version 1.8.5

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: adehabitat
-Version: 1.8.4
+Version: 1.8.5
 Date: 2009/05/04
 Title: Analysis of habitat selection by animals
 Author: Clement Calenge, contributions from Mathieu Basille, Stephane
@@ -11,6 +11,6 @@ Description: A collection of tools for the analysis of habitat
         selection by animals
 License: GPL (>= 2)
 URL: https://www.faunalia.it/animove/trac/
-Packaged: 2011-03-18 13:46:16 UTC; calenge
+Packaged: 2011-04-17 10:56:14 UTC; calenge
 Repository: CRAN
-Date/Publication: 2011-03-19 06:18:30
+Date/Publication: 2011-04-24 06:05:37

R/explore.kasc.r

@@ -169,7 +169,7 @@ explore.kasc <- function (ka, coltxt="blue",
             return("OK - Finished")
         }
         if (key == "z") {
-            tmppx <- (get("cusr", envi=e1)[1:2] - get("cusr", envir=e1)[1])
+            tmppx <- (get("cusr", envir=e1)[1:2] - get("cusr", envir=e1)[1])
             assign("xlim", c(get("a8", envir=e1)[1] - tmppx[2]/4, (get("a8", envir=e1)[1] + tmppx[2]/4)), envir=e1)
             assign("tmppy", (get("cusr", envir=e1)[3:4] - get("cusr", envir=e1)[3]), envir=e1)
             assign("ylim", c(get("a8", envir=e1)[2] - get("tmppy", envir=e1)[2]/4, (get("a8", envir=e1)[2] + get("tmppy", envir=e1)[2]/4)), envir=e1)

R/gnesfa.r

@@ -36,7 +36,7 @@ gnesfa <- function(dudi, Focus, Reference, centering = c("single", "twice"),
     ## First PCA
     rank <- dudi$rank
     pc1 <- dudi.pca(Z, center=FALSE, scale=FALSE,
-                    row.w=Reference, nf=rank, scann=FALSE)
+                    row.w=Reference, nf=rank, scannf=FALSE)
     c1 <- as.matrix(pc1$c1)
     L <- sweep(Z%*%c1,2,sqrt(pc1$eig),"/")
 

R/kplot.kselect.r

@@ -51,7 +51,7 @@
                     csub=1.5, cgrid=1.5, csize=csi[i])
         }
         s.distri(Xtmp, wgtmp, xax, yax,
-                 sub=names(li.Xi)[i], add.p=addval,
+                 sub=names(li.Xi)[i], add.plot=addval,
                  cpoint=cpoint, xlim=rx, ylim=ry,
                  ...)
     }

R/madifa.r

@@ -457,11 +457,11 @@ s.madifa <- function(x, xax=1, yax=2, cgrid = 1, clab=1, ...)
     po <- rbind(as.matrix(pol),yy)
 
     ## Draws the plot
-    s.arrow(co, xax=xax, yax=yax, xlim=c(-1,1), ylim=c(-1,1), clab=clab)
+    s.arrow(co, xax=xax, yax=yax, xlim=c(-1,1), ylim=c(-1,1), clabel=clab)
     polygon(yy)
     polygon(po, col="white")
-    s.arrow(co, xax=xax, yax=yax, xlim=c(-1,1), ylim=c(-1,1), add.p=TRUE,
-            clab=clab, ...)
+    s.arrow(co, xax=xax, yax=yax, xlim=c(-1,1), ylim=c(-1,1), add.plot=TRUE,
+            clabel=clab, ...)
 
     ## scale box
     xaxp <- par("xaxp")
@@ -498,7 +498,7 @@ plot.madifa <- function(x, index, attr, xax=1, yax=2, cont=FALSE,...)
     scatterutil.eigen(x$eig, wsel = c(xax, yax))
 
     ## Column scores
-    s.madifa(x, xax, yax, cgrid=2, clab=1.25)
+    s.madifa(x, xax, yax, cgrid=2, clabel=1.25)
 
 
     ## function to draw the niche
@@ -565,7 +565,7 @@ plot.madifa <- function(x, index, attr, xax=1, yax=2, cont=FALSE,...)
     ## Correlation with the environmental variables
     s.arrow(x$cor, xax=xax,yax=yax,
             sub="Cor(habitat var., scores) available",
-            clab=1.25, csub=2, cgrid=2, xlim=c(-1,1), ylim=c(-1,1))
+            clabel=1.25, csub=2, cgrid=2, xlim=c(-1,1), ylim=c(-1,1))
     u <- par(mar=c(0.1,0.1,2,0.1))
 
     ## Again the maps

R/plot.kselect.r

@@ -20,11 +20,11 @@
     par(mar = c(0.1, 0.1, 0.1, 0.1))
 
     ## 1. correlations between the PCA axes and the K-select axes
-    s.corcircle(x$as, xax, yax, sub = "Axis", csub = 2, clab = 1.25)
+    s.corcircle(x$as, xax, yax, sub = "Axis", csub = 2, clabel = 1.25)
 
     ## 2. scores of the environmental variables on the
     ##    K-select axes (eigenvectors)
-    s.arrow(x$l1, xax, yax, sub = "Variables", csub = 2, clab = 1.25)
+    s.arrow(x$l1, xax, yax, sub = "Variables", csub = 2, clabel = 1.25)
 
     ## 3. the eigenvalues of the analysis
     scatterutil.eigen(x$eig, wsel = c(xax, yax))
@@ -46,23 +46,23 @@
     ## 4.4. The Marginality vectors are displayed as arrows connecting the
     ##      "available" centroids to the "used" centroids
 
-    s.label(rbind(mav, mut), xax, yax, clab = 0, cpo = 0,
+    s.label(rbind(mav, mut), xax, yax, clabel = 0, cpoint = 0,
             sub = "Marginality vectors", csub = 2) ## background
     for (i in 1:nrow(mav))
         arrows(mav[i,xax], mav[i,yax], mut[i,xax], mut[i,yax],
                lwd=2, angle=20) ## arrows
-    s.label(mav, xax, yax, add.plot=TRUE, clab=1.5) ## labels
+    s.label(mav, xax, yax, add.plot=TRUE, clabel=1.5) ## labels
 
 
     ## 5. coordinates of the uncentered available points on the
     ##    axes of the K-select
     s.chull(as.data.frame(ls), x$initfac,
-            clab=1.5, sub="Available Resource units", csub=2,
+            clabel=1.5, sub="Available Resource units", csub=2,
             optchull=1, cpoint=1)
 
     ## 6. coordinates of the recentred marginality vectors on the axes
     ##    on the K-select
-    s.arrow(x$co, xax, yax, clab = 1.25, cpo = 0.5, sub = "Animals",
+    s.arrow(x$co, xax, yax, clabel = 1.25, cpoint = 0.5, sub = "Animals",
             csub = 2)
 
 }

R/simm.mba.r

@@ -8,7 +8,7 @@
       stop("matrix 2*2 expected")
     if (sum((t(sigma)-sigma)^2)> 1e-7)
       stop("symetric matrix expected")
-    if (any(eigen(sigma, sym=TRUE)$value< -1e-7))
+    if (any(eigen(sigma, symmetric=TRUE)$value< -1e-7))
       stop("positive matrix expected")
 
     n <- length(date)

R/simm.mou.r

@@ -10,7 +10,7 @@ function(date=1:100, b = c(0,0), a = diag(0.5,2),
       stop("matrix 2*2 expected")
     if (sum((t(sigma)-sigma)^2)> 1e-7)
       stop("symetric matrix expected")
-    if (any(eigen(sigma, sym=TRUE)$value< -1e-7))
+    if (any(eigen(sigma, symmetric=TRUE)$value< -1e-7))
       stop("positive matrix expected")
 
     n <- length(date)