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我试图使用 R 和 varimax 旋转进行因子分析，但没有成功。我在 SAS 上运行相同的确切数据并且可以获得结果。

在 R 中，如果我使用

fa(r=cor(m1), nfactors=8, fm="ml", rotate="varimax")

我会得到

In smc, the correlation matrix was not invertible, smc's returned as 1s
In smc, the correlation matrix was not invertible, smc's returned as 1s
Error in optim(start, FAfn, FAgr, method = "L-BFGS-B", lower = 0.005,  : 
  L-BFGS-B needs finite values of 'fn'
In addition: Warning messages:
1: In cor.smooth(R) : Matrix was not positive definite, smoothing was done
2: In cor.smooth(R) : Matrix was not positive definite, smoothing was done
3: In log(e) : NaNs produced

如果我使用

factanal(cor(m1), factors=8)

我会得到

Error in solve.default(cv) : 
  system is computationally singular: reciprocal condition number = 4.36969e-19

谁能帮助我如何使用 R 成功进行因子分析。谢谢。

提前 Tq

4

1 回答 1

15

警告和错误表明您的矩阵是奇异的，因此优化问题不存在解决方案。

这意味着您需要使用不同的因子分析方法。在给定奇异矩阵的情况下，使用fa()in package您有两种选择来执行因子分析：psych

  • pa（主轴因素分析）
  • minres（最小残差因子分析）

但是，鉴于您的数据，minres似乎只会产生有用的结果，尽管有许多健康警告：

library(psych)
library(GPArotation)
fa(r=cor(m1), nfactors=8, rotate="varimax", SMC=FALSE, fm="minres")

这给出了：

In smc, the correlation matrix was not invertible, smc's returned as 1s
In factor.stats, the correlation matrix is singular, an approximation is used
In factor.scores, the correlation matrix is singular, an approximation is used
I was unable to calculate the factor score weights, factor loadings used instead
Factor Analysis using method =  minres
Call: fa(r = cor(m1), nfactors = 8, rotate = "varimax", SMC = FALSE, 
    fm = "minres")
Standardized loadings (pattern matrix) based upon correlation matrix
                MR1   MR3   MR2   MR6   MR5   MR4   MR7   MR8   h2    u2
Adorable       0.64  0.69  0.04  0.26  0.05  0.04  0.01  0.14 0.98 0.020
Appealing      0.69  0.66  0.06  0.22  0.06  0.00  0.03  0.08 0.98 0.021
Beautiful      0.39  0.82 -0.16  0.11  0.24 -0.05 -0.07 -0.08 0.93 0.071
Boring        -0.49 -0.70  0.33 -0.27  0.01  0.03  0.11 -0.16 0.95 0.054
Calm           0.76  0.42  0.33  0.10  0.28 -0.04  0.02  0.05 0.96 0.038
Charming       0.62  0.75  0.04  0.15  0.07 -0.03  0.03  0.01 0.98 0.024
Chic           0.07  0.94 -0.13  0.17 -0.03  0.12 -0.02  0.02 0.95 0.048
Childish      -0.13  0.00  0.04  0.04 -0.04  0.98  0.01  0.00 0.98 0.016
Classic        0.82  0.16  0.28 -0.31  0.14  0.10  0.16  0.06 0.94 0.058
Comfortable    0.66  0.50  0.19  0.39  0.27 -0.02  0.13  0.08 0.97 0.033
Cool           0.81  0.43  0.03  0.32  0.00  0.01 -0.03  0.20 0.98 0.016
Creative       0.78  0.37 -0.41  0.14 -0.05  0.06 -0.05  0.20 0.98 0.024
Crowded       -0.34 -0.12 -0.77 -0.13 -0.18  0.04  0.44  0.00 0.96 0.041
Cute           0.50  0.78  0.03  0.18  0.07  0.25 -0.09  0.14 0.98 0.024
Elegant        0.67  0.70  0.07 -0.04  0.10 -0.14  0.03  0.07 0.98 0.021
Feminine       0.09  0.96  0.00  0.01  0.01 -0.02  0.04  0.03 0.93 0.069
Fun            0.58  0.45 -0.21  0.56  0.01  0.20 -0.06 -0.08 0.95 0.054
Futuristic     0.91  0.26 -0.10  0.14 -0.07 -0.03 -0.18 -0.08 0.98 0.021
Gorgeous       0.82  0.52 -0.04  0.14  0.05 -0.09 -0.08 -0.01 0.98 0.019
Impressive     0.82  0.48 -0.02  0.23  0.05  0.00 -0.10  0.07 0.98 0.021
Interesting    0.72  0.55  0.05  0.34  0.15  0.01 -0.13  0.03 0.98 0.020
Light          0.20  0.49  0.30  0.72  0.22  0.03 -0.03  0.02 0.93 0.065
Lively         0.62  0.66 -0.06  0.37  0.16  0.00 -0.04 -0.03 0.98 0.021
Lovely         0.68  0.68 -0.04  0.12  0.19 -0.03 -0.08  0.01 0.98 0.019
Luxury         0.89  0.36 -0.02  0.00  0.08 -0.15 -0.04 -0.07 0.96 0.036
Masculine      0.91 -0.06 -0.05  0.24  0.05 -0.08  0.00 -0.17 0.94 0.063
Mystic         0.95  0.05  0.13  0.01 -0.03  0.00 -0.10  0.00 0.93 0.069
Natural        0.47  0.32  0.42  0.19  0.57 -0.17  0.23  0.02 0.95 0.050
Neat          -0.07  0.06  0.27  0.08  0.93 -0.01 -0.06 -0.01 0.96 0.042
Oldfashioned  -0.64 -0.54  0.20 -0.31  0.16  0.13  0.27 -0.16 0.97 0.026
Plain         -0.23 -0.19  0.88 -0.06  0.18  0.06  0.14 -0.14 0.94 0.062
Pretty         0.66  0.68  0.06  0.17  0.16 -0.11  0.01  0.10 0.97 0.029
Professional   0.82  0.41  0.09  0.18  0.16 -0.18  0.04  0.13 0.96 0.039
Refreshing     0.54  0.58  0.19  0.45  0.30 -0.03  0.10  0.07 0.98 0.021
Relaxing       0.56  0.65  0.34  0.26  0.21 -0.04  0.13 -0.03 0.97 0.026
Sexy           0.35  0.81  0.27  0.05 -0.01 -0.24  0.01 -0.19 0.94 0.056
Simple         0.08  0.01  0.96  0.08  0.09  0.02  0.04  0.12 0.96 0.041
Sophisticated  0.86  0.44 -0.01  0.04 -0.04 -0.12  0.08  0.05 0.96 0.040
Stylish        0.77  0.58  0.06  0.15  0.00 -0.07  0.07  0.08 0.97 0.030
Surreal        0.85  0.39  0.14  0.18 -0.05  0.02  0.08 -0.02 0.93 0.067

                        MR1   MR3  MR2  MR6  MR5  MR4  MR7  MR8
SS loadings           16.50 11.81 3.57 2.45 1.89 1.34 0.55 0.37
Proportion Var         0.41  0.30 0.09 0.06 0.05 0.03 0.01 0.01
Cumulative Var         0.41  0.71 0.80 0.86 0.91 0.94 0.95 0.96
Proportion Explained   0.43  0.31 0.09 0.06 0.05 0.03 0.01 0.01
Cumulative Proportion  0.43  0.74 0.83 0.89 0.94 0.98 0.99 1.00

Test of the hypothesis that 8 factors are sufficient.

The degrees of freedom for the null model are  780  and the objective function was  NaN
The degrees of freedom for the model are 488  and the objective function was  NaN 

The root mean square of the residuals (RMSR) is  0.01 
The df corrected root mean square of the residuals is  0.02 

Fit based upon off diagonal values = 1
Measures of factor score adequacy             
                                               MR1 MR3 MR2  MR6  MR5  MR4  MR7  MR8
Correlation of scores with factors               1   1   1 1.00 1.00 1.00 1.00 0.99
Multiple R square of scores with factors         1   1   1 1.00 1.00 1.00 0.99 0.98
Minimum correlation of possible factor scores    1   1   1 0.99 0.99 0.99 0.98 0.97
Warning messages:
1: In cor.smooth(R) : Matrix was not positive definite, smoothing was done
2: In log(det(m.inv.r)) : NaNs produced
3: In log(det(r)) : NaNs produced
4: In cor.smooth(r) : Matrix was not positive definite, smoothing was done
5: In cor.smooth(r) : Matrix was not positive definite, smoothing was done
于 2013-04-02T16:35:00.040 回答