.tol,
+                    tolOpts$min.tol, tolOpts$f, ENV[-i])
+    wa.env <- WATpred(X[-i, ], optima, tol, nr, nc)
+    p <- WATpred(X[i, , drop = FALSE], optima, tol, 1, nc)
+    deMod <- deshrink(ENV[-i], wa.env, deSh)
+    deshrinkPred(p, coef(deMod), deSh)
+}
+
+`predWA` <- function(X, ENV, i, optima, deSh) {
+    wa.env <- WApred(X[-i, ], optima)
+    p <- WApred(X[i, , drop = FALSE], optima)
+    deMod <- deshrink(ENV[-i], wa.env, deSh)
+    deshrinkPred(p, coef(deMod), deSh)
+}