R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("amap-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('amap')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "acp"
> 
> ### * acp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: acp
> ### Title: Principal component analysis
> ### Aliases: acp pca print.acp
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(lubisch)
> lubisch <- lubisch[,-c(1,8)]
> p <- acp(lubisch)
> plot(p)
> 
> 
> 
> cleanEx(); ..nameEx <- "acpgen"
> 
> ### * acpgen
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: acpgen
> ### Title: Generalised principal component analysis
> ### Aliases: acpgen K W WsansC
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(lubisch)
> lubisch <- lubisch[,-c(1,8)]
> p <- acpgen(lubisch,h1=1,h2=1/sqrt(2))
> plot(p,main='ACP robuste des individus')
> 
> # See difference with acp
> 
> p <- acp(lubisch)
> plot(p)
> 
> # Other Data
> .Random.seed <- c(1,  416884367 ,1051235439)
> n <- 100   # Number of observations
> m <- 0     # Mean of 1 group
> temp <- cbind(rnorm(n,m,1),rnorm(n,0,10))
> m <- 10
> mat <- rbind(temp,cbind(rnorm(n,m,1),rnorm(n,0,10)))
> mat <- scale(mat,TRUE,TRUE)
> rm (m,n,temp) 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "dist"
> 
> ### * dist
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Dist
> ### Title: Distance Matrix Computation
> ### Aliases: Dist
> ### Keywords: multivariate cluster
> 
> ### ** Examples
> 
> x <- matrix(rnorm(100), nrow=5)
> Dist(x)
         1        2        3        4
2 5.701817                           
3 6.013119 5.032069                  
4 7.276905 5.325473 5.811861         
5 6.619295 5.306750 4.945987 6.612081
> Dist(x, diag = TRUE)
         1        2        3        4        5
1 0.000000                                    
2 5.701817 0.000000                           
3 6.013119 5.032069 0.000000                  
4 7.276905 5.325473 5.811861 0.000000         
5 6.619295 5.306750 4.945987 6.612081 0.000000
> Dist(x, upper = TRUE)
         1        2        3        4        5
1          5.701817 6.013119 7.276905 6.619295
2 5.701817          5.032069 5.325473 5.306750
3 6.013119 5.032069          5.811861 4.945987
4 7.276905 5.325473 5.811861          6.612081
5 6.619295 5.306750 4.945987 6.612081         
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "distpar"
> 
> ### * distpar
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: distpar
> ### Title: Parallelized Distance Matrix Computation
> ### Aliases: distpar
> ### Keywords: multivariate cluster
> 
> ### ** Examples
> 
> x <- matrix(rnorm(100), nrow=5)
> 
> ## compute dist with 8 threads
> distpar(x,nbproc=8)
         1        2        3        4
2 5.701817                           
3 6.013119 5.032069                  
4 7.276905 5.325473 5.811861         
5 6.619295 5.306750 4.945987 6.612081
> 
> ## compute pearson dist with 8 threads
> distpar(x,nbproc=8,method="pearson")
          1         2         3         4
2 1.1629925                              
3 1.1584844 1.0825533                    
4 1.3551134 0.9010061 0.9812551          
5 1.1997694 0.9815441 0.7656573 1.1024095
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "hcluster"
> 
> ### * hcluster
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: hcluster
> ### Title: Hierarchical Clustering
> ### Aliases: hcluster
> ### Keywords: multivariate cluster
> 
> ### ** Examples
> 
> 
> data(USArrests)
> hc <- hcluster(USArrests,link = "ave")
> plot(hc)
> plot(hc, hang = -1)
> 
> ## Do the same with centroid clustering and squared Euclidean distance,
> ## cut the tree into ten clusters and reconstruct the upper part of the
> ## tree from the cluster centers.
> hc <- hclust(dist(USArrests)^2, "cen")
> memb <- cutree(hc, k = 10)
> cent <- NULL
> for(k in 1:10){
+   cent <- rbind(cent, colMeans(USArrests[memb == k, , drop = FALSE]))
+ }
> hc1 <- hclust(dist(cent)^2, method = "cen", members = table(memb))
> opar <- par(mfrow = c(1, 2))
> plot(hc,  labels = FALSE, hang = -1, main = "Original Tree")
> plot(hc1, labels = FALSE, hang = -1, main = "Re-start from 10 clusters")
> par(opar)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "hclusterpar"
> 
> ### * hclusterpar
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: hclusterpar
> ### Title: Parallelized Hierarchical Clustering
> ### Aliases: hclusterpar
> ### Keywords: multivariate cluster
> 
> ### ** Examples
> 
> 
> data(USArrests)
> hc <- hclusterpar(USArrests,link = "ave",nbproc=8)
> plot(hc)
> plot(hc, hang = -1)
> 
> ## To check...
> hc <- hcluster(USArrests,link = "ave")
> plot(hc)
> ## The same ?
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.acp"
> 
> ### * plot.acp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.acp
> ### Title: Graphics for Principal component Analysis
> ### Aliases: plot.acp biplot.acp plot2.acp
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(lubisch)
> lubisch <- lubisch[,-c(1,8)]
> p <- acp(lubisch)
> plot(p)
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  0.3 0.03 0.32 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')