#s 2-1
> five.data <- read.csv("http://mlab.arrow.jp/r_factor/five_subject.csv")
> fa(five.data, nfactors=2, rotate="varimax")
Factor Analysis using method =  minres
Call: fa(r = five.data, nfactors = 2, rotate = "varimax")
Standardized loadings (pattern matrix) based upon correlation matrix
      MR1  MR2   h2    u2 com
 0.96 0.11 0.94 0.061 1.0
Љ 0.41 0.20 0.21 0.789 1.4
p 0.40 0.18 0.19 0.810 1.4
w 0.17 0.90 0.83 0.168 1.1
 0.26 0.65 0.49 0.505 1.3

                       MR1  MR2
SS loadings           1.35 1.31
Proportion Var        0.27 0.26
Cumulative Var        0.27 0.53
Proportion Explained  0.51 0.49
Cumulative Proportion 0.51 1.00

Mean item complexity =  1.2
Test of the hypothesis that 2 factors are sufficient.

The degrees of freedom for the null model are  10  and the objective function was  1.04 with Chi Square of  268.84
The degrees of freedom for the model are 1  and the objective function was  0 

The root mean square of the residuals (RMSR) is  0 
The df corrected root mean square of the residuals is  0 

The harmonic number of observations is  263 with the empirical chi square  0  with prob <  0.95 
The total number of observations was  263  with Likelihood Chi Square =  0.01  with prob <  0.93 

Tucker Lewis Index of factoring reliability =  1.039
RMSEA index =  0  and the 90 % confidence intervals are  0 0.048
BIC =  -5.56
Fit based upon off diagonal values = 1
Measures of factor score adequacy             
                                                   MR1  MR2
Correlation of (regression) scores with factors   0.97 0.92
Multiple R square of scores with factors          0.94 0.84
Minimum correlation of possible factor scores     0.87 0.68

#s 2-2
> five.result <- fa(five.data, nfactors=2, rotate="varimax")
> fa.diagram(five.result, cut=0, simple=FALSE, sort=FALSE, digits=3)

#s 2-3
> five.result <- fa(five.data, nfactors=2, rotate="varimax")
> print(five.result, digits=3)
Factor Analysis using method =  minres
Call: fa(r = five.data, nfactors = 2, rotate = "varimax")
Standardized loadings (pattern matrix) based upon correlation matrix
       MR1   MR2    h2     u2  com
 0.963 0.106 0.939 0.0614 1.02
Љ 0.414 0.197 0.211 0.7894 1.43
p 0.397 0.178 0.190 0.8103 1.39
w 0.171 0.896 0.832 0.1680 1.07
 0.257 0.655 0.495 0.5054 1.30

                        MR1   MR2
SS loadings           1.353 1.313
Proportion Var        0.271 0.263
Cumulative Var        0.271 0.533
Proportion Explained  0.507 0.493
Cumulative Proportion 0.507 1.000

Mean item complexity =  1.2
Test of the hypothesis that 2 factors are sufficient.

The degrees of freedom for the null model are  10  and the objective function was  1.036 with Chi Square of  268.836
The degrees of freedom for the model are 1  and the objective function was  0 

The root mean square of the residuals (RMSR) is  0.001 
The df corrected root mean square of the residuals is  0.003 

The harmonic number of observations is  263 with the empirical chi square  0.004  with prob <  0.947 
The total number of observations was  263  with Likelihood Chi Square =  0.007  with prob <  0.932 

Tucker Lewis Index of factoring reliability =  1.0386
RMSEA index =  0  and the 90 % confidence intervals are  0 0.0482
BIC =  -5.565
Fit based upon off diagonal values = 1
Measures of factor score adequacy             
                                                    MR1   MR2
Correlation of (regression) scores with factors   0.967 0.918
Multiple R square of scores with factors          0.935 0.842
Minimum correlation of possible factor scores     0.870 0.684

#s 2-4
> cor(five.data)
                Љ      p      w      
 1.0000000 0.4196431 0.4017573 0.2596599 0.3168844
Љ 0.4196431 1.0000000 0.1999055 0.2466170 0.2371480
p 0.4017573 0.1999055 1.0000000 0.2291707 0.2171796
w 0.2596599 0.2466170 0.2291707 1.0000000 0.6304982
 0.3168844 0.2371480 0.2171796 0.6304982 1.0000000
> describe(five.data)
     vars   n  mean    sd median trimmed   mad min max range  skew kurtosis   se
    1 263 60.29 11.72     61   60.34 11.86  29  90    61 -0.07    -0.30 0.72
Љ    2 263 64.58 14.81     64   64.49 16.31  33  99    66  0.07    -0.66 0.91
p    3 263 59.87 18.84     61   60.56 22.24  15  98    83 -0.27    -0.74 1.16
w    4 263 50.83 23.18     50   50.60 20.76   0 100   100  0.09    -0.46 1.43
    5 263 49.75 20.64     50   49.82 20.76   1 100    99 -0.03    -0.31 1.27
> pairs.panels(five.data, digits=3)
