[Analogue-commits] r251 - pkg/R

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

Author: gsimpson
Date: 2012-02-09 13:41:40 +0100 (Thu, 09 Feb 2012)
New Revision: 251

Modified:
   pkg/R/bootstrap.R
   pkg/R/crossval.R
   pkg/R/internal.R
   pkg/R/predict.wa.R
Log:
replace .Internal(sample(...)) usage with sample.int()

Modified: pkg/R/bootstrap.R
===================================================================
--- pkg/R/bootstrap.R	2012-01-11 14:29:38 UTC (rev 250)
+++ pkg/R/bootstrap.R	2012-02-09 12:41:40 UTC (rev 251)
@@ -54,9 +54,7 @@
     for(i in 1:n.boot)
       {
         ## draw a bootstrap sample of size n.train
-        ##samp.boot <- sample(1:n.train, replace = TRUE)
-        samp.boot <- .Internal(sample(n.train, n.train,
-                                      replace = TRUE, prob = NULL))
+          samp.boot <- sample.int(n.train, n.train, replace = TRUE)
         ## store the unique values for subsetting later
         samp.test <- unique(samp.boot)
         ## subset the env data for training set
@@ -373,4 +371,3 @@
     cat("\n")
     invisible(x)
   }
-

Modified: pkg/R/crossval.R
===================================================================
--- pkg/R/crossval.R	2012-01-11 14:29:38 UTC (rev 250)
+++ pkg/R/crossval.R	2012-02-09 12:41:40 UTC (rev 251)
@@ -49,7 +49,7 @@
             if(verbose)
                 setTxtProgressBar(pb, i)
             ## do a k-fold CV
-            pind <- ind[.Internal(sample(N, N, TRUE, NULL))]
+            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
@@ -78,7 +78,7 @@
         for(i in seq_len(nboot)) {
             if(verbose)
                 setTxtProgressBar(pb, i)
-            bSamp <- .Internal(sample(N, N, TRUE, NULL))
+            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

Modified: pkg/R/internal.R
===================================================================
--- pkg/R/internal.R	2012-01-11 14:29:38 UTC (rev 250)
+++ pkg/R/internal.R	2012-02-09 12:41:40 UTC (rev 251)
@@ -226,7 +226,6 @@
 ##    coef[1] + x * coef[2]
 ##}
 
-
 ## w.tol --- computes weighted standard deviations AKA tolerances
 w.tol <- function(x, env, opt, useN2 = TRUE) {
     ## x   = species abundances

Modified: pkg/R/predict.wa.R
===================================================================
--- pkg/R/predict.wa.R	2012-01-11 14:29:38 UTC (rev 250)
+++ pkg/R/predict.wa.R	2012-02-09 12:41:40 UTC (rev 251)
@@ -98,8 +98,7 @@
                     flush.console()
                 }
                 ## bootstrap sample
-                sel <- .Internal(sample(n.train, n.train,
-                                        TRUE, NULL))
+                sel <- sample.int(n.train, n.train, replace = TRUE)
                 nr <- NROW(X[sel, , drop = FALSE]) ## number of samples
                 nr.oob <- NROW(X[-sel, , drop = FALSE])
                 wa.optima <- w.avg(X[sel,,drop = FALSE], ENV[sel])