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("subselect-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('subselect')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "anneal"
> 
> ### * anneal
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: anneal
> ### Title: Simulated Annealing Search for an optimal k-variable subset
> ### Aliases: anneal
> ### Keywords: manip
> 
> ### ** Examples
> 
> 
> # For illustration of use, a small data set with very few iterations
> # of the algorithm. 
> 
> data(swiss)
> anneal(cor(swiss),2,3,nsol=4,niter=10,criterion="RM")
$subsets
, , Card.2

           Var.1 Var.2 Var.3
Solution 1     3     6     0
Solution 2     4     5     0
Solution 3     4     5     0
Solution 4     3     6     0

, , Card.3

           Var.1 Var.2 Var.3
Solution 1     3     5     6
Solution 2     4     5     6
Solution 3     4     5     6
Solution 4     1     2     5


$values
              card.2    card.3
Solution 1 0.8016409 0.8769672
Solution 2 0.7982296 0.9043760
Solution 3 0.7982296 0.9043760
Solution 4 0.8016409 0.8791856

$bestvalues
   Card.2    Card.3 
0.8016409 0.9043760 

$bestsets
       Var.1 Var.2 Var.3
Card.2     3     6     0
Card.3     4     5     6

$call
anneal(mat = cor(swiss), kmin = 2, kmax = 3, nsol = 4, niter = 10, 
    criterion = "RM")

> 
> ##$subsets
> ##, , Card.2
> ##
> ##           Var.1 Var.2 Var.3
> ##Solution 1     3     6     0
> ##Solution 2     4     5     0
> ##Solution 3     1     2     0
> ##Solution 4     3     6     0
> ##
> ##, , Card.3
> ##
> ##           Var.1 Var.2 Var.3
> ##Solution 1     4     5     6
> ##Solution 2     3     5     6
> ##Solution 3     3     4     6
> ##Solution 4     4     5     6
> ##
> ##
> ##$values
> ##              card.2    card.3
> ##Solution 1 0.8016409 0.9043760
> ##Solution 2 0.7982296 0.8769672
> ##Solution 3 0.7945390 0.8777509
> ##Solution 4 0.8016409 0.9043760
> ##
> ##$bestvalues
> ##   Card.2    Card.3 
> ##0.8016409 0.9043760 
> ##
> ##$bestsets
> ##       Var.1 Var.2 Var.3
> ##Card.2     3     6     0
> ##Card.3     4     5     6
> ##
> ##$call
> ##anneal(cor(swiss), 2, 3, nsol = 4, niter = 10, criterion = "RM")
> 
> #
> #
> # Excluding variable number 6 from the subsets.
> # 
> 
> data(swiss)
> anneal(cor(swiss),2,3,nsol=4,niter=10,criterion="RM",exclude=c(6))
$subsets
, , Card.2

           Var.1 Var.2 Var.3
Solution 1     1     2     0
Solution 2     4     5     0
Solution 3     4     5     0
Solution 4     4     5     0

, , Card.3

           Var.1 Var.2 Var.3
Solution 1     1     2     4
Solution 2     1     4     5
Solution 3     1     2     5
Solution 4     1     2     5


$values
              card.2    card.3
Solution 1 0.7945390 0.8537422
Solution 2 0.7982296 0.8686515
Solution 3 0.7982296 0.8791856
Solution 4 0.7982296 0.8791856

$bestvalues
   Card.2    Card.3 
0.7982296 0.8791856 

$bestsets
       Var.1 Var.2 Var.3
Card.2     4     5     0
Card.3     1     2     5

$call
anneal(mat = cor(swiss), kmin = 2, kmax = 3, nsol = 4, niter = 10, 
    exclude = c(6), criterion = "RM")

> 
> ##$subsets
> ##, , Card.2
> ##
> ##           Var.1 Var.2 Var.3
> ##Solution 1     4     5     0
> ##Solution 2     4     5     0
> ##Solution 3     4     5     0
> ##Solution 4     4     5     0
> ##
> ##, , Card.3
> ##
> ##           Var.1 Var.2 Var.3
> ##Solution 1     1     2     5
> ##Solution 2     1     2     5
> ##Solution 3     1     2     5
> ##Solution 4     1     4     5
> ##
> ##
> ##$values
> ##              card.2    card.3
> ##Solution 1 0.7982296 0.8791856
> ##Solution 2 0.7982296 0.8791856
> ##Solution 3 0.7982296 0.8791856
> ##Solution 4 0.7982296 0.8686515
> ##
> ##$bestvalues
> ##   Card.2    Card.3 
> ##0.7982296 0.8791856 
> ##
> ##$bestsets
> ##       Var.1 Var.2 Var.3
> ##Card.2     4     5     0
> ##Card.3     1     2     5
> ##
> ##$call
> ##anneal(cor(swiss), 2, 3, nsol = 4, niter = 10, criterion = "RM",
> ##     exclude=c(6))
> 
> # specifying initial solutions: using the subsets produced by
> # simulated annealing for one criterion (RM, by default) as initial
> # solutions for the simulated annealing search with a different criterion.
> 
> data(swiss)
> rmresults<-anneal(cor(swiss),2,3,nsol=4,niter=10, setseed=TRUE)
> anneal(cor(swiss),2,3,nsol=4,niter=10,criterion="gcd", initialsol=rmresults$subsets)
$subsets
, , Card.2

           Var.1 Var.2 Var.3
Solution 1     4     6     0
Solution 2     3     6     0
Solution 3     3     6     0
Solution 4     3     6     0

, , Card.3

           Var.1 Var.2 Var.3
Solution 1     4     5     6
Solution 2     4     5     6
Solution 3     1     5     6
Solution 4     4     5     6


$values
              card.2    card.3
Solution 1 0.7792651 0.9253720
Solution 2 0.8487026 0.9253720
Solution 3 0.8487026 0.8048528
Solution 4 0.8487026 0.9253720

$bestvalues
   Card.2    Card.3 
0.8487026 0.9253720 

$bestsets
       Var.1 Var.2 Var.3
Card.2     3     6     0
Card.3     4     5     6

$call
anneal(mat = cor(swiss), kmin = 2, kmax = 3, nsol = 4, niter = 10, 
    criterion = "gcd", initialsol = rmresults$subsets)

> 
> ##$subsets
> ##, , Card.2
> ##
> ##           Var.1 Var.2 Var.3
> ##Solution 1     3     6     0
> ##Solution 2     3     6     0
> ##Solution 3     3     6     0
> ##Solution 4     3     6     0
> ##
> ##, , Card.3
> ##
> ##           Var.1 Var.2 Var.3
> ##Solution 1     4     5     6
> ##Solution 2     4     5     6
> ##Solution 3     3     4     6
> ##Solution 4     4     5     6
> ##
> ##
> ##$values
> ##              card.2   card.3
> ##Solution 1 0.8487026 0.925372
> ##Solution 2 0.8487026 0.925372
> ##Solution 3 0.8487026 0.798864
> ##Solution 4 0.8487026 0.925372
> ##
> ##$bestvalues
> ##   Card.2    Card.3 
> ##0.8487026 0.9253720 
> ##
> ##$bestsets
> ##       Var.1 Var.2 Var.3
> ##Card.2     3     6     0
> ##Card.3     4     5     6
> ##
> ##$call
> ##anneal(cor(swiss), 2, 3, nsol = 4, niter = 10, criterion = "gcd", 
> ##    initialsol = rmresults$subsets)
> 
> 
> 
> cleanEx(); ..nameEx <- "gcd"
> 
> ### * gcd
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gcd.coef
> ### Title: Computes Yanai's GCD in the context of the variable-subset
> ###   selection problem
> ### Aliases: gcd.coef
> ### Keywords: manip
> 
> ### ** Examples
> 
> 
> ## An example with a very small data set.
> 
> data(iris3) 
> x<-iris3[,,1]
> gcd.coef(cor(x),c(1,3))
[1] 0.7666286
> ## [1] 0.7666286
> gcd.coef(cor(x),c(1,3),pcindices=c(1,3))
[1] 0.584452
> ## [1] 0.584452
> gcd.coef(cor(x),c(1,3),pcindices=1)
[1] 0.6035127
> ## [1] 0.6035127
> 
> ## An example computing the GCDs of three subsets produced when the
> ## anneal function attempted to optimize the RV criterion (using an
> ## absurdly small number of iterations).
> 
> data(swiss)
> rvresults<-anneal(cor(swiss),2,nsol=4,niter=5,criterion="Rv")
> gcd.coef(cor(swiss),rvresults$subsets)
              Card.2
Solution 1 0.7092591
Solution 2 0.5608784
Solution 3 0.6351617
Solution 4 0.4962297
> 
> ##              Card.2
> ##Solution 1 0.4962297
> ##Solution 2 0.7092591
> ##Solution 3 0.4748525
> ##Solution 4 0.4649259
> 
> 
> 
> cleanEx(); ..nameEx <- "genetic"
> 
> ### * genetic
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: genetic
> ### Title: Genetic Algorithm searching for an optimal k-variable subset
> ### Aliases: genetic
> ### Keywords: manip
> 
> ### ** Examples
> 
> # For illustration of use, a small data set with very few iterations
> # of the algorithm.  
> 
> data(swiss)
> genetic(cor(swiss),3,4,popsize=10,nger=5,criterion="Rv")
 ABORTING. For cardinality k=
[1] 4
 there is not enough genetic diversity in generation number 
[1] 3
 for acceptable levels of consanguinity (couples differing by at least 2 genes).
 Try reducing the maximum acceptable number  of clones (maxclone) or increasing the population size (popsize)
 Best criterion value found so far:
[1] 0.9557145
$subsets
            Var.1 Var.2 Var.3
Solution 1      1     2     3
Solution 2      1     2     3
Solution 3      1     2     3
Solution 4      3     4     6
Solution 5      3     4     6
Solution 6      3     4     5
Solution 7      3     4     5
Solution 8      1     3     6
Solution 9      1     3     6
Solution 10     1     3     6

$values
 Solution 1  Solution 2  Solution 3  Solution 4  Solution 5  Solution 6 
  0.9141995   0.9141995   0.9141995   0.9034868   0.9034868   0.9020271 
 Solution 7  Solution 8  Solution 9 Solution 10 
  0.9020271   0.8988192   0.8988192   0.8988192 

$bestvalues
   Card.3 
0.9141995 

$bestsets
Var.1 Var.2 Var.3 
    1     2     3 

$call
genetic(mat = cor(swiss), kmin = 3, kmax = 4, popsize = 10, nger = 5, 
    criterion = "Rv")

> 
> ## For cardinality k=
> ##[1] 4
> ## there is not enough genetic diversity in generation number 
> ##[1] 5
> ## for acceptable levels of consanguinity (couples differing by at
> ## least 2 genes). 
> ## [1]
> ## Try reducing the maximum acceptable number  of clones (maxclone) or
> ## increasing the population size (popsize) 
> ## [1]
> ## Best criterion value found so far:
> ##[1] 0.9590526
> ##$subsets
> ##            Var.1 Var.2 Var.3
> ##Solution 1      1     2     3
> ##Solution 2      1     2     3
> ##Solution 3      1     2     5
> ##Solution 4      1     2     6
> ##Solution 5      3     4     6
> ##Solution 6      3     4     5
> ##Solution 7      3     4     5
> ##Solution 8      1     3     6
> ##Solution 9      2     4     5
> ##Solution 10     1     3     4
> ##
> ##$values
> ## Solution 1  Solution 2  Solution 3  Solution 4  Solution 5  Solution 6 
> ##  0.9141995   0.9141995   0.9098502   0.9074543   0.9034868   0.9020271 
> ## Solution 7  Solution 8  Solution 9 Solution 10 
> ##  0.9020271   0.8988192   0.8982510   0.8940945 
> ##
> ##$bestvalues
> ##   Card.3 
> ##0.9141995 
> ##
> ##$bestsets
> ##Var.1 Var.2 Var.3 
> ##    1     2     3 
> ##
> ##$call
> ##genetic(cor(swiss), 3, 4, popsize = 10, nger = 5, criterion = "Rv")
> 
> 
> 
> cleanEx(); ..nameEx <- "improve"
> 
> ### * improve
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: improve
> ### Title: Restricted Local Improvement search for an optimal k-variable
> ###   subset
> ### Aliases: improve
> ### Keywords: manip
> 
> ### ** Examples
> 
> # For illustration of use, a small data set with very few iterations
> # of the algorithm. 
> #
> 
> data(swiss)
> improve(cor(swiss),2,3,nsol=4,criterion="GCD")
$subsets
, , Card.2

           Var.1 Var.2 Var.3
Solution 1     3     6     0
Solution 2     3     6     0
Solution 3     3     6     0
Solution 4     3     6     0

, , Card.3

           Var.1 Var.2 Var.3
Solution 1     4     5     6
Solution 2     4     5     6
Solution 3     4     5     6
Solution 4     4     5     6


$values
              card.2   card.3
Solution 1 0.8487026 0.925372
Solution 2 0.8487026 0.925372
Solution 3 0.8487026 0.925372
Solution 4 0.8487026 0.925372

$bestvalues
   Card.2    Card.3 
0.8487026 0.9253720 

$bestsets
       Var.1 Var.2 Var.3
Card.2     3     6     0
Card.3     4     5     6

$call
improve(mat = cor(swiss), kmin = 2, kmax = 3, nsol = 4, criterion = "GCD")

> ## $subsets
> ## , , Card.2
> ##
> ##            Var.1 Var.2 Var.3
> ## Solution 1     3     6     0
> ## Solution 2     3     6     0
> ## Solution 3     3     6     0
> ## Solution 4     3     6     0
> ##
> ## , , Card.3
> ##
> ##            Var.1 Var.2 Var.3
> ## Solution 1     4     5     6
> ## Solution 2     4     5     6
> ## Solution 3     4     5     6
> ## Solution 4     4     5     6
> ##
> ##
> ## $values
> ##               card.2   card.3
> ## Solution 1 0.8487026 0.925372
> ## Solution 2 0.8487026 0.925372
> ## Solution 3 0.8487026 0.925372
> ## Solution 4 0.8487026 0.925372
> ##
> ## $bestvalues
> ##    Card.2    Card.3 
> ## 0.8487026 0.9253720 
> ##
> ## $bestsets
> ##        Var.1 Var.2 Var.3
> ## Card.2     3     6     0
> ## Card.3     4     5     6
> ##
> ##$call
> ##improve(cor(swiss), 2, 3, nsol = 4, criterion = "GCD")
> 
> #
> #
> # Forcing the inclusion of variable 1 in the subset
> #
> 
>  improve(cor(swiss),2,3,nsol=4,criterion="GCD",include=c(1))
$subsets
, , Card.2

           Var.1 Var.2 Var.3
Solution 1     1     6     0
Solution 2     1     6     0
Solution 3     1     6     0
Solution 4     1     6     0

, , Card.3

           Var.1 Var.2 Var.3
Solution 1     1     5     6
Solution 2     1     5     6
Solution 3     1     5     6
Solution 4     1     5     6


$values
              card.2    card.3
Solution 1 0.7284477 0.8048528
Solution 2 0.7284477 0.8048528
Solution 3 0.7284477 0.8048528
Solution 4 0.7284477 0.8048528

$bestvalues
   Card.2    Card.3 
0.7284477 0.8048528 

$bestsets
       Var.1 Var.2 Var.3
Card.2     1     6     0
Card.3     1     5     6

$call
improve(mat = cor(swiss), kmin = 2, kmax = 3, nsol = 4, include = c(1), 
    criterion = "GCD")

> 
> ## $subsets
> ## , , Card.2
> ##
> ##            Var.1 Var.2 Var.3
> ## Solution 1     1     6     0
> ## Solution 2     1     6     0
> ## Solution 3     1     6     0
> ## Solution 4     1     6     0
> ##
> ## , , Card.3
> ##
> ##            Var.1 Var.2 Var.3
> ## Solution 1     1     5     6
> ## Solution 2     1     5     6
> ## Solution 3     1     5     6
> ## Solution 4     1     5     6
> ##
> ##
> ## $values
> ##               card.2    card.3
> ## Solution 1 0.7284477 0.8048528
> ## Solution 2 0.7284477 0.8048528
> ## Solution 3 0.7284477 0.8048528
> ## Solution 4 0.7284477 0.8048528
> ##
> ## $bestvalues
> ##    Card.2    Card.3 
> ## 0.7284477 0.8048528 
> ##
> ## $bestsets
> ##        Var.1 Var.2 Var.3
> ## Card.2     1     6     0
> ## Card.3     1     5     6
> ##
> ##$call
> ##improve(cor(swiss), 2, 3, nsol = 4, criterion = "GCD", include = c(1))
> 
> 
> 
> cleanEx(); ..nameEx <- "leaps"
> 
> ### * leaps
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: leaps
> ### Title: A Leaps and Bounds Algorithm for finding the best variable
> ###   subsets
> ### Aliases: leaps
> ### Keywords: manip
> 
> ### ** Examples
> 
> 
> # For illustration of use, a small data set with very few iterations
> # of the algorithm. 
> 
> data(swiss)
> leaps(cor(swiss),nsol=3,criterion="RM")
$subsets
, , Card.1

           Var.1 Var.2 Var.3 Var.4 Var.5
Solution 1     3     0     0     0     0
Solution 2     1     0     0     0     0
Solution 3     4     0     0     0     0

, , Card.2

           Var.1 Var.2 Var.3 Var.4 Var.5
Solution 1     3     6     0     0     0
Solution 2     4     5     0     0     0
Solution 3     1     2     0     0     0

, , Card.3

           Var.1 Var.2 Var.3 Var.4 Var.5
Solution 1     4     5     6     0     0
Solution 2     1     2     5     0     0
Solution 3     3     4     6     0     0

, , Card.4

           Var.1 Var.2 Var.3 Var.4 Var.5
Solution 1     2     4     5     6     0
Solution 2     1     2     5     6     0
Solution 3     1     4     5     6     0

, , Card.5

           Var.1 Var.2 Var.3 Var.4 Var.5
Solution 1     1     2     3     5     6
Solution 2     1     2     4     5     6
Solution 3     2     3     4     5     6


$values
              card.1    card.2    card.3    card.4    card.5
Solution 1 0.6729689 0.8016409 0.9043760 0.9510757 0.9804629
Solution 2 0.6286185 0.7982296 0.8791856 0.9506434 0.9776338
Solution 3 0.6286130 0.7945390 0.8777509 0.9395708 0.9752551

$bestvalues
   Card.1    Card.2    Card.3    Card.4    Card.5 
0.6729689 0.8016409 0.9043760 0.9510757 0.9804629 

$bestsets
       Var.1 Var.2 Var.3 Var.4 Var.5
Card.1     3     0     0     0     0
Card.2     3     6     0     0     0
Card.3     4     5     6     0     0
Card.4     2     4     5     6     0
Card.5     1     2     3     5     6

$call
leaps(mat = cor(swiss), nsol = 3, criterion = "RM")

> 
> ##$subsets
> ##, , Card.1
> ##
> ##           Var.1 Var.2 Var.3 Var.4 Var.5
> ##Solution 1     3     0     0     0     0
> ##Solution 2     1     0     0     0     0
> ##Solution 3     4     0     0     0     0
> ##
> ##, , Card.2
> ##
> ##           Var.1 Var.2 Var.3 Var.4 Var.5
> ##Solution 1     3     6     0     0     0
> ##Solution 2     4     5     0     0     0
> ##Solution 3     1     2     0     0     0
> ##
> ##, , Card.3
> ##
> ##           Var.1 Var.2 Var.3 Var.4 Var.5
> ##Solution 1     4     5     6     0     0
> ##Solution 2     1     2     5     0     0
> ##Solution 3     3     4     6     0     0
> ##
> ##, , Card.4
> ##
> ##           Var.1 Var.2 Var.3 Var.4 Var.5
> ##Solution 1     2     4     5     6     0
> ##Solution 2     1     2     5     6     0
> ##Solution 3     1     4     5     6     0
> ##
> ##, , Card.5
> ##
> ##           Var.1 Var.2 Var.3 Var.4 Var.5
> ##Solution 1     1     2     3     5     6
> ##Solution 2     1     2     4     5     6
> ##Solution 3     2     3     4     5     6
> ##
> ##
> ##$values
> ##              card.1    card.2    card.3    card.4    card.5
> ##Solution 1 0.6729689 0.8016409 0.9043760 0.9510757 0.9804629
> ##Solution 2 0.6286185 0.7982296 0.8791856 0.9506434 0.9776338
> ##Solution 3 0.6286130 0.7945390 0.8777509 0.9395708 0.9752551
> ##
> ##$bestvalues
> ##   Card.1    Card.2    Card.3    Card.4    Card.5 
> ##0.6729689 0.8016409 0.9043760 0.9510757 0.9804629 
> ##
> ##$bestsets
> ##       Var.1 Var.2 Var.3 Var.4 Var.5
> ##Card.1     3     0     0     0     0
> ##Card.2     3     6     0     0     0
> ##Card.3     4     5     6     0     0
> ##Card.4     2     4     5     6     0
> ##Card.5     1     2     3     5     6
> ##
> ##$call
> ##leaps(cor(swiss), nsol = 3, criterion = "RM")
> 
> #
> #
> # Asking only for 2- and 3- dimensional subsets and excluding 
> # variable number 6.
> # 
> 
> data(swiss)
> leaps(cor(swiss),2,3,exclude=6,nsol=3,criterion="RM")
$subsets
, , Card.2

           Var.1 Var.2 Var.3
Solution 1     4     5     0
Solution 2     1     2     0
Solution 3     1     3     0

, , Card.3

           Var.1 Var.2 Var.3
Solution 1     1     2     5
Solution 2     1     4     5
Solution 3     2     4     5


$values
              card.2    card.3
Solution 1 0.7982296 0.8791856
Solution 2 0.7945390 0.8686515
Solution 3 0.7755232 0.8628693

$bestvalues
   Card.2    Card.3 
0.7982296 0.8791856 

$bestsets
       Var.1 Var.2 Var.3
Card.2     4     5     0
Card.3     1     2     5

$call
leaps(mat = cor(swiss), kmin = 2, kmax = 3, nsol = 3, exclude = 6, 
    criterion = "RM")

> 
> ##$subsets
> ##, , Card.2
> ##
> ##           Var.1 Var.2 Var.3
> ##Solution 1     4     5     0
> ##Solution 2     1     2     0
> ##Solution 3     1     3     0
> ##
> ##, , Card.3
> ##
> ##           Var.1 Var.2 Var.3
> ##Solution 1     1     2     5
> ##Solution 2     1     4     5
> ##Solution 3     2     4     5
> ##
> ##
> ##$values
> ##              card.2    card.3
> ##Solution 1 0.7982296 0.8791856
> ##Solution 2 0.7945390 0.8686515
> ##Solution 3 0.7755232 0.8628693
> ##
> ##$bestvalues
> ##   Card.2    Card.3 
> ##0.7982296 0.8791856 
> ##
> ##$bestsets
> ##       Var.1 Var.2 Var.3
> ##Card.2     4     5     0
> ##Card.3     1     2     5
> ##
> ##$call
> ##leaps(cor(swiss), 2, 3, exclude = 6, nsol = 3, criterion = "RM")
> 
> #
> # Searching for 2- and 3- dimensional subsets that best approximate the 
> # spaces generated by the first three Principal Components
> #
> 
> data(swiss)
> leaps(cor(swiss),2,3,criterion="gcd",pcindices=1:3,nsol=3)
$subsets
, , Card.2

           Var.1 Var.2 Var.3
Solution 1     4     5     0
Solution 2     5     6     0
Solution 3     4     6     0

, , Card.3

           Var.1 Var.2 Var.3
Solution 1     4     5     6
Solution 2     3     5     6
Solution 3     2     5     6


$values
              card.2    card.3
Solution 1 0.7831833 0.9253720
Solution 2 0.7475694 0.8459354
Solution 3 0.7383667 0.8243148

$bestvalues
   Card.2    Card.3 
0.7831833 0.9253720 

$bestsets
       Var.1 Var.2 Var.3
Card.2     4     5     0
Card.3     4     5     6

$call
leaps(mat = cor(swiss), kmin = 2, kmax = 3, nsol = 3, criterion = "gcd", 
    pcindices = 1:3)

> 
> ##$subsets
> ##, , Card.2
> ##
> ##           Var.1 Var.2 Var.3
> ##Solution 1     4     5     0
> ##Solution 2     5     6     0
> ##Solution 3     4     6     0
> ##
> ##, , Card.3
> ##
> ##           Var.1 Var.2 Var.3
> ##Solution 1     4     5     6
> ##Solution 2     3     5     6
> ##Solution 3     2     5     6
> ##
> ##
> ##$values
> ##              card.2    card.3
> ##Solution 1 0.7831827 0.9253684
> ##Solution 2 0.7475630 0.8459302
> ##Solution 3 0.7383665 0.8243032
> ##
> ##$bestvalues
> ##   Card.2    Card.3 
> ##0.7831827 0.9253684 
> ##
> ##$bestsets
> ##       Var.1 Var.2 Var.3
> ##Card.2     4     5     0
> ##Card.3     4     5     6
> ##
> ##$call
> ##leaps(cor(swiss), 2, 3, criterion = "gcd", pcindices = 1:3, nsol = 3)
> 
> 
> 
> cleanEx(); ..nameEx <- "rm"
> 
> ### * rm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rm.coef
> ### Title: Computes the RM coefficient for variable subset selection
> ### Aliases: rm.coef
> ### Keywords: manip
> 
> ### ** Examples
> 
> 
> ## An example with a very small data set.
> 
> data(iris3) 
> x<-iris3[,,1]
> rm.coef(var(x),c(1,3))
[1] 0.8724422
> ## [1] 0.8724422
> 
> ## An example computing the RMs of three subsets produced when the
> ## anneal function attempted to optimize the RV criterion (using an
> ## absurdly small number of iterations).
> 
> data(swiss)
> rvresults<-anneal(cor(swiss),2,nsol=4,niter=5,criterion="Rv")
> rm.coef(cor(swiss),rvresults$subsets)
              Card.2
Solution 1 0.7945390
Solution 2 0.7468382
Solution 3 0.7755232
Solution 4 0.7982296
> 
> ##              Card.2
> ##Solution 1 0.7982296
> ##Solution 2 0.7945390
> ##Solution 3 0.7649296
> ##Solution 4 0.7623326
> 
> 
> 
> cleanEx(); ..nameEx <- "rv"
> 
> ### * rv
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rv.coef
> ### Title: Computes the RV-coefficient applied to the variable subset
> ###   selection problem
> ### Aliases: rv.coef
> ### Keywords: manip
> 
> ### ** Examples
> 
> 
> # A simple example with a trivially small data set
> 
> data(iris3) 
> x<-iris3[,,1]
> rv.coef(var(x),c(1,3))
[1] 0.8659685
> ## [1] 0.8659685
> 
> ## An example computing the RVs of three subsets produced when the
> ## anneal function attempted to optimize the RM criterion (using an
> ## absurdly small number of iterations).
> 
> data(swiss)
> rmresults<-anneal(cor(swiss),2,nsol=4,niter=5,criterion="Rm")
> rv.coef(cor(swiss),rmresults$subsets)
              Card.2
Solution 1 0.8389669
Solution 2 0.7529066
Solution 3 0.8389669
Solution 4 0.8668403
> 
> ##              Card.2
> ##Solution 1 0.8389669
> ##Solution 2 0.8663006
> ##Solution 3 0.8093862
> ##Solution 4 0.7529066
> 
> 
> 
> cleanEx(); ..nameEx <- "trimmatrix"
> 
> ### * trimmatrix
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: trim.matrix
> ### Title: Given an ill-conditioned square matrix, deletes rows/columns
> ###   until a well-conditioned submatrix is obtained.
> ### Aliases: trim.matrix
> ### Keywords: manip
> 
> ### ** Examples
> 
> 
> # a trivial example, for illustration of use: creating an extra column,
> # as the sum of columns in the "iris" data, and then using the function
> # trim.matrix to exclude it from the data's correlation matrix
> 
> data(iris)
> lindepir<-cbind(apply(iris[,-5],1,sum),iris[,-5])
> colnames(lindepir)[1]<-"Sum"
> cor(lindepir)
                    Sum Sepal.Length Sepal.Width Petal.Length Petal.Width
Sum           1.0000000    0.9409143  -0.2230928    0.9713793   0.9538850
Sepal.Length  0.9409143    1.0000000  -0.1175698    0.8717538   0.8179411
Sepal.Width  -0.2230928   -0.1175698   1.0000000   -0.4284401  -0.3661259
Petal.Length  0.9713793    0.8717538  -0.4284401    1.0000000   0.9628654
Petal.Width   0.9538850    0.8179411  -0.3661259    0.9628654   1.0000000
> 
> ##                    Sum Sepal.Length Sepal.Width Petal.Length Petal.Width
> ##Sum           1.0000000    0.9409143  -0.2230928    0.9713793   0.9538850
> ##Sepal.Length  0.9409143    1.0000000  -0.1175698    0.8717538   0.8179411
> ##Sepal.Width  -0.2230928   -0.1175698   1.0000000   -0.4284401  -0.3661259
> ##Petal.Length  0.9713793    0.8717538  -0.4284401    1.0000000   0.9628654
> ##Petal.Width   0.9538850    0.8179411  -0.3661259    0.9628654   1.0000000
> 
> trim.matrix(cor(lindepir))
$trimmedmat
             Sepal.Length Sepal.Width Petal.Length Petal.Width
Sepal.Length    1.0000000  -0.1175698    0.8717538   0.8179411
Sepal.Width    -0.1175698   1.0000000   -0.4284401  -0.3661259
Petal.Length    0.8717538  -0.4284401    1.0000000   0.9628654
Petal.Width     0.8179411  -0.3661259    0.9628654   1.0000000

$numbers.discarded
[1] 1

$names.discarded
[1] "Sum"

$size
[1] 4

> 
> ##$trimmedmat
> ##             Sepal.Length Sepal.Width Petal.Length Petal.Width
> ##Sepal.Length    1.0000000  -0.1175698    0.8717538   0.8179411
> ##Sepal.Width    -0.1175698   1.0000000   -0.4284401  -0.3661259
> ##Petal.Length    0.8717538  -0.4284401    1.0000000   0.9628654
> ##Petal.Width     0.8179411  -0.3661259    0.9628654   1.0000000
> ##
> ##$numbers.discarded
> ##[1] 1
> ##
> ##$names.discarded
> ##[1] "Sum"
> ##
> ##$size
> ##[1] 4
> 
> data(swiss)
> lindepsw<-cbind(apply(swiss,1,sum),swiss)
> colnames(lindepsw)[1]<-"Sum"
> trim.matrix(cor(lindepsw))
$trimmedmat
                  Fertility Agriculture Examination   Education   Catholic
Fertility         1.0000000  0.35307918  -0.6458827 -0.66378886  0.4636847
Agriculture       0.3530792  1.00000000  -0.6865422 -0.63952252  0.4010951
Examination      -0.6458827 -0.68654221   1.0000000  0.69841530 -0.5727418
Education        -0.6637889 -0.63952252   0.6984153  1.00000000 -0.1538589
Catholic          0.4636847  0.40109505  -0.5727418 -0.15385892  1.0000000
Infant.Mortality  0.4165560 -0.06085861  -0.1140216 -0.09932185  0.1754959
                 Infant.Mortality
Fertility              0.41655603
Agriculture           -0.06085861
Examination           -0.11402160
Education             -0.09932185
Catholic               0.17549591
Infant.Mortality       1.00000000

$numbers.discarded
[1] 1

$names.discarded
[1] "Sum"

$size
[1] 6

> 
> ##$lowrankmat
> ##                  Fertility Agriculture examination   Education   Catholic
> ##Fertility         1.0000000  0.35307918  -0.6458827 -0.66378886  0.4636847
> ##Agriculture       0.3530792  1.00000000  -0.6865422 -0.63952252  0.4010951
> ##Examination      -0.6458827 -0.68654221   1.0000000  0.69841530 -0.5727418
> ##Education        -0.6637889 -0.63952252   0.6984153  1.00000000 -0.1538589
> ##Catholic          0.4636847  0.40109505  -0.5727418 -0.15385892  1.0000000
> ##Infant.Mortality  0.4165560 -0.06085861  -0.1140216 -0.09932185  0.1754959
> ##                 Infant.Mortality
> ##Fertility              0.41655603
> ##Agriculture           -0.06085861
> ##Examination           -0.11402160
> ##Education             -0.09932185
> ##Catholic               0.17549591
> ##Infant.Mortality       1.00000000
> ##
> ##$numbers.discarded
> ##[1] 1
> ##
> ##$names.discarded
> ##[1] "Sum"
> ##
> ##$size
> ##[1] 6
> 
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  0.49 0.06 0.55 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')