X-Git-Url: https://git.donarmstrong.com/?a=blobdiff_plain;f=R%2Fdist.gene.R;h=516812a94e1d617088003f688c1670f458ec5553;hb=2653eb671caf9234635e44b895ef48b377a89a78;hp=ea17b9b577a37c3cc617d552d9e5bfa5d8eb3465;hpb=f5788af1ae347b00a14d94d12b50b3804d63e9bf;p=ape.git

diff --git a/R/dist.gene.R b/R/dist.gene.R
index ea17b9b..516812a 100644
--- a/R/dist.gene.R
+++ b/R/dist.gene.R
@@ -1,8 +1,8 @@
-## dist.gene.R (2008-07-18)
+## dist.gene.R (2012-04-02)
 
 ##   Pairwise Distances from Genetic Data
 
-## Copyright 2002-2008 Emmanuel Paradis
+## Copyright 2002-2012 Emmanuel Paradis
 
 ## This file is part of the R-package `ape'.
 ## See the file ../COPYING for licensing issues.
@@ -11,27 +11,29 @@ dist.gene <-
     function(x, method = "pairwise", pairwise.deletion = FALSE,
              variance = FALSE)
 {
-    if (!is.data.frame(x) || !is.matrix(x))
-        stop("'x' should be a matrix or a data.frame")
+    if (is.data.frame(x)) x <- as.matrix(x) else { # suggestion by Markus Schlegel
+        if (!is.matrix(x))
+            stop("'x' should be a matrix or a data.frame")
+    }
     method <- match.arg(method, c("pairwise", "percentage"))
 
     if (!pairwise.deletion) {
         ## delete the columns with at least one NA:
         del <- apply(x, 2, function(xx) any(is.na(xx)))
-        x <- x[, -del]
+        x <- x[, !del]
     }
     n <- dim(x)
     L <- n[2]
     n <- n[1]
-    D <- double(n*(n - 1)/2)
+    D <- double(n * (n - 1)/2)
     if (pairwise.deletion) L <- D
-    k <- 1
+    k <- 1L
     for (i in 1:(n - 1)) {
         for (j in (i + 1):n) {
             y <- x[i, ] != x[j, ]
             if (pairwise.deletion) L[k] <- sum(!is.na(y))
             D[k] <-  sum(y, na.rm = TRUE)
-            k <- k + 1
+            k <- k + 1L
         }
     }
     ## L is either a single integer value if pairwise.deletion = FALSE,
@@ -41,14 +43,14 @@ dist.gene <-
 
     attr(D, "Size") <- n
     attr(D, "Labels") <-  dimnames(x)[[1]]
-    attr(D, "Diag") <- attr(d, "Upper") <- FALSE
+    attr(D, "Diag") <- attr(D, "Upper") <- FALSE
     attr(D, "call") <- match.call()
     attr(D, "method") <- method
     class(D) <- "dist"
 
     if (variance) {
         y <- if (method == "pairwise") L else 1
-        attr(D, "variance") <- D*(y - D)/L
+        attr(D, "variance") <- D * (y - D)/L
     }
     D
 }