#s 6-1
> fa.result <- fa(set.data, nfactors=3, fm="minres", rotate="varimax")
> print(fa.result, digits=3)
Factor Analysis using method =  minres
Call: fa(r = set.data, nfactors = 3, rotate = "varimax", fm = "minres")
Standardized loadings (pattern matrix) based upon correlation matrix
     MR1   MR2   MR3    h2     u2  com
q1 0.209 0.949 0.206 0.986 0.0141 1.19
q2 0.248 0.411 0.406 0.395 0.6048 2.63
q3 0.170 0.512 0.170 0.320 0.6803 1.45
q4 0.925 0.262 0.235 0.980 0.0202 1.30
q5 0.534 0.328 0.455 0.599 0.4009 2.65
q6 0.741 0.223 0.394 0.754 0.2463 1.73
q7 0.260 0.159 0.487 0.330 0.6700 1.77
q8 0.135 0.100 0.537 0.317 0.6832 1.20
q9 0.172 0.209 0.442 0.269 0.7311 1.76

                        MR1   MR2   MR3
SS loadings           1.939 1.636 1.374
Proportion Var        0.215 0.182 0.153
Cumulative Var        0.215 0.397 0.550
Proportion Explained  0.392 0.330 0.278
Cumulative Proportion 0.392 0.722 1.000

Mean item complexity =  1.7
Test of the hypothesis that 3 factors are sufficient.

The degrees of freedom for the null model are  36  and the objective function was  3.774 with Chi Square of  951.739
The degrees of freedom for the model are 12  and the objective function was  0.072 

The root mean square of the residuals (RMSR) is  0.026 
The df corrected root mean square of the residuals is  0.046 

The harmonic number of observations is  257 with the empirical chi square  12.901  with prob <  0.376 
The total number of observations was  257  with Likelihood Chi Square =  17.967  with prob <  0.117 

Tucker Lewis Index of factoring reliability =  0.9803
RMSEA index =  0.0456  and the 90 % confidence intervals are  0 0.0836
BIC =  -48.622
Fit based upon off diagonal values = 0.996
Measures of factor score adequacy             
                                                    MR1   MR2   MR3
Correlation of (regression) scores with factors   0.976 0.985 0.757
Multiple R square of scores with factors          0.953 0.971 0.573
Minimum correlation of possible factor scores     0.905 0.942 0.146

#s 6-2
> fa.result <- fa(set.data, nfactors=3, fm="ml", rotate="promax", pro.m=2)
> print(fa.result, digits=3)
Factor Analysis using method =  ml
Call: fa(r = set.data, nfactors = 3, rotate = "promax", fm = "ml", 
    pro.m = 2)
Standardized loadings (pattern matrix) based upon correlation matrix
     ML2    ML1    ML3    h2    u2  com
q1 0.027  0.986 -0.001 0.995 0.005 1.00
q2 0.128  0.358  0.311 0.407 0.593 2.24
q3 0.069  0.495  0.063 0.311 0.689 1.07
q4 0.963  0.074 -0.014 0.979 0.021 1.01
q5 0.460  0.158  0.338 0.599 0.401 2.11
q6 0.722  0.047  0.216 0.751 0.249 1.19
q7 0.204  0.065  0.392 0.305 0.695 1.57
q8 0.017 -0.026  0.586 0.341 0.659 1.01
q9 0.048  0.124  0.432 0.272 0.728 1.19

                        ML2   ML1   ML3
SS loadings           2.062 1.635 1.264
Proportion Var        0.229 0.182 0.140
Cumulative Var        0.229 0.411 0.551
Proportion Explained  0.416 0.330 0.255
Cumulative Proportion 0.416 0.745 1.000

 With factor correlations of 
      ML2   ML1   ML3
ML2 1.000 0.418 0.460
ML1 0.418 1.000 0.408
ML3 0.460 0.408 1.000

Mean item complexity =  1.4
Test of the hypothesis that 3 factors are sufficient.

The degrees of freedom for the null model are  36  and the objective function was  3.774 with Chi Square of  951.739
The degrees of freedom for the model are 12  and the objective function was  0.066 

The root mean square of the residuals (RMSR) is  0.028 
The df corrected root mean square of the residuals is  0.048 

The harmonic number of observations is  257 with the empirical chi square  14.042  with prob <  0.298 
The total number of observations was  257  with Likelihood Chi Square =  16.557  with prob <  0.167 

Tucker Lewis Index of factoring reliability =  0.9849
RMSEA index =  0.0401  and the 90 % confidence intervals are  0 0.0795
BIC =  -50.032
Fit based upon off diagonal values = 0.995
Measures of factor score adequacy             
                                                    ML2   ML1   ML3
Correlation of (regression) scores with factors   0.990 0.997 0.818
Multiple R square of scores with factors          0.979 0.995 0.668
Minimum correlation of possible factor scores     0.958 0.990 0.337

#s 6-3
> fa.result <- fa(set.data, nfactors=3, fm="minres", rotate="varimax")
> fa.plot(fa.result,label=colnames(set.data),choose=c(1,2),pch=1,pos=3,
xlim=c(0,1),ylim=c(0,1))
> fa.plot(fa.result,label=colnames(set.data),choose=c(2,3),pch=1,pos=3,
xlim=c(0,1),ylim=c(0,1))

#s 6-4
> fa.result <- fa(set.data, nfactors=3, fm="ml", rotate="promax", pro.m=2) #Ђɂ͏ĂȂ
> print(fa.result$Structure, digits=3)

Loadings:
   ML2   ML1   ML3  
q1 0.439 0.997 0.413
q2 0.421 0.539 0.516
q3 0.305 0.549 0.297
q4 0.987 0.471 0.459
q5 0.682 0.488 0.614
q6 0.842 0.437 0.568
q7 0.412 0.311 0.512
q8 0.275 0.220 0.583
q9 0.299 0.320 0.504

                 ML2   ML1   ML3
SS loadings    2.945 2.485 2.293
Proportion Var 0.327 0.276 0.255
Cumulative Var 0.327 0.603 0.858
