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("energy-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('energy')
Loading required package: boot
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "edist"
> 
> ### * edist
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: edist
> ### Title: E-distance
> ### Aliases: edist
> ### Keywords: multivariate cluster nonparametric
> 
> ### ** Examples
> 
>  ## compute e-distances for 3 samples of iris data
>  data(iris)
>  edist(iris[,1:4], c(50,50,50))
          1         2
2 123.55381          
3 195.30396  38.85415
> 
> ## Don't show: 
>      ## compute e-distances from a distance object
>      data(iris)
>      edist(dist(iris[,1:4]), c(50, 50, 50), distance=TRUE)
          1         2
2 123.55381          
3 195.30396  38.85415
>     
>      ## compute e-distances from a distance matrix
>      data(iris)
>      d <- as.matrix(dist(iris[,1:4]))
>      edist(d, c(50, 50, 50), distance=TRUE) 
          1         2
2 123.55381          
3 195.30396  38.85415
>      
> ## End Don't show
>  ## compute e-distances from vector of group labels
>  d <- dist(matrix(rnorm(100), nrow=50))
>  g <- cutree(energy.hclust(d), k=4)
>  edist(d, sizes=table(g), ix=rank(g, ties.method="first"))
         1        2        3
2 7.345510                  
3 7.484970 5.879414         
4 5.488066 5.848766 4.614402
>  
> 
> 
> cleanEx(); ..nameEx <- "energy.hclust"
> 
> ### * energy.hclust
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: energy.hclust
> ### Title: Hierarchical Clustering by Minimum (Energy) E-distance
> ### Aliases: energy.hclust
> ### Keywords: multivariate cluster
> 
> ### ** Examples
> 
>    ## Not run: 
> ##D    
> ##D    library(cluster)
> ##D    data(animals)
> ##D    plot(energy.hclust(dist(animals)))
> ##D    
> ## End(Not run)
>    
>    data(USArrests)
>    ecl <- energy.hclust(dist(USArrests))
>    print(ecl)    

Call:
energy.hclust(dst = dist(USArrests))

Cluster method   : e-distance 
Distance         : euclidean 
Number of objects: 50 

>    plot(ecl)
>    cutree(ecl, k=3)
       Alabama         Alaska        Arizona       Arkansas     California 
             1              1              1              2              1 
      Colorado    Connecticut       Delaware        Florida        Georgia 
             2              3              1              1              2 
        Hawaii          Idaho       Illinois        Indiana           Iowa 
             3              3              1              3              3 
        Kansas       Kentucky      Louisiana          Maine       Maryland 
             3              3              1              3              1 
 Massachusetts       Michigan      Minnesota    Mississippi       Missouri 
             2              1              3              1              2 
       Montana       Nebraska         Nevada  New Hampshire     New Jersey 
             3              3              1              3              2 
    New Mexico       New York North Carolina   North Dakota           Ohio 
             1              1              1              3              3 
      Oklahoma         Oregon   Pennsylvania   Rhode Island South Carolina 
             2              2              3              2              1 
  South Dakota      Tennessee          Texas           Utah        Vermont 
             3              2              2              3              3 
      Virginia     Washington  West Virginia      Wisconsin        Wyoming 
             2              2              3              3              2 
>    cutree(ecl, h=150)
       Alabama         Alaska        Arizona       Arkansas     California 
             1              2              3              4              3 
      Colorado    Connecticut       Delaware        Florida        Georgia 
             4              5              1              6              4 
        Hawaii          Idaho       Illinois        Indiana           Iowa 
             7              5              1              5              7 
        Kansas       Kentucky      Louisiana          Maine       Maryland 
             5              5              1              8              3 
 Massachusetts       Michigan      Minnesota    Mississippi       Missouri 
             9              1              7              2              4 
       Montana       Nebraska         Nevada  New Hampshire     New Jersey 
             5              5              1              7              9 
    New Mexico       New York North Carolina   North Dakota           Ohio 
             3              1              6              7              5 
      Oklahoma         Oregon   Pennsylvania   Rhode Island South Carolina 
             9              9              5              9              2 
  South Dakota      Tennessee          Texas           Utah        Vermont 
             8              4              4              5              7 
      Virginia     Washington  West Virginia      Wisconsin        Wyoming 
             9              9              8              7              9 
>    
>    ## compare performance of e-clustering, Ward's method, group average method
>    ## when sampled populations have equal means: n=200, d=5, two groups
>    z <- rbind(matrix(rnorm(1000), nrow=200), matrix(rnorm(1000, 0, 5), nrow=200))
>    g <- c(rep(1, 200), rep(2, 200))
>    d <- dist(z)
>    e <- energy.hclust(d)
>    a <- hclust(d, method="average")
>    w <- hclust(d^2, method="ward")
>    list("E" = table(cutree(e, k=2) == g), "Ward" = table(cutree(w, k=2) == g),
+         "Avg" = table(cutree(a, k=2) == g))
$E

FALSE  TRUE 
   12   388 

$Ward

FALSE  TRUE 
  143   257 

$Avg

FALSE  TRUE 
  199   201 

>  
> 
> 
> cleanEx(); ..nameEx <- "eqdist.etest"
> 
> ### * eqdist.etest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: eqdist.etest
> ### Title: Multisample E-statistic (Energy) Test of Equal Distributions
> ### Aliases: eqdist.etest
> ### Keywords: multivariate htest nonparametric
> 
> ### ** Examples
> 
>  data(iris)
>  
>  ## test if the 3 varieties of iris data (d=4) have equal distributions
>  eqdist.etest(iris[,1:4], c(50,50,50))

 Multivariate 3-sample E-test of equal distributions 
	Sample sizes:        50 50 50 
	Test statistic:     357.7 
	Approx. p-value:    < 2.22e-16 
	999 replicates, resampling method = permutation
> 
>  ## compare incomplete versions of two sample test
>  x <- c(rpois(400, 2), rnbinom(600, size=1, mu=2))
>  eqdist.etest(x, c(400, 600), incomplete=TRUE, N=100)

 Univariate 2-sample E-test of equal distributions, incomplete version with max size 100 
	Sample sizes:        400 600 
	Test statistic:     8.83 
	Approx. p-value:    0.003003 
	999 replicates, resampling method = permutation
>  eqdist.etest(x, c(400, 600), incomplete=TRUE, N=200)

 Univariate 2-sample E-test of equal distributions, incomplete version with max size 200 
	Sample sizes:        400 600 
	Test statistic:     14.66 
	Approx. p-value:    < 2.22e-16 
	999 replicates, resampling method = permutation
>   
> ## Don't show: 
>   x <- matrix(rnorm(500), nrow=100)
>   y <- matrix(rnorm(500, mean=5), nrow=100)
>   x <- rbind(x, y)
>   eqdist.etest(dist(x), sizes=c(100, 100), distance=TRUE)

 2-sample E-test of equal distributions 
	Sample sizes:        100 100 
	Test statistic:     861 
	Approx. p-value:    < 2.22e-16 
	999 replicates, resampling method = permutation
>   eqdist.etest(x, sizes=c(100, 100), incomplete=TRUE, N=50, R=100)

 Multivariate 2-sample E-test of equal distributions, incomplete version with max size 50 
	Sample sizes:        100 100 
	Test statistic:     431.5 
	Approx. p-value:    < 2.22e-16 
	100 replicates, resampling method = permutation
> ## End Don't show
> 
> 
> 
> cleanEx(); ..nameEx <- "ksample.e"
> 
> ### * ksample.e
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ksample.e
> ### Title: E-statistic (Energy Statistic) for Multivariate k-sample Test of
> ###   Equal Distributions
> ### Aliases: ksample.e
> ### Keywords: multivariate htest nonparametric
> 
> ### ** Examples
> 
> ## compute 3-sample E-statistic for 4-dimensional iris data
>  data(iris)
>  ksample.e(iris[,1:4], c(50,50,50))
[1] 357.7119
> 
> ## compute a 3-sample univariate E-statistic
>  ksample.e(rnorm(150), c(25,75,50))
[1] 5.677216
> 
> 
> 
> cleanEx(); ..nameEx <- "mvnorm.e"
> 
> ### * mvnorm.e
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mvnorm.e
> ### Title: E-statistic (Energy Statistic) for Testing Multivariate
> ###   Normality
> ### Aliases: mvnorm.e
> ### Keywords: multivariate htest
> 
> ### ** Examples
> 
>  
>  ## compute multivariate normality test statistic for iris Setosa data
>  data(iris)
>  mvnorm.e(iris[1:50, 1:4])
[1] 1.203397
>  
> 
> 
> cleanEx(); ..nameEx <- "mvnorm.etest"
> 
> ### * mvnorm.etest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mvnorm.etest
> ### Title: E-statistic (Energy) Test of Multivariate Normality
> ### Aliases: mvnorm.etest
> ### Keywords: multivariate htest
> 
> ### ** Examples
> 
>  ## test if the iris Setosa data has multivariate normal distribution
>  data(iris)
>  mvnorm.etest(iris[1:50,1:4])

 E-test of multivariate normality 
	Sample size:        50 
	Dimension:          4 
	Test statistic:     1.203 
	Approx. p-value:    0.041041 
	999 replicates
>  
>  ## test a univariate sample for normality
>  x <- runif(50, 0, 10)
>  mvnorm.etest(x)

 E-test of multivariate normality 
	Sample size:        50 
	Dimension:          1 
	Test statistic:     1.524 
	Approx. p-value:    < 2.22e-16 
	999 replicates
>  
> 
> 
> cleanEx(); ..nameEx <- "normal.e"
> 
> ### * normal.e
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: normal.e
> ### Title: E-statistic (Energy Statistic) for Testing Univariate Normality
> ### Aliases: normal.e
> ### Keywords: htest
> 
> ### ** Examples
> 
>  x <- rnorm(30)
>  normal.e(x)
[1] 0.5225757
> 
> 
> 
> cleanEx(); ..nameEx <- "poisson.m"
> 
> ### * poisson.m
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: poisson.m
> ### Title: Mean Distance Statistic for Testing Poisson Distribution
> ### Aliases: poisson.m
> ### Keywords: htest
> 
> ### ** Examples
> 
>  x <- rpois(20, 1)
> poisson.m(x)
[1] 0.02680415
>  
> 
> 
> cleanEx(); ..nameEx <- "poisson.mtest"
> 
> ### * poisson.mtest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: poisson.mtest
> ### Title: Mean Distance Test for Poisson Distribution
> ### Aliases: poisson.mtest
> ### Keywords: htest
> 
> ### ** Examples
> 
>  x <- rpois(20, 1)
>  poisson.mtest(x)

 Mean distance test of Poisson distribution 
	Sample size:        20 
	Sample mean:        1.2 
	Test statistic:     0.02680 
	Approx. p-value:    0.57157 
	999 replicates
>  
> 
> 
> cleanEx(); ..nameEx <- "print.etest.eqdist"
> 
> ### * print.etest.eqdist
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print.etest.eqdist
> ### Title: Print Multisample E-test (Energy Test) for Equal Distributions
> ### Aliases: print.etest.eqdist
> ### Keywords: print
> 
> ### ** Examples
> 
> ## print test if the 3 varieties of iris data (d=4) have equal distributions
>  data(iris)
>  e <- eqdist.etest(iris[,1:4], c(50,50,50))
>  print.etest.eqdist(e)

 Multivariate 3-sample E-test of equal distributions 
	Sample sizes:        50 50 50 
	Test statistic:     357.7 
	Approx. p-value:    < 2.22e-16 
	999 replicates, resampling method = permutation
>  
> 
> 
> cleanEx(); ..nameEx <- "print.etest.mvnorm"
> 
> ### * print.etest.mvnorm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print.etest.mvnorm
> ### Title: Print E-test (Energy Test) of Multivariate Normality
> ### Aliases: print.etest.mvnorm
> ### Keywords: print
> 
> ### ** Examples
> 
>  ## print E-test for 5-dimensional data
>  x <- matrix(rnorm(100), nrow=20, ncol=5)
>  e <- mvnorm.etest(x)
>  print.etest.mvnorm(e)

 E-test of multivariate normality 
	Sample size:        20 
	Dimension:          5 
	Test statistic:     1.138 
	Approx. p-value:    0.25726 
	999 replicates
>  
> 
> 
> cleanEx(); ..nameEx <- "print.etest.poisson"
> 
> ### * print.etest.poisson
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print.etest.poisson
> ### Title: Print Mean Distance Test for Poisson Distribution
> ### Aliases: print.etest.poisson
> ### Keywords: print
> 
> ### ** Examples
> 
>  x <- rpois(20, 1)
>  e <- poisson.mtest(x)
>  print.etest.poisson(e)

 Mean distance test of Poisson distribution 
	Sample size:        20 
	Sample mean:        1.2 
	Test statistic:     0.02680 
	Approx. p-value:    0.57157 
	999 replicates
>  
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  10.56 0.18 10.82 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')