stata可以进行这样的解析
程序先是factor var1-var10,pcf(var1-var10表示着10个指标的变量)运行得到此结果
Factor analysis/correlation Number of obs = 9
Method: principal-component factors Retained factors = 2
Rotation: (unrotated) Number of params = 11
--------------------------------------------------------------------------
Factor Eigenvalue Difference Proportion Cumulative
-------------+------------------------------------------------------------
Factor1 4.62365 3.45469 0.7706 0.7706
Factor2 1.16896 1.05664 0.1948 0.9654
Factor3 0.11232 0.05395 0.0187 0.9842
Factor4 0.05837 0.02174 0.0097 0.9939
Factor5 0.03663 0.03657 0.0061 1.0000
Factor6 0.00006 . 0.0000 1.0000
--------------------------------------------------------------------------
LR test: independent vs. saturated: chi2(15) = 100.49 Prob>chi2 = 0.0000
Factor loadings (pattern matrix) and unique variances
-------------------------------------------------
Variable Factor1 Factor2 Uniqueness
-------------+--------------------+--------------
rings 0.9792 0.0772 0.0353
logdsun 0.6710 -0.7109 0.0443
lograd 0.9229 0.3736 0.0088
logmoons 0.9765 0.0003 0.0465
logmass 0.8338 0.5446 0.0082
logdense -0.8451 0.4705 0.0644
-------------------------------------------------
会发现有俩个因子的特征值大于1,由这俩个因子代表变量
然后再进行rotate矩阵旋转
Factor analysis/correlation Number of obs = 9
Method: principal-component factors Retained factors = 2
Rotation: orthogonal varimax (Kaiser off) Number of params = 11
--------------------------------------------------------------------------
Factor Variance Difference Proportion Cumulative
-------------+------------------------------------------------------------
Factor1 3.36900 0.94539 0.5615 0.5615
Factor2 2.42361 . 0.4039 0.9654
--------------------------------------------------------------------------
LR test: independent vs. saturated: chi2(15) = 100.49 Prob>chi2 = 0.0000
Rotated factor loadings (pattern matrix) and unique variances
-------------------------------------------------
Variable Factor1 Factor2 Uniqueness
-------------+--------------------+--------------
rings 0.8279 0.5285 0.0353
logdsun 0.1071 0.9717 0.0443
lograd 0.9616 0.2580 0.0088
logmoons 0.7794 0.5882 0.0465
logmass 0.9936 0.0678 0.0082
logdense -0.3909 -0.8848 0.0644
-------------------------------------------------
Factor rotation matrix
--------------------------------
Factor1 Factor2
-------------+------------------
Factor1 0.7980
Factor2 0.6026 -0.7980
--------------------------------
最后进行Predict f1 f2这俩个因子得分便会在表dta文件中体现出来,然后综合得分f=a*f1+b*f2
a,b分别是因子所占权重