R version 3.2.2 (2015-08-14) -- "Fire Safety"
Copyright (C) 2015 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'q()' to quit R.

> x <- array(list(0.31
+ ,0.33
+ ,0.50
+ ,0.00
+ ,0.25
+ ,0.00
+ ,0.00
+ ,0.33
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+ ,1.00
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+ ,0.00
+ ,0.57
+ ,0.66
+ ,0.50
+ ,0.00
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+ ,0.75
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+ ,0.75
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+ ,0.66
+ ,0.66
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+ ,0.33
+ ,0.00
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+ ,0.75
+ ,0.50
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.00
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+ ,1.00
+ ,0.77
+ ,0.33
+ ,0.00
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+ ,0.75
+ ,0.66
+ ,0.66
+ ,0.75
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,0.66
+ ,1.00
+ ,0.80
+ ,0.33
+ ,1.00
+ ,0.66
+ ,0.66
+ ,0.50
+ ,0.66
+ ,0.66
+ ,0.75
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,0.66
+ ,1.00
+ ,0.95
+ ,0.33
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,0.66
+ ,1.00)
+ ,dim=c(17
+ ,51)
+ ,dimnames=list(c('N'
+ ,'1'
+ ,'2'
+ ,'3'
+ ,'4'
+ ,'5'
+ ,'6'
+ ,'7'
+ ,'8'
+ ,'9'
+ ,'10'
+ ,'11'
+ ,'12'
+ ,'13'
+ ,'14'
+ ,'15'
+ ,'16')
+ ,1:51))
>  y <- array(NA,dim=c(17,51),dimnames=list(c('N','1','2','3','4','5','6','7','8','9','10','11','12','13','14','15','16'),1:51))
>  for (i in 1:dim(x)[1])
+  {
+  	for (j in 1:dim(x)[2])
+  	{
+  		y[i,j] <- as.numeric(x[i,j])
+  	}
+  }
> par1 = '2'
> par1 <- '2'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., 2014, Factor Analysis (v1.0.3) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_factor_analysis.wasp/
> #Source of accompanying publication: 
> #
> library(psych)
> par1 <- as.numeric(par1)
> x <- t(x)
> nrows <- length(x[,1])
> ncols <- length(x[1,])
> y <- array(as.double(x[1:nrows,2:ncols]),dim=c(nrows,ncols-1))
> colnames(y) <- colnames(x)[2:ncols]
> rownames(y) <- x[,1]
> y
        1   2    3    4    5    6    7    8   9   10   11   12   13   14   15
0.31 0.33 0.5 0.00 0.25 0.00 0.00 0.33 0.25 0.5 1.00 0.33 0.33 1.00 1.00 0.00
0.57 0.66 0.5 0.00 0.66 0.75 0.33 0.75 0.75 0.5 0.75 0.66 0.33 0.66 0.66 0.33
0.52 0.66 0.5 0.75 0.25 0.25 0.33 0.75 0.50 0.5 0.33 0.33 0.75 1.00 0.75 0.33
0.47 0.33 0.5 0.00 0.25 0.50 0.66 0.66 0.50 0.5 0.66 0.33 0.66 0.33 0.66 0.33
0.67 0.66 1.0 0.66 0.66 0.75 0.66 0.66 0.75 1.0 0.66 0.33 0.66 0.33 0.66 0.66
0.62 0.66 1.0 0.66 0.66 0.50 0.66 0.66 0.75 1.0 0.66 0.33 0.66 0.33 0.66 0.33
0.74 1.00 1.0 0.66 0.66 0.75 0.66 0.66 0.75 1.0 1.00 0.66 0.66 0.33 0.66 0.66
0.71 1.00 0.0 1.00 1.00 0.75 0.66 0.66 0.75 1.0 1.00 0.66 0.66 0.33 0.66 0.66
0.79 1.00 1.0 1.00 1.00 1.00 0.66 0.66 1.00 0.0 1.00 1.00 1.00 0.33 0.66 1.00
0.66 0.66 1.0 0.66 0.66 0.75 0.66 0.66 0.75 0.5 0.66 0.33 0.33 0.66 1.00 0.66
0.74 0.66 1.0 1.00 1.00 1.00 0.66 0.66 1.00 1.0 0.66 0.66 0.66 0.33 0.66 0.66
0.65 0.66 1.0 1.00 0.66 1.00 0.66 0.25 0.75 0.5 0.66 0.66 0.66 0.66 0.66 0.66
0.55 0.66 1.0 0.66 0.66 0.75 0.66 0.75 0.50 0.5 0.33 0.33 0.33 0.33 0.66 0.33
0.45 0.25 0.5 0.66 0.33 0.50 0.33 0.66 0.50 0.5 0.66 0.66 0.33 0.66 0.33 0.33
0.32 0.33 0.5 0.33 0.33 0.00 0.33 0.33 0.33 0.5 1.00 0.00 0.00 1.00 0.33 0.00
0.35 0.33 0.5 0.33 0.33 0.33 0.33 0.33 0.33 0.5 1.00 0.33 0.33 0.66 0.33 0.00
0.32 0.33 1.0 0.33 0.33 0.00 0.33 0.33 0.25 0.5 0.66 0.00 0.33 0.66 0.33 0.00
0.39 0.33 1.0 0.33 0.33 0.25 0.33 0.33 0.25 0.5 0.66 0.33 0.33 0.66 0.66 0.33
0.4  0.33 0.0 0.66 0.33 0.25 0.33 0.33 0.50 0.5 0.66 0.66 0.33 0.66 0.66 0.33
0.36 0.33 0.5 0.33 0.33 0.00 0.33 0.33 0.25 0.5 1.00 0.00 0.33 1.00 0.33 0.33
0.32 0.25 0.5 0.33 0.33 0.00 0.33 0.33 0.25 0.0 1.00 0.33 0.33 0.66 0.33 0.33
0.31 0.25 0.5 0.66 0.33 0.33 0.00 0.33 0.25 0.5 1.00 0.33 0.33 1.00 0.33 0.00
0.22 0.00 0.5 0.33 0.00 0.00 0.00 0.33 0.25 0.5 1.00 0.00 0.33 1.00 0.00 0.00
0.37 0.33 0.5 0.66 0.00 0.33 0.33 0.33 0.50 0.5 0.66 0.33 0.33 1.00 0.33 0.00
0.21 0.00 0.5 0.33 0.00 0.00 0.00 0.00 0.33 0.5 0.66 0.33 0.00 1.00 0.00 0.33
0.19 0.00 0.5 0.00 0.00 0.00 0.00 0.33 0.33 0.0 0.66 0.33 0.00 1.00 0.00 0.33
0.19 0.00 0.0 0.00 0.00 0.00 0.00 0.33 0.25 0.5 1.00 0.33 0.00 0.66 0.33 0.00
0.17 0.00 0.0 0.00 0.00 0.00 0.00 0.33 0.25 0.5 1.00 0.33 0.00 0.66 0.00 0.00
0.19 0.00 0.0 0.00 0.33 0.00 0.00 0.33 0.25 0.5 1.00 0.00 0.00 0.66 0.33 0.00
0.18 0.00 0.5 0.00 0.33 0.00 0.00 0.33 0.25 0.0 1.00 0.00 0.00 0.66 0.33 0.00
0.32 0.33 0.5 0.33 0.00 0.00 0.33 0.33 0.25 0.5 1.00 0.33 0.33 0.66 0.33 0.00
0.38 0.33 0.5 0.33 0.00 0.00 0.33 0.33 0.25 0.5 1.00 0.66 0.33 0.66 0.66 0.00
0.44 0.33 0.5 0.33 0.33 0.00 0.33 0.33 0.25 0.5 1.00 0.66 0.33 1.00 0.66 0.33
0.42 0.33 0.5 0.33 0.00 0.00 0.33 0.33 0.25 0.5 1.00 0.66 0.33 1.00 0.66 0.33
0.43 0.33 0.5 0.33 0.33 0.00 0.33 0.33 0.25 0.5 1.00 0.66 0.66 1.00 0.66 0.33
0.46 0.33 0.5 0.66 0.33 0.00 0.33 0.33 0.50 0.5 1.00 0.66 0.66 1.00 0.66 0.33
0.46 0.33 0.5 0.66 0.00 0.25 0.33 0.33 0.50 0.5 1.00 0.66 0.66 1.00 0.66 0.33
0.53 0.66 0.5 0.33 0.00 0.25 0.33 0.66 0.50 0.5 1.00 1.00 1.00 1.00 0.66 0.33
0.51 0.66 0.5 0.33 0.00 0.25 0.33 0.33 0.50 0.5 1.00 1.00 1.00 1.00 0.66 0.33
0.51 0.66 0.5 0.33 0.00 0.25 0.33 0.00 0.50 0.5 1.00 1.00 1.00 1.00 0.66 0.33
0.5  0.33 0.0 0.33 0.00 0.00 0.33 0.33 0.33 0.5 1.00 1.00 1.00 1.00 1.00 0.33
0.59 0.66 0.5 0.66 0.33 0.25 0.33 0.33 0.50 0.5 1.00 1.00 1.00 0.66 1.00 0.66
0.59 0.66 0.5 0.66 0.33 0.25 0.33 0.66 0.50 0.5 1.00 1.00 0.66 1.00 1.00 0.66
0.6  0.33 1.0 0.66 0.33 0.25 0.33 0.66 0.50 0.5 1.00 1.00 0.66 0.66 1.00 0.66
0.6  0.33 0.5 0.66 0.33 0.25 0.33 0.66 0.75 0.5 1.00 1.00 0.66 0.66 1.00 0.66
0.6  0.33 0.5 0.66 0.33 0.25 0.33 0.66 0.75 0.5 1.00 1.00 0.66 0.66 1.00 0.66
0.64 0.33 0.5 0.66 0.33 0.25 0.66 0.33 0.50 0.5 1.00 1.00 0.66 1.00 1.00 0.66
0.76 0.33 1.0 0.66 0.66 0.50 0.66 0.66 0.75 0.5 1.00 1.00 1.00 1.00 1.00 0.66
0.77 0.33 0.0 0.66 0.66 0.75 0.66 0.66 0.75 1.0 1.00 1.00 1.00 1.00 1.00 0.66
0.8  0.33 1.0 0.66 0.66 0.50 0.66 0.66 0.75 1.0 1.00 1.00 1.00 1.00 1.00 0.66
0.95 0.33 1.0 1.00 1.00 1.00 1.00 1.00 1.00 1.0 1.00 1.00 1.00 1.00 1.00 0.66
       16
0.31 0.00
0.57 0.33
0.52 0.75
0.47 0.00
0.67 0.66
0.62 0.33
0.74 1.00
0.71 0.66
0.79 1.00
0.66 0.66
0.74 0.66
0.65 0.33
0.55 0.33
0.45 0.33
0.32 0.00
0.35 0.00
0.32 0.00
0.39 0.00
0.4  0.00
0.36 0.00
0.32 0.00
0.31 0.00
0.22 0.00
0.37 0.00
0.21 0.00
0.19 0.00
0.19 0.00
0.17 0.00
0.19 0.00
0.18 0.00
0.32 0.00
0.38 0.33
0.44 0.33
0.42 0.33
0.43 0.00
0.46 0.00
0.46 0.00
0.53 0.33
0.51 0.33
0.51 0.66
0.5  0.66
0.59 0.66
0.59 0.33
0.6  0.66
0.6  0.66
0.6  0.66
0.64 0.66
0.76 1.00
0.77 1.00
0.8  1.00
0.95 1.00
> fit <- principal(y, nfactors=par1, rotate='varimax')
> fit
Principal Components Analysis
Call: principal(r = y, nfactors = par1, rotate = "varimax")
Standardized loadings (pattern matrix) based upon correlation matrix
    PC1   PC2   h2   u2 com
1  0.50  0.56 0.57 0.43 2.0
2  0.17  0.61 0.40 0.60 1.1
3  0.60  0.52 0.63 0.37 2.0
4  0.38  0.78 0.75 0.25 1.5
5  0.45  0.81 0.85 0.15 1.6
6  0.60  0.66 0.79 0.21 2.0
7  0.44  0.62 0.58 0.42 1.8
8  0.65  0.65 0.85 0.15 2.0
9  0.38  0.40 0.30 0.70 2.0
10 0.36 -0.61 0.50 0.50 1.6
11 0.91 -0.12 0.85 0.15 1.0
12 0.89  0.10 0.80 0.20 1.0
13 0.15 -0.73 0.56 0.44 1.1
14 0.83  0.11 0.70 0.30 1.0
15 0.77  0.42 0.77 0.23 1.5
16 0.83  0.34 0.81 0.19 1.3

                       PC1  PC2
SS loadings           5.87 4.86
Proportion Var        0.37 0.30
Cumulative Var        0.37 0.67
Proportion Explained  0.55 0.45
Cumulative Proportion 0.55 1.00

Mean item complexity =  1.5
Test of the hypothesis that 2 components are sufficient.

The root mean square of the residuals (RMSR) is  0.06 
 with the empirical chi square  39.31  with prob <  1 

Fit based upon off diagonal values = 0.99> fs <- factor.scores(y,fit)
> fs
$scores
             PC1          PC2
0.31 -0.38411795 -1.040305150
0.57 -0.26293388  0.872563645
0.52 -0.04108371  0.605737357
0.47 -0.72809882  0.883243969
0.67 -0.06358066  1.972693807
0.62 -0.35341625  1.849438973
0.74  0.69108499  1.481674985
0.71  0.75654039  1.287667325
0.79  1.28447096  1.469388317
0.66  0.06515627  1.528336586
0.74  0.28394069  2.320808514
0.65  0.19092255  1.359543350
0.55 -1.01281218  2.316238676
0.45 -0.50915927  0.608364627
0.32 -1.08306315 -0.264855875
0.35 -0.85162370 -0.033750686
0.32 -1.49680401  0.593302847
0.39 -0.90746531  0.470900332
0.4  -0.37219170 -0.006870085
0.36 -0.73863368 -0.393366309
0.32 -0.78688093 -0.388561172
0.31 -0.67898252 -0.493122397
0.22 -1.19977817 -0.758848172
0.37 -0.82605662  0.015264884
0.21 -1.34494798 -0.535925809
0.19 -1.46294645 -0.554379130
0.19 -1.16689713 -0.911986896
0.17 -1.37297664 -0.811913665
0.19 -1.42531509 -0.552522869
0.18 -1.57289582 -0.435446700
0.32 -0.83804994 -0.435850537
0.38 -0.25746488 -0.722770865
0.44  0.07591596 -0.846502244
0.42  0.10036934 -1.039750664
0.43  0.14404100 -0.931894279
0.46  0.28875886 -0.751688985
0.46  0.30492620 -0.812193033
0.53  0.90507412 -0.886237129
0.51  0.87724840 -1.071702799
0.51  0.98996367 -1.277799959
0.5   1.21805716 -1.762251428
0.59  1.23952649 -0.587812813
0.59  1.11207555 -0.600601043
0.6   0.92179076 -0.163956367
0.6   1.06826516 -0.304746235
0.6   1.06826516 -0.304746235
0.64  1.24217527 -0.778984751
0.76  1.58693382 -0.047049098
0.77  1.80452410 -0.277260947
0.8   1.65621896  0.081754784
0.95  1.86193064  1.068731343

$weights
           PC1          PC2
1   0.04050051  0.090431404
2  -0.04956700  0.155660583
3   0.06800507  0.066739898
4  -0.02199146  0.173792487
5  -0.01080457  0.173091572
6   0.05038092  0.104462538
7   0.01747257  0.116459218
8   0.06269009  0.096629917
9   0.03349602  0.062270448
10  0.17744680 -0.233522185
11  0.24161922 -0.171653700
12  0.20378812 -0.103545052
13  0.14542326 -0.238280574
14  0.18681242 -0.090717037
15  0.12729157  0.009000321
16  0.15296966 -0.022456026

$r.scores
              PC1           PC2
PC1  1.000000e+00 -1.762479e-15
PC2 -1.741662e-15  1.000000e+00

$R2
PC1 PC2 
 NA  NA 

> postscript(file="/var/wessaorg/rcomp/tmp/1ciuh1447955338.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(fs$scores,pch=20)
> text(fs$scores,labels=rownames(y),pos=3)
> dev.off()
null device 
          1 
> 
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Rotated Factor Loadings',par1+1,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variables',1,TRUE)
> for (i in 1:par1) {
+ a<-table.element(a,paste('Factor',i,sep=''),1,TRUE)
+ }
> a<-table.row.end(a)
> for (j in 1:length(fit$loadings[,1])) {
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(fit$loadings)[j],header=TRUE)
+ for (i in 1:par1) {
+ a<-table.element(a,round(fit$loadings[j,i],3))
+ }
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/2940o1447955338.tab") 
> 
> try(system("convert tmp/1ciuh1447955338.ps tmp/1ciuh1447955338.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.736   0.105   0.836