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("sca-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('sca')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "agglomblock"
> 
> ### * agglomblock
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: agglomblock
> ### Title: Agglomerate Two Block-Components in SCA
> ### Aliases: agglomblock
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "allcrit"
> 
> ### * allcrit
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: allcrit
> ### Title: Simple Component Quality Criterion Computation
> ### Aliases: allcrit sccrit
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "corcomp"
> 
> ### * corcomp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: corcomp
> ### Title: Covariance and Correlation Matrix of Components P on S
> ### Aliases: corcomp covcomp
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(USJudgeRatings)
> S.jr <- cor(USJudgeRatings)
> sca.jr <- sca(S.jr, b=4, inter=FALSE)
> Vr <- covcomp(S.jr, P = sca.jr$simplemat)
> Vr
            B1          B2          D3          D4          D5
B1 10.12146983 -0.03027369 -0.11615970  0.22388444  0.09314354
B2 -0.03027369  1.00000000 -0.23839401 -0.04749288 -0.12583462
D3 -0.11615970 -0.23839401  0.39376977  0.06205256  0.09458930
D4  0.22388444 -0.04749288  0.06205256  0.23496304  0.01737470
D5  0.09314354 -0.12583462  0.09458930  0.01737470  0.10722424
> Cr <- corcomp(S.jr, P = sca.jr$simplemat)
> Cr
            B1          B2         D3          D4          D5
B1  1.00000000 -0.00951576 -0.0581852  0.14517851  0.08940966
B2 -0.00951576  1.00000000 -0.3799043 -0.09797805 -0.38428520
D3 -0.05818520 -0.37990426  1.0000000  0.20400402  0.46033536
D4  0.14517851 -0.09797805  0.2040040  1.00000000  0.10946394
D5  0.08940966 -0.38428520  0.4603354  0.10946394  1.00000000
> 
> 
> 
> cleanEx(); ..nameEx <- "firstpcres"
> 
> ### * firstpcres
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: firstpcres
> ### Title: First Principal Component of Residuals given Components
> ### Aliases: firstpcres
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "hearlossC"
> 
> ### * hearlossC
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: hearlossC
> ### Title: Hearing Loss Correlation Data
> ### Aliases: hearlossC
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(hearlossC)
> symnum(hearlossC)
        L5 L1 L2 L4 R5 R1 R2 R4
Left5c  1                      
Left1k  ,  1                   
Left2k  .  .  1                
Left4k        .  1             
Right5c ,  .        1          
Right1k ,  ,  .     ,  1       
Right2k    .  ,  .     .  1    
Right4k       .  ,        .  1 
attr(,"legend")
[1] 0 ' ' 0.3 '.' 0.6 ',' 0.8 '+' 0.9 '*' 0.95 'B' 1
> 
> 
> 
> cleanEx(); ..nameEx <- "maxmatrix"
> 
> ### * maxmatrix
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: maxmatrix
> ### Title: Largest Element in Correlation Matrix
> ### Aliases: maxmatrix
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(reflexesC)
> maxmatrix(reflexesC) # ->  0.98 at  [1, 2]
$row
[1] 1

$col
[1] 2

$val
[1] 0.98

> 
> 
> 
> cleanEx(); ..nameEx <- "nextdiff"
> 
> ### * nextdiff
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: nextdiff
> ### Title: Compute the Next Simple Difference-Component for SCA
> ### Aliases: nextdiff
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "percent"
> 
> ### * percent
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: percent
> ### Title: Simple Formatting of Percentages
> ### Aliases: percent
> ### Keywords: utilities print
> 
> ### ** Examples
> 
> percent(0.25)
[1] "25 %"
> noquote(percent((1:10)/10))
 [1] 10 %  20 %  30 %  40 %  50 %  60 %  70 %  80 %  90 %  100 %
> (pc <- percent((1:10)/30, 1, sep=""))
 [1] "3.3%"  "6.7%"  "10.0%" "13.3%" "16.7%" "20.0%" "23.3%" "26.7%" "30.0%"
[10] "33.3%"
> noquote(pc)
 [1] 3.3%  6.7%  10.0% 13.3% 16.7% 20.0% 23.3% 26.7% 30.0% 33.3%
> 
> 
> 
> cleanEx(); ..nameEx <- "pitpropC"
> 
> ### * pitpropC
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: pitpropC
> ### Title: Pitprops Strength Correlation Data
> ### Aliases: pitpropC
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(pitpropC)
> symnum(pitpropC)
        tp l m ts o rngt rngb bwm bwd w c k d
topdiam 1                                    
length  B  1                                 
moist   .    1                               
testsg  .    + 1                             
ovensg            1                          
ringtop .      .  . 1                        
ringbut .  .        +    1                   
bowmax  .  .             .    1              
bowdist .  ,             .    .   1          
whorls  .  .             ,    .   .   1      
clear                                 . 1    
knots                         .       .   1  
diaknot             .    .                  1
attr(,"legend")
[1] 0 ' ' 0.3 '.' 0.6 ',' 0.8 '+' 0.9 '*' 0.95 'B' 1
> 
> 
> 
> cleanEx(); ..nameEx <- "quickcrit"
> 
> ### * quickcrit
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: quickcrit
> ### Title: Additional Contribution of New Component to the SC System
> ### Aliases: quickcrit
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "reflexesC"
> 
> ### * reflexesC
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: reflexesC
> ### Title: Human Reflexes Correlation Data
> ### Aliases: reflexesC
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(reflexesC)
> symnum(reflexesC)
          t.R t.L b.R b.L w.R w.L k.R k.L a.R a.L
triceps.R 1                                      
triceps.L B   1                                  
biceps.R  .   ,   1                              
biceps.L  ,   ,   +   1                          
wrist.R   .   .   ,   ,   1                      
wrist.L   .   .   .   ,   B   1                  
knee.R    .   .   .   .   .   .   1              
knee.L            .   .           +   1          
ankle.R                           .   .   1      
ankle.L                           .   .   *   1  
attr(,"legend")
[1] 0 ' ' 0.3 '.' 0.6 ',' 0.8 '+' 0.9 '*' 0.95 'B' 1
> 
> 
> 
> cleanEx(); ..nameEx <- "sca"
> 
> ### * sca
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: sca
> ### Title: Simple Component Analysis - Interactively
> ### Aliases: sca print.simpcomp
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(pitpropC)
> sc.pitp <- sca(pitpropC, interactive=FALSE)
> sc.pitp
------------------------------------------------------------ 
Simple Component Analysis 
------------------------------------------------------------ 
Optimality criterion        : corrected sum of variances 
Clustering procedure        : median linkage 
Within-block differences    : TRUE 
Possible invertion of signs : FALSE 
Number of block-components  : 6 
Number of diff.-components  : 0 
------------------------------------------------------------ 
Simple matrix: 
        B1 B2 B3 B4 B5 B6
topdiam  1  0  0  0  0  0
length   1  0  0  0  0  0
moist    0  1  0  0  0  0
testsg   0  1  0  0  0  0
ovensg   0  0  1  0  0  0
ringtop  1  0  0  0  0  0
ringbut  1  0  0  0  0  0
bowmax   1  0  0  0  0  0
bowdist  1  0  0  0  0  0
whorls   1  0  0  0  0  0
clear    0  0  0  1  0  0
knots    0  0  0  0  1  0
diaknot  0  0  0  0  0  1
------------------------------------------------------------ 
Variance principal components:
 32.45 % 18.29 % 14.45 % 8.53 % 7.00 % 6.27 % 
Variance simple components   :
 30.16 % 14.48 % 7.69 % 7.69 % 7.69 % 7.69 % 
------------------------------------------------------------ 
Extracted variability PCA: 87.00 % 
Extracted variability SCA: 72.82 % 
Optimality SCA           : 83.70 % 
------------------------------------------------------------ 
Correlations simple components: 
      B1   B2    B3    B4    B5    B6
B1  1.00 0.20  0.08 -0.03 -0.21 -0.20
B2  0.20 1.00  0.04  0.13  0.20  0.07
B3  0.08 0.04  1.00 -0.09 -0.14 -0.21
B4 -0.03 0.13 -0.09  1.00  0.03  0.01
B5 -0.21 0.20 -0.14  0.03  1.00  0.18
B6 -0.20 0.07 -0.21  0.01  0.18  1.00
------------------------------------------------------------ 
Max (abs) correlation: 0.21 ( B1 - B5 ) 
------------------------------------------------------------ 
> ## to see it's low-level components:
> str(sc.pitp)
List of 10
 $ simplemat  : num [1:13, 1:6] 1 1 0 0 0 1 1 1 1 1 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:13] "topdiam" "length" "moist" "testsg" ...
  .. ..$ : chr [1:6] "B1" "B2" "B3" "B4" ...
 $ loadings   : num [1:13, 1:6] 0.378 0.378 0.000 0.000 0.000 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:13] "topdiam" "length" "moist" "testsg" ...
  .. ..$ : chr [1:6] "B1" "B2" "B3" "B4" ...
 $ allcrit    :List of 7
  ..$ varpc : num [1:6] 0.3245 0.1829 0.1445 0.0853 0.0700 ...
  ..$ varsc : Named num [1:6] 0.3016 0.1448 0.0769 0.0769 0.0769 ...
  .. ..- attr(*, "names")= chr [1:6] "B1" "B2" "B3" "B4" ...
  ..$ cumpc : num 0.87
  ..$ cumsc : num 0.728
  ..$ opt   : num 0.837
  ..$ corsc : num [1:6, 1:6]  1.0000  0.1987  0.0792 -0.0309 -0.2128 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : chr [1:6] "B1" "B2" "B3" "B4" ...
  .. .. ..$ : chr [1:6] "B1" "B2" "B3" "B4" ...
  ..$ maxcor:List of 3
  .. ..$ row: chr "B1"
  .. ..$ col: chr "B5"
  .. ..$ val: num 0.213
 $ nblock     : num 6
 $ ndiff      : num 0
 $ criterion  : chr "csv"
 $ cluster    : chr "median"
 $ withinblock: logi TRUE
 $ invertsigns: logi FALSE
 $ vardata    : num [1:13, 1:13] 1 0.954 0.364 0.342 -0.129 0.313 0.496 0.424 0.592 0.545 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:13] "topdiam" "length" "moist" "testsg" ...
  .. ..$ : chr [1:13] "topdiam" "length" "moist" "testsg" ...
 - attr(*, "class")= chr "simpcomp"
> 
> ## Let `X' be a matrix containing some data set whose rows correspond to
> ## subjects and whose columns correspond to variables. For example:
> 
> library(MASS)
> Sig <- function(p, rho) { r <- diag(p); r[col(r) != row(r)] <- rho; r}
> rmvN <- function(n,p, rho)
+         mvrnorm(n, mu=rep(0,p), Sigma= Sig(p, rho))
> X <- cbind(rmvN(100, 3, 0.7),
+            rmvN(100, 2, 0.9),
+            rmvN(100, 4, 0.8))
> 
> ## An optimal simple system with at least 5 components for the data in `X',
> ## where the number of block-components is such that correlations among
> ## them are all smaller than 0.4, can be automatically obtained as:
> 
> (r <- sca(cor(X), qmin=5, corblocks=0.4, interactive=FALSE))
------------------------------------------------------------ 
Simple Component Analysis 
------------------------------------------------------------ 
Optimality criterion        : corrected sum of variances 
Clustering procedure        : median linkage 
Within-block differences    : TRUE 
Possible invertion of signs : FALSE 
Number of block-components  : 3 
Number of diff.-components  : 2 
------------------------------------------------------------ 
Simple matrix: 
   B1 B2 B3 D4 D5
V1  0  1  0  1  1
V2  0  1  0  0 -2
V3  0  1  0 -1  1
V4  0  0  1  0  0
V5  0  0  1  0  0
V6  1  0  0  0  0
V7  1  0  0  0  0
V8  1  0  0  0  0
V9  1  0  0  0  0
------------------------------------------------------------ 
Variance principal components:
 37.65 % 25.31 % 19.96 % 4.41 % 3.71 % 
Variance simple components   :
 36.34 % 25.49 % 20.71 % 4.24 % 3.60 % 
------------------------------------------------------------ 
Extracted variability PCA: 91.04 % 
Extracted variability SCA: 89.71 % 
Optimality SCA           : 98.54 % 
------------------------------------------------------------ 
Correlations simple components: 
      B1    B2    B3    D4    D5
B1  1.00 -0.11 -0.10 -0.03  0.04
B2 -0.11  1.00 -0.05  0.01 -0.02
B3 -0.10 -0.05  1.00  0.12  0.01
D4 -0.03  0.01  0.12  1.00 -0.02
D5  0.04 -0.02  0.01 -0.02  1.00
------------------------------------------------------------ 
Max (abs) correlation: 0.12 ( B3 - D4 ) 
------------------------------------------------------------ 
> 
> ## On the other hand, an optimal simple system with two block-components
> ## and two difference-components for the data in `X' can be automatically
> ## obtained as:
> 
> (r <- sca(cor(X), b=2, d=2, qmin=0, corblocks=0, interactive=FALSE))
------------------------------------------------------------ 
Simple Component Analysis 
------------------------------------------------------------ 
Optimality criterion        : corrected sum of variances 
Clustering procedure        : median linkage 
Within-block differences    : TRUE 
Possible invertion of signs : FALSE 
Number of block-components  : 2 
Number of diff.-components  : 2 
------------------------------------------------------------ 
Simple matrix: 
   B1 B2 D3 D4
V1  0  1  2  1
V2  0  1  2  0
V3  0  1  2 -1
V4  0  1 -3  0
V5  0  1 -3  0
V6  1  0  0  0
V7  1  0  0  0
V8  1  0  0  0
V9  1  0  0  0
------------------------------------------------------------ 
Variance principal components:
 37.65 % 25.31 % 19.96 % 4.41 % 
Variance simple components   :
 36.34 % 22.43 % 23.77 % 4.24 % 
------------------------------------------------------------ 
Extracted variability PCA: 87.33 % 
Extracted variability SCA: 85.89 % 
Optimality SCA           : 98.36 % 
------------------------------------------------------------ 
Correlations simple components: 
      B1    B2    D3    D4
B1  1.00 -0.15  0.00 -0.03
B2 -0.15  1.00  0.11  0.08
D3  0.00  0.11  1.00 -0.09
D4 -0.03  0.08 -0.09  1.00
------------------------------------------------------------ 
Max (abs) correlation: 0.15 ( B1 - B2 ) 
------------------------------------------------------------ 
> 
> ## The resulting simple matrix is contained in `r$simplemat'.
> ## A matrix of scores for such simple components can then be obtained as:
> 
> (Z <- scale(X) %*% r$loadings)
                B1           B2           D3           D4
  [1,]  0.23489300 -0.230824971 -1.682311995  0.339250057
  [2,] -0.41763611 -0.862616406  1.253457321  0.902864469
  [3,] -2.15248370  0.434354964 -3.058601058  1.055258926
  [4,]  0.10524605  1.563918402  2.049593308 -0.251941227
  [5,]  1.93768043  1.683933450 -1.474133577 -1.154871217
  [6,]  3.05819451  0.061805775 -2.546244771  0.992912872
  [7,] -2.50812095  0.523990732  0.371290634  0.190988652
  [8,] -0.40550215  1.273969048  0.123173157  0.078481621
  [9,]  2.26894634 -0.327537916  1.648800464 -0.116601306
 [10,] -1.95669532 -0.299013273 -0.728116415 -0.095122517
 [11,] -4.64431521  2.694872479  0.436438051  0.009240197
 [12,] -1.66491793  0.405821956  0.248672700  0.304744842
 [13,] -1.74481650 -1.390428598 -0.233593395 -0.544903209
 [14,]  0.19101565 -3.650828468 -1.725485629 -0.630294444
 [15,] -0.67359236  1.250167112  1.175158707  0.560284633
 [16,]  0.45461687  0.681989083 -1.250242256  0.902829781
 [17,] -0.72525249 -0.631564284  0.438783760 -0.127030740
 [18,]  0.83371964  0.940896055  1.074493712 -0.666354529
 [19,] -0.57660018 -0.238423969  2.193642496  0.222035314
 [20,]  2.32514132  1.016500352  0.047450940 -0.015092785
 [21,] -1.29053896  2.150205060 -0.473513204 -0.884312358
 [22,]  0.60308350  2.143085297 -0.820745186 -0.312644891
 [23,] -1.55563492  0.619612769 -0.850379223 -0.328371149
 [24,]  0.47274531 -4.411681913 -0.191541017 -0.160824736
 [25,] -0.02187903  1.042221050  0.087484188 -0.648352909
 [26,] -3.07944005  0.135554206 -0.604225448  0.825819245
 [27,] -0.18898252 -0.696238066  0.147392446 -0.190697097
 [28,]  2.27568398 -1.957533748 -1.811432034 -0.895960905
 [29,]  1.39040750 -1.565455066  0.347657485  0.233491434
 [30,]  0.34857846  0.949818602 -0.341511651  0.188060930
 [31,] -2.15313095  1.306311891  1.734754490 -0.592811129
 [32,]  2.26412290 -1.687499677  1.503862634 -1.500612661
 [33,]  0.20205653  0.003444181  0.743910977 -0.494331973
 [34,] -0.76945934  0.925403202 -1.577619997  0.614914061
 [35,]  3.16734399 -2.276516347 -1.171401281 -0.112142079
 [36,] -1.38447555 -0.095657055 -1.289070456  0.250367042
 [37,]  0.63321628  0.119593715 -1.490682547  0.353921577
 [38,]  2.48206639 -0.261336340 -0.129455081 -0.569439661
 [39,]  1.24574604  0.940828527  1.484566297  0.715134330
 [40,] -0.47047777  0.347652256  1.318655874 -0.019969052
 [41,] -0.76409015  0.235873975 -1.031110203  0.775490316
 [42,]  0.76206999 -1.443223726  0.807810433  0.302315839
 [43,]  1.02037618  0.932266956  0.415373645  0.448221051
 [44,]  0.13198062  0.286247070  0.842135237 -0.416780407
 [45,]  2.55113930 -3.063438018  1.616316183  0.862697544
 [46,] -0.06185026 -2.334895330  0.681875312 -0.969076479
 [47,] -1.22928451  1.095392412 -0.645167775 -0.770786488
 [48,]  1.07526387  0.651034144  0.962283600 -0.174603064
 [49,] -3.99381524  0.361394771 -1.040176539  0.157542768
 [50,]  0.83891644  2.604003214 -1.142317238  0.883836845
 [51,]  0.98925955  1.616362949 -1.205544731 -0.046350189
 [52,] -0.36034089 -0.396480225 -1.437084177  0.210368102
 [53,] -2.01396038  0.589295916 -0.104176748  0.002670704
 [54,] -0.61354511 -1.830941645 -1.064319537  0.043802054
 [55,]  2.97901627  3.055055288 -0.221643552  0.667284744
 [56,] -1.32760431  2.914432383  1.411906430  0.826054150
 [57,] -4.25208420 -1.683958495  0.803859724 -1.566021240
 [58,]  1.63666568 -1.686335465 -1.013616469  0.432971943
 [59,] -0.84011282 -1.117112197  2.587619122  0.473549859
 [60,] -0.87929138 -0.527868850  0.009144008 -0.654808857
 [61,] -2.20879588  0.693929739  5.264520036  0.396857849
 [62,]  0.03601635  0.902725761 -1.501124659 -0.188758664
 [63,]  1.39309776  0.162600683  1.357152259 -0.410585877
 [64,] -1.01633733 -0.523502345  0.430013945  0.263824505
 [65,] -1.05861402 -1.308751017 -0.675850416  1.066589398
 [66,]  2.14461343  0.598971383 -0.506519629 -0.342779436
 [67,] -0.37150600 -1.953672801 -2.710680479 -0.191172734
 [68,] -0.17261533  2.217503365  0.894534939 -0.391676140
 [69,] -1.09198014 -0.487335647  0.715089331 -0.177798490
 [70,]  1.76080653  1.463957271  3.712703341 -0.604242275
 [71,] -1.81365553  0.104068741  0.865640220 -0.659801452
 [72,]  1.64958347 -0.870467620 -1.116828883  0.177605483
 [73,]  1.61784748 -0.103675952  1.469713195 -0.597354798
 [74,] -0.76798853 -1.470123290 -0.985113807  1.468641123
 [75,]  1.68287649 -2.844014563 -0.151766927  0.142410204
 [76,]  1.91826677  0.186914254  0.255949652  0.608511529
 [77,]  1.17613934 -0.649917047 -0.666963527 -1.457257368
 [78,]  3.87159588 -0.306992929  0.100979362 -0.052504599
 [79,] -3.60886469 -0.229755414  0.198455761 -0.972062609
 [80,] -2.08155934  0.581454772 -2.567793937  0.234633472
 [81,] -2.49554270 -0.269673951 -1.484572511  0.462072982
 [82,]  0.40457474  0.608890759 -1.389153262 -0.457325892
 [83,]  3.04671142  0.548136474  2.178863825  0.655958569
 [84,]  3.23540083 -2.037643658 -1.864890362 -0.091022297
 [85,]  0.99571791  1.593532335 -0.653730067 -0.460352355
 [86,]  1.68765617  0.196028761  0.358324384 -0.547675433
 [87,] -0.16863118 -0.647043884  3.347510959 -0.712184997
 [88,]  0.93834816 -0.286091593 -0.757229972  0.665059325
 [89,]  0.81304415 -1.361688207  2.360744385  0.305437349
 [90,] -1.35997662 -0.548828684  1.086611941  0.728232183
 [91,] -0.47065557  0.258164198 -2.053179701 -0.198759132
 [92,] -0.14571924  1.924872209  0.574922838  0.091383661
 [93,]  2.68012059  2.277695740  0.009184174  0.258663547
 [94,] -1.84123635  0.998197672  0.362187801 -0.995721423
 [95,] -3.89668049  1.779849509  1.750185250  1.207649422
 [96,]  1.51133991 -0.264425052  1.515922797  0.269049090
 [97,] -1.43538367 -0.464103621 -3.117396670  0.104487242
 [98,] -0.76960964 -0.899343169 -0.725218581  0.725400431
 [99,]  1.77643501 -2.408365184 -0.602794422 -0.130473299
[100,] -1.32410148 -0.051977193 -1.490502731 -0.109254697
> 
> ## On the other hand, scores of simple components calculated from the
> ## variance-covariance matrix of `X' can be obtained as:
> 
> r <- sca(var(X), b=2, d=2, qmin=0, corblocks=0, interactive=FALSE)
> Z <- scale(X, scale=FALSE) %*% r$loadings
> 
> ## One can also use the program interactively as follows:
> 
> if(interactive()) {
+   r <- sca(cor(X), corblocks=0.4, qmin=5, interactive = TRUE)
+ 
+   ## Since the interactive part of the program is active here, the proposed
+   ## system can then be  modified according to the user's wishes. The
+   ## result of the procedure will be contained in `r'.
+ }
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "simpvector"
> 
> ### * simpvector
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: simpvector
> ### Title: Simplify a (Principal Component) Vector to a Simple Component
> ### Aliases: simpvector
> ### Keywords: multivariate
> 
> ### ** Examples
> 
>  x0 <- c(-2:3, 3:-1,0:3,1,1)
>  cbind(x0, simpvector(x0)) # entries (-11, 0, 3)
      x0    
 [1,] -2 -11
 [2,] -1 -11
 [3,]  0   0
 [4,]  1   3
 [5,]  2   3
 [6,]  3   3
 [7,]  3   3
 [8,]  2   3
 [9,]  1   3
[10,]  0   0
[11,] -1 -11
[12,]  0   0
[13,]  1   3
[14,]  2   3
[15,]  3   3
[16,]  1   3
[17,]  1   3
> 
> 
> 
> cleanEx(); ..nameEx <- "sortmatrix"
> 
> ### * sortmatrix
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: sortmatrix
> ### Title: Sort Simple Component Matrix
> ### Aliases: sortmatrix
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  1.01 0.04 1.83 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')