我尝试使用带有varimax旋转的R进行因子分析，但不成功。我在SAS上运行相同的确切数据，可以得到结果。
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
任何人都可以帮我使用R.成功进行因子分析。谢谢。
提前Tq
答案 0 :(得分：13)
警告和错误表明您的矩阵是单数的，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fa()包中使用psych，您可以使用两种方法在给定奇异矩阵的情况下执行因子分析：
pa（主轴因子分析）minres（最小残留因子分析）但是，根据您的数据，只有minres似乎会产生有用的结果，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