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> # testing that the print.GPArotation output is identical 
> # for 2 runs of quartimin rotation, that have 2
> # different looking loadings matrices wrt sign and order
> # the print.GPArotation should look identical
> 
> 
>  Sys.getenv("R_LIBS")
[1] "D:\\temp\\RtmpO4cY16\\RLIBS_2b7c074005b36"
>  library()
>  require("GPArotation")
Loading required package: GPArotation
>  search()
 [1] ".GlobalEnv"          "package:GPArotation" "package:stats"      
 [4] "package:graphics"    "package:grDevices"   "package:utils"      
 [7] "package:datasets"    "package:methods"     "Autoloads"          
[10] "package:base"       
>  Sys.info()
       sysname        release        version       nodename        machine 
     "Windows"   "Server x64"  "build 20348"     "CRANWIN3"       "x86-64" 
         login           user effective_user 
        "CRAN"         "CRAN"         "CRAN" 
> 
> require("stats")  
> require("GPArotation")  
> 
> athl <- matrix(c(
+   .73, -.07, .50, .82, -.01, .27,  .77, -.46, -.22,  .78, .17, .03,
+   .77, .41, .13,  .81, -.01, .27,  .71, -.45, -.30,  .82, .12, -.11,
+   .66, -.15, -.45,  .39, .76, -.40), byrow=T,  ncol =3)
> ## z1 gives the results that have the right ordering and sign of the factors
> ## z2 is a random other order and sign
> set.seed(238)
> z1 <- quartimin(athl, Tmat = Random.Start(3))
> head(z1$loadings)
           [,1]        [,2]         [,3]
[1,] -0.9451409  0.05346419  0.180327102
[2,] -0.7725340 -0.14313505 -0.011868009
[3,] -0.1322889 -0.86168891  0.128470606
[4,] -0.5377313 -0.20500970 -0.287529554
[5,] -0.6888492  0.05547964 -0.440716643
[6,] -0.7665208 -0.13863386 -0.009868994
> set.seed(46)
> z2 <- quartimin(athl, Tmat = Random.Start(3))
> head(z2$loadings)
           [,1]         [,2]        [,3]
[1,] -0.9451416 -0.180326159  0.05346510
[2,] -0.7725340  0.011869079 -0.14313424
[3,] -0.1322881 -0.128469679 -0.86168934
[4,] -0.5377307  0.287530600 -0.20500891
[5,] -0.6888486  0.440717717  0.05548088
[6,] -0.7665208  0.009870051 -0.13863307
> 
> 
> #> z1
> #Oblique rotation method Quartimin converged.
> #Loadings:
> #         [,1]    [,2]     [,3]
> # [1,]  0.9451 -0.0535 -0.18033
> # [2,]  0.7725  0.1431  0.01187
> # [3,]  0.1323  0.8617 -0.12847
> # [4,]  0.5377  0.2050  0.28753
> # [5,]  0.6888 -0.0555  0.44072
> # [6,]  0.7665  0.1386  0.00987
> # [7,]  0.0150  0.8967 -0.08931
> # [8,]  0.4047  0.3792  0.32647
> # [9,] -0.1056  0.7915  0.24071
> #[10,] -0.0155 -0.0165  0.94994
> #
> #                [,1]  [,2]  [,3]
> #SS loadings    3.034 2.405 1.401
> #Proportion Var 0.303 0.240 0.140
> #Cumulative Var 0.303 0.544 0.684
> #
> #Phi:
> #      [,1]  [,2]  [,3]
> #[1,] 1.000 0.554 0.259
> #[2,] 0.554 1.000 0.186
> #[3,] 0.259 0.186 1.000
> #> z2
> #Oblique rotation method Quartimin converged.
> #Loadings:
> #         [,1]    [,2]     [,3]
> # [1,]  0.9451 -0.0535 -0.18033
> # [2,]  0.7725  0.1431  0.01187
> # [3,]  0.1323  0.8617 -0.12847
> # [4,]  0.5377  0.2050  0.28753
> # [5,]  0.6888 -0.0555  0.44072
> # [6,]  0.7665  0.1386  0.00987
> # [7,]  0.0150  0.8967 -0.08930
> # [8,]  0.4047  0.3792  0.32647
> # [9,] -0.1056  0.7915  0.24071
> #[10,] -0.0155 -0.0165  0.94994
> #
> #                [,1]  [,2]  [,3]
> #SS loadings    3.034 2.405 1.401
> #Proportion Var 0.303 0.240 0.140
> #Cumulative Var 0.303 0.544 0.684
> #
> #Phi:
> #      [,1]  [,2]  [,3]
> #[1,] 1.000 0.554 0.259
> #[2,] 0.554 1.000 0.186
> #[3,] 0.259 0.186 1.000
> 
> proc.time()
   user  system elapsed 
   0.15    0.06    0.21