R语言-因子分析

> ######因子分析
> pt<-read.csv("profile_telecom.csv")
> head(pt)
       ID cnt_call cnt_msg cnt_wei cnt_web
1 1964627       46      90      36      31
2 3107769       53       2       0       2
3 3686296       28      24       5       8
4 3961002        9       2       0       4
5 4174839      145       2       0       1
6 5068087      186       4       3       1
> library(psych)
> #用fa.parallel()确定主成分个数
> fa.parallel(pt,fa="both",n.iter = 100)###因子分析碎石图选转折点而不是特征根大于一

> #fa(pt,nfactors = ,rotate = "varimax",fm=""):fm提取因子方法有pa(主轴迭代)
> #、ml(最大似然)、wls(最小二乘)等方法
> ptfa<-fa(r=pt,nfactors = 2,rotate = "promax",fm="pa",scores = T)
> ptfa
Factor Analysis using method =  pa
Call: fa(r = pt, nfactors = 2, rotate = "promax", scores = T, fm = "pa")
Standardized loadings (pattern matrix) based upon correlation matrix
           PA1   PA2     h2      u2 com
ID       -0.01  0.04 0.0016  0.9984 1.1
cnt_call  0.13 -0.02 0.0148  0.9852 1.1
cnt_msg   0.20  0.87 0.9319  0.0681 1.1
cnt_wei   1.01 -0.05 0.9896  0.0104 1.0
cnt_web   0.85  0.28 1.0048 -0.0048 1.2

                       PA1  PA2
SS loadings           1.96 0.98
Proportion Var        0.39 0.20
Cumulative Var        0.39 0.59
Proportion Explained  0.67 0.33
Cumulative Proportion 0.67 1.00

 With factor correlations of 
     PA1  PA2
PA1 1.00 0.41
PA2 0.41 1.00

Mean item complexity =  1.1
Test of the hypothesis that 2 factors are sufficient.

The degrees of freedom for the null model are  10  and the objective function was  5.01 with Chi Square of  2988.84
The degrees of freedom for the model are 1  and the objective function was  0.01 

The root mean square of the residuals (RMSR) is  0.01 
The df corrected root mean square of the residuals is  0.04 

The harmonic number of observations is  600 with the empirical chi square  1.47  with prob <  0.23 
The total number of observations was  600  with Likelihood Chi Square =  6.48  with prob <  0.011 

Tucker Lewis Index of factoring reliability =  0.982
RMSEA index =  0.096  and the 90 % confidence intervals are  0.037 0.171
BIC =  0.09
Fit based upon off diagonal values = 1
Measures of factor score adequacy             
                                                  PA1  PA2
Correlation of (regression) scores with factors     1 0.99
Multiple R square of scores with factors            1 0.98
Minimum correlation of possible factor scores       1 0.97
> tail(ptfa$scores)#看迭代结果的后五行
              PA1         PA2
[595,]  1.7944781  8.40547805
[596,]  0.2931260 -0.72735784
[597,] -0.3431254  0.48556060
[598,]  3.0720057 -0.72170499
[599,] -0.1089760  0.06106985
[600,] -0.5381938 -0.47854547
> factor.plot(ptfa)

fa.diagram(ptfa)

> ptsum<-cbind(ptpc,ptfa$scores)#和上期主成分分析的结果对比
> head(ptsum)
       ID cnt_call cnt_msg cnt_wei cnt_web        RC1        RC3        RC2        PA1
1 1964627       46      90      36      31  0.1952344  3.8712835 -0.3726676  1.1900638
2 3107769       53       2       0       2 -0.4219981 -0.6793516 -0.1552081 -0.5338358
3 3686296       28      24       5       8 -0.4194772  0.5202526 -0.5541321 -0.2507665
4 3961002        9       2       0       4 -0.2943034 -0.6714705 -0.8283602 -0.3536961
5 4174839      145       2       0       1 -0.5535192 -0.6802487  1.2451860 -0.6250782
6 5068087      186       4       3       1 -0.5413228 -0.6159420  1.8639601 -0.6259881
           PA2
1  4.036884424
2 -0.513818738
3  0.675144374
4 -0.007852624
5 -0.782894711
6 -1.046314108