Remove dontrun in examples and restore graĥical parameters modified b…

…y par

This is required fo new submission on CRAN. dontrun examples are either removed, transformed into donttest or tested for interactivity

R/LCBD.comp.R

@@ -98,7 +98,8 @@
 #' col = c("white", "brown"), main = "Doubs fish abundance diff. LCBD")
 #' }
 #' 
-#' \dontrun{
+#' \donttest{
+#' if(require("ade4", quietly = TRUE) & require("betapart", quietly = TRUE)){
 #' ### Example 3
 #' ### This example requires packages \code{"betapart"} and \code{"ade4"} for data. 
 #' ### For the Baselga-family indices, the same partitioning results are obtained using
@@ -131,6 +132,7 @@
 #' out3.AbDiff$beta
 #' # Compare BDtotal here to RichDiff/Nes in out.comp$part (above)
 #' }
+#' }
 #'
 #' @importFrom stats as.dist
 #' @export LCBD.comp

R/Tiahura.R

@@ -108,7 +108,7 @@
 #'                               chron=TRUE)
 #' 
 #' ## Plotting the results
-#' par(mfrow=c(3,1))
+#' oldpar <- par(mfrow=c(3,1))
 #' 
 #' ## First graph: constrained clusters
 #' par(mar=c(3,6.5,2,2))
@@ -176,6 +176,6 @@
 #' ## plot_slice(3L,5L)
 #' ## plot_slice(4L,5L)
 #' ## plot_slice(5L,5L)
-#' 
+#' par(oldpar)
 NULL
 ##

R/WRperiodogram.R

@@ -140,7 +140,7 @@
 #' # Periodogram of the lunar component only 
 #' res.moon.250 <- WRperiodogram(moon, nperm=0)  # T1=2, T2=n/2=250; no test
 #' res.moon.130 <- WRperiodogram(moon, T2=130, nperm=499)
-#' par(mfrow=c(1,2))
+#' oldpar <- par(mfrow=c(1,2))
 #' # Plot 2 moon cycles, n = 118*2 = 236 points
 #' plot(moon[1:236], xlab="One time unit = 6 hours") 
 #' plot(res.moon.130, prog=1) # Plot the periodogram
@@ -207,6 +207,7 @@
 #' # The yearly cycle and harmonics are significant
 #' # Compare the results of afc() to those of WRperiodogram above
 #' acf(ldeaths)   # lag=1.0 is one year; see ?acf
+#' par(oldpar)
 #' 
 #' @useDynLib adespatial, .registration = TRUE 
 #' @importFrom graphics lines points

R/aem.R

@@ -71,7 +71,7 @@
 #' # Illustrate 4 AEM eigenfunctions using bubble plots
 #' opal <- palette()
 #' palette(c("black","white"))
-#' par(mfrow=c(2,2))
+#' oldpar <- par(mfrow=c(2,2))
 #' symbols(x=xy[,2:3], circles=abs(res$vectors[,1]), inches=FALSE, asp=1,
 #'  fg=ifelse(sign(-res$vectors[,1])+1>0,1,0), 
 #'  bg=ifelse(sign(res$vectors[,1])+1>0,1,0), xlab="x", ylab="y")
@@ -113,6 +113,7 @@
 #' title("AEM 4")
 #' 
 #' palette(opal)
+#' par(oldpar)
 #' 
 #' # Construct AEM eigenfunctions with a function of the distance
 #' # as weights to put on the links

R/aem.build.binary.R

@@ -168,7 +168,7 @@
             xy.range.min<-xy.range[1,2]-((xy.range[2,2]-xy.range[1,2])/5)
             xy.range.max<-xy.range[2,2]
 
-            par(mar=c(1,1,1,1))
+            oldpar <- par(mar=c(1,1,1,1))
             plot(coords[, 2:3], pch = 20, asp = 1, cex = 0.5, axes = FALSE, 
                 xlab = "", ylab = "", ylim=c(xy.range.min,xy.range.max))
             segments(x0 = coords[link[, 1], 2], y0 = coords[link[, 
@@ -181,6 +181,7 @@
             points(site0[1],site0[2],pch=19,col="blue")
             segments(x0=site0[1],y0=site0[2],x1=coords[low.y,2],y1=coords[low.y,3],col="blue")
             par(mar=c(5,4,4,2))
+            par(oldpar)
         }
         n.low.y <- length(low.y)
         link <- rbind(cbind(rep(0, n.low.y), low.y), as.matrix(link))

R/aem.weight.edges.R

@@ -195,7 +195,7 @@ aem.weight.edges <-
             xy.range.min<-xy.range[1,2]-((xy.range[2,2]-xy.range[1,2])/5)
             xy.range.max<-xy.range[2,2]
 
-            par(mar=c(1,1,1,1))
+            oldpar <- par(mar=c(1,1,1,1))
             plot(coords[, 2:3], pch = 20, asp = 1, cex = 0.5, axes = FALSE, xlab = "", ylab = "", ylim=c(xy.range.min,xy.range.max))
             segments(x0 = coords[links[, 1], 2], y0 = coords[links[, 1], 3], x1 = coords[links[, 2], 2], y1 = coords[links[, 2], 3], col = "red")
             text(coords[,2:3],labels=as.character(coords[,1]),pos=2,col="red")
@@ -205,6 +205,7 @@ aem.weight.edges <-
             points(site0[1],site0[2],pch=19,col="blue")
             segments(x0=site0[1],y0=site0[2],x1=coords[low.y,2],y1=coords[low.y,3],col="blue")
             par(mar=c(5,4,4,2))
+            par(oldpar)
         }
 
         ### Reorganize the edges 

R/chooseCN.R

@@ -59,12 +59,12 @@
 #' if(require("ade4", quietly = TRUE)){
 #' data(mafragh)
 #'
-#' par(mfrow=c(2,2))
+#' oldpar <- par(mfrow=c(2,2))
 #' cn1 <- chooseCN(mafragh$xy,ask=FALSE,type=1)
 #' cn2 <- chooseCN(mafragh$xy,ask=FALSE,type=2)
 #' cn3 <- chooseCN(mafragh$xy,ask=FALSE,type=3)
 #' cn4 <- chooseCN(mafragh$xy,ask=FALSE,type=4)
-#' 
+#' par(oldpar)
 #' }
 #'
 #' @export chooseCN

R/constr.hclust.R

@@ -277,7 +277,7 @@
 #' ## A fonction to plot the Whiskey clustering results:
 #' 
 #' plotWhiskey <- function(wh, k) {
-#'    par(fig=c(0,1,0,1))
+#'    oldpar <- par(fig=c(0,1,0,1))
 #'    plot(grpWD2cst_ScotchWhiskey[[wh]], k=k, links=TRUE, las=1,
 #'         xlab="Eastings (km)", ylab="Northings (km)", cex=0.1, lwd=3,
 #'         main=sprintf("Feature: %s",wh))
@@ -294,6 +294,7 @@
 #'    text(ScotchWhiskey$geo@coords/1000,labels=1:length(ScotchWhiskey$geo))
 #'    rect(xleft=SpeyZoom$xlim[1L], ybottom=SpeyZoom$ylim[1L],
 #'         xright=SpeyZoom$xlim[2L], ytop=SpeyZoom$ylim[2L], lwd=2, lty=1L)
+#'    par(oldpar)
 #' }
 #' 
 #' ## Plot the clustering results on the map of Scotland for 5 groups.
@@ -306,14 +307,13 @@
 #' 
 #' ## End of the Scotch Whiskey tasting data example
 #' 
-#' \dontrun{
+#' \donttest{
 #' 
 #' ## Third example: Fish community composition along the Doubs River,
 #' ## France. The sequence is analyzed as a case of chronological
 #' ## clustering, substituting space for time.
 #' 
-#' library(ade4)
-#' library(adespatial)
+#' if(require("ade4", quietly = TRUE)){
 #' data(doubs, package="ade4")
 #' Doubs.D <- dist.ldc(doubs$fish, method="hellinger")
 #' grpWD2cst_fish <- constr.hclust(Doubs.D, method="ward.D2", chron=TRUE,
@@ -349,12 +349,13 @@
 #'         coords=ex.xy      # File of geographic coordinates
 #'     )
 #' 
-#' par(mfrow=c(1,2))
+#' oldpar <- par(mfrow=c(1,2))
 #' ## Plot the map of the results for k = 3
 #' plot(test.out, k=3)
 #' ## Plot the dendrogram
 #' stats:::plot.hclust(test.out, hang=-1)
-#' 
+#' par(oldpar)
+#' }
 #'
 #' ## Same example modified: disjoint clusters
 #' ## Same ex.Y and ex.xy as in the previous example
@@ -381,12 +382,13 @@
 #'     )
 #' cutree(test.out2, k=2)
 #' 
-#' par(mfrow=c(1,2))
+#' oldpar <- par(mfrow=c(1,2))
 #' ## Plot the map of the results for k = 3
 #' plot(test.out2, k=3)
 #' ## Plot the dendrogram showing the disconnected groups
 #' stats:::plot.hclust(test.out2, hang=-1)
 #' axis(2,at=0:ceiling(max(test.out2$height,na.rm=TRUE)))
+#' par(oldpar)
 #' 
 #' ## End of the disjoint clusters example
 #' 
@@ -396,8 +398,7 @@
 #' ## Computing time grows with N at roughly the same speed as the memory
 #' ## storage requirements to store the dissimilarity matrices.
 #' 
-#' require(magrittr)
-#' require(pryr)
+#' if(require("magrittr", quietly = TRUE) & require("pryr", quietly = TRUE)){
 #' 
 #' benchmark <- function(nobj) {
 #'     # Argument -
@@ -436,7 +437,7 @@
 #' 
 #' ## Plotting the results:
 #' ok <- res %>% apply(1L, function(x) !x %>% is.na %>% any)
-#' par(mar=c(3,6,2,2),mfrow=c(2L,1L))
+#' oldpar <- par(mar=c(3,6,2,2),mfrow=c(2L,1L))
 #' barplot(height = res[ok,"Time (sec)"], names.arg= res[ok,"N.objects"],
 #'         ylab="Time (seconds)\n",xlab="",las=1L,log="y")
 #' par(mar=c(5,6,0,2))
@@ -446,9 +447,10 @@
 #' 
 #' ## Examine the output file
 #' res
-#' 
+#' par(oldpar)
 #' ## End of the benchmarking example
 #' }
+#' }
 #' ## End of examples
 #' 
 #' @useDynLib adespatial, .registration = TRUE

R/envspace.test.R

@@ -148,7 +148,7 @@
 #' @keywords spatial
 #' 
 #' @examples
-#' \dontrun{
+#' \donttest{
 #' if(require(vegan)) { 
 #' # Illustration of the test of the SSEF on the oribatid mite data
 #' # (Borcard et al. 1992, 1994 for details on the dataset):

R/listw.explore.R

@@ -6,7 +6,7 @@
 #' @seealso \code{\link{chooseCN}}
 #' @returns No return value
 #' @examples
-#' \dontrun{
+#' if(interactive()){
 #' ## a matrix or an object of class 'Spatial*' should be in the global environment
 #' xy <- matrix(rnorm(50), 25)
 #' listw.explore()

R/listw.select.R

@@ -125,7 +125,7 @@
 #'@keywords spatial
 #'
 #' @examples
-#' \dontrun{
+#' \donttest{
 #' if(require(spdep)) {
 #' ### Create a grid of 15 x 15:
 #' grid <- expand.grid(x = seq(1, 15, 1), y = seq(1, 15, 1))

R/multispati.R

@@ -108,7 +108,7 @@
 #'       labels <- lapply(1:length(obj$eig),function(i) bquote(lambda[.(i)]))
 #'       # draw the plot
 #'       xmax <- eval(as.list(obj$call)$dudi)$eig[1]*1.1
-#'       par(las=1)
+#'       oldpar <- par(las=1)
 #'       var <- sum.obj[,2]
 #'       moran <- sum.obj[,3]
 #'       plot(x=var,y=moran,type='n',xlab='Inertia',ylab="Spatial autocorrelation (I)",
@@ -123,6 +123,7 @@
 #'       segments(0,I0,xmax,I0,lty=2)
 #'       abline(v=0)
 #'       title("Spatial and inertia components of the eigenvalues")
+#'       par(oldpar)
 #'     }
 #'     fgraph(maf.coa.ms)
 #'     ## end eigenvalues details
@@ -134,11 +135,11 @@
 #'       g4 <- s.corcircle(maf.coa.ms$as, plot = FALSE)
 #'       G1 <- ADEgS(list(g2, g3, g4), layout = c(1, 3))
 #'     } else {
-#'       par(mfrow = c(1, 3))
+#'       oldpar <- par(mfrow = c(1, 3))
 #'       barplot(maf.coa$eig)
 #'       barplot(maf.coa.ms$eig) 
 #'       s.corcircle(maf.coa.ms$as)
-#'       par(mfrow = c(1, 1))
+#'       par(oldpar)
 #'     }
 #'  
 #'  
@@ -149,12 +150,12 @@
 #'       g8 <- s.value(maf.xy, maf.coa.ms$li[, 2], plot = FALSE)
 #'       G2 <- ADEgS(list(g5, g6, g7, g8), layout = c(2, 2))
 #'     } else {
-#'       par(mfrow = c(2, 2))
+#'       oldpar <- par(mfrow = c(2, 2))
 #'       s.value(maf.xy, -maf.coa$li[, 1])
 #'       s.value(maf.xy, -maf.coa$li[, 2])
 #'       s.value(maf.xy, maf.coa.ms$li[, 1])
 #'       s.value(maf.xy, maf.coa.ms$li[, 2])
-#'       par(mfrow = c(1, 1))
+#'       par(oldpar)
 #'     }
 #' 
 #' 
@@ -167,10 +168,10 @@
 #'       g10 <- s.match(w1.ms, w1.msm, plab.cex = 0.75, plot = FALSE)
 #'       G3 <- cbindADEg(g9, g10, plot = TRUE)
 #'     } else {
-#'       par(mfrow = c(1,2))
+#'       oldpar <- par(mfrow = c(1,2))
 #'       s.match(w1, w1m, clab = 0.75)
 #'       s.match(w1.ms, w1.msm, clab = 0.75)
-#'       par(mfrow = c(1, 1))
+#'       par(oldpar)
 #'     }
 #' 
 #'     maf.pca <- dudi.pca(mafragh$env, scannf = FALSE)

R/plot.TBI.R

@@ -109,7 +109,7 @@
 #'
 #' ## Draw BC plots
 #' 
-#' par(mfrow=c(1,2))
+#' oldpar <- par(mfrow=c(1,2))
 #' 
 #' s.names <- paste("Surv",1:12,sep=".")
 #' 
@@ -125,7 +125,7 @@
 #' 
 #' ## In the 3rd plot, draw all symbols small and of the same size, using cex.symb=NULL
 #' 
-#' dev.off()
+#' par(oldpar)
 #' 
 #' plot(res1, s.names=1:12, col.bg="gold", pch.loss=23, pch.gain=24, 
 #' main="B-C plot, Pyrifos, surveys 4 & 11", cex.symb=NULL)
@@ -145,7 +145,7 @@
 #' 
 #' ## Draw BC plots
 #' 
-#' par(mfrow=c(1,2))
+#' oldpar <- par(mfrow=c(1,2))
 #' 
 #' s.names=rownames(res2$BCD.mat)
 #' 
@@ -158,7 +158,7 @@
 #' 
 #' plot(res2, s.names=1:20, col.rim="coral2", pch.loss=19, pch.gain=15, 
 #' main="B-C plot, Mite data", xlim=c(0,0.6), ylim=c(0,0.6))
-#' 
+#' par(oldpar)
 #' }
 #' 
 #' @importFrom graphics abline

R/stimodels.R

@@ -194,7 +194,7 @@
 #' # of the times shows a significant spatial structure can be done with model 6a. For a  
 #' # global test of temporal structure at the various sites, use model 6b.
 #' 
-#' \dontrun{     # Code not run during CRAN software tests
+#' \donttest{     # Code not run during CRAN software tests
 #'
 #' # Example 2. Run space-time analysis with global tests for main effects after testing
 #' # the interaction, which is significant in this example