我确信我遗漏了一些明显的东西，但这是我第一次在统计类之外使用 SEM。
大局是我正在尝试对多元调节中介分析进行建模，直到今天早上我开始遇到此错误时才遇到问题：
Error in eigen(VarCov, symmetric = TRUE, only.values = TRUE) :
infinite or missing values in 'x'
我想我已经完成了我之前关于这个错误的问题的尽职调查，并且我推测我观察到的协方差矩阵是奇异的。我一直试图找出导致问题的确切原因，并最终将我的模型缩减为单个潜在变量，但我仍然遇到此错误。因此，我在两个观察到的变量上对其进行了测试，得到了相同的错误，并使用三个不同的观察到的变量进行了尝试：
simple_data = modmed_subset[,c("baq_ff_1RC", "baq_ff_2RC")]
simple = '
#measurement model
feelfat =~ baq_ff_1RC + baq_ff_2RC'
fitted_simple = sem(simple, simple_data, se = "bootstrap", bootstrap = 10)
#>Error in eigen(VarCov, symmetric = TRUE, only.values = TRUE) :
#> infinite or missing values in 'x'
View(simple_data)
cor(simple_data, use="complete.obs")
#> baq_ff_1RC baq_ff_2RC
#>baq_ff_1RC 1.0000000 0.6429606
#>baq_ff_2RC 0.6429606 1.0000000
simple_data = modmed_subset[,c("baq_ff_3RC", "baq_ff_4RC", "baq_ff_5RC")]
simple = '
#measurement model
feelfat =~ baq_ff_3RC + baq_ff_4RC + baq_ff_5RC
#regression model'
fitted_simple = sem(simple, simple_data, se = "bootstrap", bootstrap = 10)
#>Error in eigen(VarCov, symmetric = TRUE, only.values = TRUE) :
#> infinite or missing values in 'x'
cor(simple_data, use="complete.obs")
#> baq_ff_3RC baq_ff_4RC baq_ff_5RC
#>baq_ff_3RC 1.0000000 0.6016666 0.6527502
#>baq_ff_4RC 0.6016666 1.0000000 0.5865960
#>baq_ff_5RC 0.6527502 0.5865960 1.0000000
我仍然遇到同样的错误。我应该提到，我还在一个数据集上测试了这些，只有具有完整数据的案例并得到了相同的错误。
PS：我知道这是极少数的引导重采样，但由于我在排除故障时一直在重新运行模型，所以我选择了它来保持运行。
以下是一些数据（来自省略不完整案例的数据框）
structure(list(baq_ff_3RC = c(4, 5, 5, 3, 4, 4, 5, 1, 5, 5, 2,
4, 5, 2, 1, 1, 3, 5, 5, 4, 2, 1, 1, 4, 5, 3, 3, 4, 5, 4, 5, 1,
3, 1, 5, 5, 4, 4, 2, 5, 2, 4, 5, 5, 5, 3, 2, 5, 3, 3, 1, 4, 1,
2, 1, 5, 2, 1, 5, 3, 1, 4, 1, 5, 4, 1, 4, 4, 2, 5, 4, 4, 3, 3,
1, 5, 5, 3, 3, 4, 4, 3, 2, 3, 2, 2, 2, 5, 4, 4, 5, 4, 4, 4, 5,
4, 4, 3, 4, 4, 1, 2, 1, 3, 5, 5, 4, 5, 3, 5, 2, 3, 3, 3, 4, 3,
4, 2, 4, 2, 1, 5, 1, 3, 4, 2, 4, 1, 4, 3, 1, 3, 4, 3, 4, 4, 2,
3, 5, 5, 3, 3, 3, 2, 4, 4, 4, 1, 4, 2, 1, 4, 2, 2, 2, 5, 4, 3,
2, 4, 2, 5, 3, 4, 4, 4, 4, 2, 4, 4, 1, 5, 3, 3, 5, 2, 3, 4, 2,
5, 4, 2, 3, 2, 4, 5, 5, 4, 2, 1, 3, 5, 4, 5, 5, 4, 5, 5, 5, 5,
1, 1, 1, 3, 4, 1, 4, 5, 5, 1, 4, 5, 5, 4, 4, 4, 4, 2, 5, 4, 4,
2, 4, 5, 4, 1, 5, 4, 4, 5, 3, 3, 5, 5, 2, 4, 1, 3, 4, 4, 4, 4,
4, 5, 3, 4, 4, 4, 1, 2, 3, 1, 4, 1, 4, 5, 5, 4, 4, 4, 5, 4, 4,
1, 3, 3, 4, 1, 5, 4, 4, 2, 5, 1, 2, 4, 1, 4, 4, 4, 4, 1, 1, 1,
5, 4, 1, 1, 1, 5, 1, 1, 1, 5, 3, 4, 1, 3, 4, 4, 1, 4, 4, 1, 1,
3, 3, 4, 5, 4, 2, 3, 5, 3, 3, 4, 1, 5, 3, 2, 5, 3, 4, 2, 4, 5,
3, 4, 2, 2, 4, 2, 2, 5, 3, 2, 4, 3, 2, 2, 3, 5, 1, 4, 2, 4, 4,
3, 3, 2, 2, 1, 4, 1, 4, 4, 5, 5, 1, 1, 2, 4, 1, 2, 1, 3, 4, 1,
3, 4, 5, 4, 4, 4, 5, 3, 1, 4, 5, 4, 5, 5, 5, 1, 1, 4, 4, 4, 5,
1, 1, 3, 2, 2, 3, 4, 5, 4, 5, 5, 1, 5, 4, 4, 3, 4, 4, 2, 3, 2,
5, 4, 4, 5, 5, 5, 3, 2, 3, 4, 5, 2, 4, 5, 5, 4, 4, 5, 4, 2, 3,
1, 2, 1, 4, 1, 5, 4, 1, 1, 1, 2, 2, 4, 5, 1, 4, 5, 4, 4, 5, 5,
5, 4, 4, 2, 2, 3, 2, 3, 5, 3, 5, 4, 4, 1, 3, 4, 1, 1, 2, 4, 1,
1, 2, 2, 2, 1, 1, 5, 5, 2, 2, 2, 3, 2, 4, 1, 5, 5, 2, 4, 3, 4,
5, 4, 1, 2, 4, 2, 4, 5, 3, 5, 2, 4, 5, 4, 1, 1, 2, 5, 4, 1, 5,
4, 5, 3, 4, 3, 1, 1, 4, 5, 4, 1, 4, 5, 1, 4, 4, 2, 4, 4, 4, 2,
2, 3, 1, 4, 5, 5, 2, 2, 4, 4, 4, 1, 4, 1, 1, 5, 5, 1, 4, 4, 5,
2, 5, 1, 4, 3, 5, 5, 4, 4, 2, 5, 4, 2, 4, 5, 2, 2, 3, 4, 2, 4,
1, 2, 5, 3, 5, 5, 3, 5, 3, 5, 3, 2, 3, 4, 2, 5, 5, 4, 4, 5, 4,
1, 4, 4, 4, 4, 3, 1, 2, 5, 3, 5, 1, 4, 5, 3, 1, 5, 5, 4, 5, 4,
1, 4, 5, 5, 5, 3, 1, 5, 2, 2, 3, 5, 5, 4, 1, 4, 1, 4, 5, 4, 5,
4, 5, 4, 4, 5, 5, 5, 2, 2, 3, 4, 5, 5, 4, 1, 2, 1, 4, 2, 5, 5,
1, 5, 5, 4, 3, 4, 5, 4, 1, 5, 4, 5, 5, 2, 3, 3, 4, 1, 5, 4, 2,
4, 3, 4, 4, 1, 4, 2, 5, 1, 1, 5, 2, 2, 3, 5, 4, 5, 5, 2, 3, 4,
5, 5, 1, 5, 4, 4, 5, 2, 5, 1, 5, 4, 5, 2, 4, 5, 4, 3, 4, 1, 1,
4, 4, 2, 5, 5, 2, 5, 3, 3, 4, 1, 2, 3, 1, 4, 3, 4, 2, 5, 1, 4,
3, 3, 1, 5, 3, 5, 2, 5, 1, 4, 5, 5, 4, 2, 1, 1, 5, 1, 3, 4, 1,
3, 4, 1, 4, 4, 1, 1, 3, 5, 4, 4, 5, 3, 4, 1, 4, 4, 5, 4, 1, 4,
2, 3, 5, 1, 1, 5, 3, 5, 4, 5, 3, 1, 1, 1, 3, 5, 5, 1, 4, 5, 4,
2, 4, 4, 2, 1, 3, 4, 5, 5, 5, 5, 5, 3), baq_ff_4RC = c(5, 4,
4, 4, 3, 4, 5, 2, 5, 5, 4, 4, 5, 4, 1, 4, 5, 5, 5, 4, 4, 3, 1,
4, 5, 4, 3, 3, 4, 3, 5, 2, 3, 3, 5, 5, 4, 4, 2, 5, 3, 5, 1, 5,
5, 1, 2, 4, 4, 4, 2, 3, 1, 3, 2, 5, 4, 2, 5, 3, 2, 4, 1, 3, 4,
5, 5, 4, 5, 1, 4, 1, 3, 4, 1, 3, 5, 3, 3, 4, 4, 4, 3, 3, 3, 1,
3, 5, 5, 5, 4, 4, 5, 5, 5, 3, 3, 4, 5, 2, 4, 2, 2, 4, 4, 5, 3,
1, 3, 5, 2, 4, 3, 3, 4, 2, 4, 4, 3, 2, 2, 3, 2, 4, 5, 3, 4, 2,
4, 4, 1, 4, 2, 5, 1, 5, 3, 2, 5, 5, 2, 4, 3, 4, 3, 4, 4, 2, 4,
2, 1, 4, 4, 4, 2, 5, 3, 3, 3, 4, 3, 5, 4, 5, 4, 4, 4, 4, 3, 3,
2, 5, 5, 5, 4, 2, 3, 4, 4, 5, 2, 3, 4, 3, 5, 4, 4, 5, 3, 3, 3,
5, 3, 4, 5, 3, 5, 5, 5, 4, 2, 2, 1, 2, 4, 1, 4, 5, 4, 1, 3, 5,
5, 4, 5, 4, 4, 4, 4, 3, 3, 4, 2, 5, 4, 4, 4, 4, 5, 5, 1, 4, 5,
5, 4, 4, 2, 2, 2, 4, 5, 3, 4, 5, 5, 4, 5, 5, 1, 4, 4, 3, 5, 1,
4, 1, 4, 3, 3, 4, 5, 3, 4, 1, 3, 2, 4, 1, 5, 4, 3, 2, 4, 3, 3,
4, 1, 3, 4, 5, 4, 3, 3, 5, 5, 5, 1, 2, 1, 5, 1, 4, 3, 5, 5, 5,
5, 4, 2, 3, 1, 4, 3, 1, 2, 4, 2, 5, 4, 4, 2, 2, 5, 3, 2, 4, 3,
4, 5, 1, 3, 4, 4, 2, 2, 5, 4, 3, 5, 4, 5, 2, 2, 4, 4, 4, 3, 2,
4, 1, 3, 5, 2, 4, 2, 5, 4, 3, 5, 2, 1, 1, 5, 4, 2, 2, 5, 4, 4,
5, 4, 5, 1, 3, 1, 5, 4, 1, 4, 4, 5, 1, 4, 2, 4, 4, 2, 4, 5, 4,
5, 4, 5, 1, 3, 2, 5, 3, 4, 4, 1, 2, 4, 3, 3, 3, 4, 5, 5, 5, 2,
5, 5, 5, 3, 2, 4, 3, 3, 3, 5, 4, 4, 5, 4, 5, 3, 2, 4, 5, 5, 3,
5, 4, 5, 4, 4, 5, 4, 3, 4, 1, 2, 2, 5, 2, 5, 4, 1, 2, 1, 3, 4,
3, 5, 3, 5, 5, 5, 5, 5, 5, 4, 4, 3, 2, 5, 2, 1, 4, 5, 4, 5, 2,
3, 1, 5, 5, 2, 3, 4, 4, 5, 4, 3, 4, 4, 4, 1, 5, 5, 4, 1, 4, 4,
1, 4, 1, 5, 5, 1, 3, 4, 5, 5, 4, 1, 4, 3, 2, 4, 5, 2, 5, 1, 5,
1, 4, 4, 1, 4, 3, 4, 2, 5, 4, 5, 4, 4, 4, 2, 1, 4, 4, 3, 2, 3,
3, 1, 4, 4, 4, 4, 2, 3, 3, 1, 2, 2, 4, 4, 5, 1, 2, 4, 4, 4, 2,
3, 2, 4, 4, 4, 2, 4, 1, 5, 3, 5, 2, 3, 2, 4, 5, 5, 4, 1, 5, 4,
2, 5, 1, 3, 3, 4, 2, 2, 4, 4, 4, 4, 4, 5, 5, 2, 5, 2, 5, 4, 3,
2, 3, 4, 5, 5, 2, 4, 5, 4, 4, 4, 4, 4, 4, 1, 2, 3, 5, 4, 5, 1,
5, 5, 4, 1, 4, 5, 5, 5, 4, 1, 4, 5, 4, 5, 4, 1, 4, 1, 4, 2, 5,
4, 4, 2, 5, 4, 5, 4, 5, 5, 4, 3, 4, 5, 5, 4, 3, 4, 1, 4, 5, 5,
5, 4, 1, 4, 3, 4, 1, 5, 5, 1, 5, 5, 2, 2, 2, 5, 3, 1, 5, 4, 5,
4, 3, 3, 4, 4, 1, 5, 3, 3, 5, 3, 3, 3, 3, 4, 2, 5, 5, 1, 5, 4,
4, 2, 3, 4, 4, 5, 3, 5, 4, 5, 5, 1, 4, 4, 5, 4, 4, 4, 2, 5, 4,
2, 5, 4, 2, 3, 2, 5, 1, 4, 4, 5, 4, 5, 5, 4, 2, 1, 5, 4, 1, 4,
4, 1, 5, 3, 3, 2, 4, 1, 5, 4, 3, 3, 4, 4, 5, 4, 5, 2, 2, 5, 2,
4, 2, 1, 5, 5, 3, 3, 2, 1, 4, 2, 4, 4, 5, 1, 1, 4, 4, 5, 3, 2,
2, 4, 4, 4, 4, 4, 4, 2, 4, 4, 4, 5, 1, 2, 4, 3, 5, 4, 5, 4, 2,
2, 5, 2, 5, 4, 1, 4, 5, 2, 2, 3, 5, 2, 1, 2, 4, 1, 5, 5, 5, 5,
3), baq_ff_5RC = c(4, 4, 3, 3, 4, 4, 5, 2, 3, 5, 4, 4, 5, 2,
1, 2, 3, 5, 5, 4, 5, 2, 3, 5, 5, 4, 4, 3, 4, 4, 4, 1, 3, 1, 5,
3, 1, 3, 3, 4, 3, 4, 5, 5, 5, 1, 4, 5, 4, 5, 3, 4, 1, 4, 1, 5,
4, 1, 5, 2, 1, 4, 1, 4, 5, 4, 5, 2, 2, 5, 4, 3, 3, 4, 1, 4, 2,
4, 2, 3, 4, 4, 3, 3, 3, 2, 5, 5, 3, 4, 4, 4, 4, 4, 5, 3, 1, 3,
3, 4, 2, 4, 2, 2, 5, 4, 4, 4, 4, 5, 3, 4, 4, 3, 4, 4, 3, 4, 1,
2, 1, 4, 4, 4, 4, 2, 2, 1, 4, 2, 1, 3, 3, 5, 4, 4, 5, 3, 5, 5,
3, 4, 2, 4, 2, 2, 4, 1, 5, 2, 1, 4, 4, 2, 3, 5, 4, 2, 4, 4, 2,
5, 3, 3, 2, 3, 4, 4, 4, 3, 1, 5, 3, 5, 2, 4, 4, 4, 4, 4, 2, 3,
3, 4, 4, 5, 4, 4, 2, 2, 5, 5, 3, 2, 5, 4, 5, 2, 5, 4, 4, 3, 2,
4, 3, 1, 4, 5, 5, 1, 3, 5, 5, 2, 3, 4, 5, 2, 4, 4, 3, 4, 4, 4,
4, 1, 4, 3, 4, 5, 3, 2, 5, 5, 5, 4, 1, 4, 3, 3, 4, 4, 3, 5, 2,
4, 5, 5, 1, 2, 2, 2, 2, 1, 4, 4, 4, 4, 3, 3, 5, 4, 4, 1, 2, 3,
2, 2, 5, 4, 2, 1, 5, 1, 3, 4, 1, 2, 4, 4, 4, 3, 4, 1, 5, 4, 1,
3, 1, 4, 1, 1, 3, 5, 4, 4, 1, 3, 3, 4, 1, 4, 1, 1, 1, 2, 1, 5,
4, 4, 5, 4, 5, 4, 4, 5, 2, 2, 5, 1, 2, 2, 4, 3, 4, 5, 4, 4, 3,
2, 3, 3, 4, 3, 2, 4, 4, 4, 4, 2, 4, 5, 4, 4, 4, 3, 4, 4, 4, 2,
1, 1, 5, 3, 4, 2, 5, 5, 4, 5, 3, 4, 1, 3, 1, 4, 4, 1, 4, 2, 4,
2, 4, 3, 4, 2, 1, 4, 5, 4, 5, 5, 2, 1, 2, 4, 3, 5, 5, 2, 1, 4,
4, 2, 3, 4, 5, 4, 5, 5, 3, 5, 2, 5, 4, 3, 4, 3, 2, 2, 5, 4, 3,
5, 4, 4, 4, 1, 5, 5, 4, 4, 5, 4, 5, 4, 4, 5, 5, 3, 3, 1, 3, 1,
4, 1, 4, 4, 1, 1, 1, 5, 3, 2, 5, 3, 5, 4, 4, 5, 5, 5, 5, 4, 4,
5, 4, 3, 2, 2, 5, 4, 5, 3, 4, 1, 1, 4, 4, 1, 1, 4, 1, 2, 2, 1,
2, 1, 1, 5, 5, 3, 4, 2, 3, 1, 4, 4, 5, 5, 1, 3, 5, 4, 5, 2, 1,
4, 4, 2, 5, 3, 2, 4, 2, 4, 5, 4, 4, 1, 2, 1, 4, 3, 5, 4, 5, 3,
5, 5, 3, 1, 5, 3, 4, 2, 4, 5, 1, 4, 4, 3, 2, 1, 1, 3, 1, 4, 3,
4, 4, 4, 1, 2, 5, 4, 2, 1, 2, 1, 2, 3, 4, 1, 4, 1, 5, 4, 5, 1,
4, 4, 4, 5, 4, 2, 2, 5, 2, 1, 4, 5, 1, 2, 1, 4, 4, 4, 3, 3, 4,
2, 5, 5, 3, 5, 4, 5, 4, 3, 2, 4, 4, 5, 5, 2, 4, 5, 2, 2, 4, 2,
2, 4, 2, 2, 3, 5, 4, 5, 1, 5, 5, 2, 1, 4, 5, 4, 4, 4, 1, 4, 5,
5, 5, 3, 1, 5, 2, 3, 3, 5, 5, 5, 2, 4, 4, 4, 4, 4, 5, 4, 2, 4,
5, 4, 4, 5, 4, 1, 3, 4, 4, 5, 4, 1, 4, 1, 5, 2, 5, 4, 1, 5, 5,
2, 4, 2, 5, 4, 1, 5, 5, 3, 5, 1, 3, 4, 3, 1, 5, 2, 2, 3, 2, 4,
4, 2, 5, 2, 5, 2, 2, 5, 4, 2, 4, 2, 4, 4, 5, 2, 4, 4, 5, 5, 1,
5, 4, 4, 1, 4, 4, 1, 5, 4, 5, 4, 4, 3, 4, 2, 4, 1, 3, 4, 4, 2,
5, 5, 2, 2, 4, 4, 3, 1, 4, 4, 1, 4, 4, 4, 4, 5, 1, 2, 3, 4, 4,
5, 4, 4, 3, 5, 3, 3, 5, 5, 1, 2, 1, 1, 5, 1, 2, 3, 1, 3, 2, 4,
4, 4, 1, 1, 2, 5, 5, 3, 4, 5, 4, 2, 2, 4, 4, 4, 3, 3, 1, 3, 5,
1, 2, 5, 4, 5, 1, 5, 5, 1, 1, 5, 1, 5, 2, 1, 2, 5, 4, 4, 3, 4,
2, 1, 3, 4, 5, 5, 5, 5, 5, 4)), row.names = c(1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L,
32L, 33L, 35L, 36L, 37L, 38L, 39L, 40L, 43L, 44L, 45L, 46L, 47L,
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647L, 648L, 649L, 650L, 651L, 652L, 653L, 654L, 655L, 656L, 657L,
658L, 659L, 660L, 661L, 662L, 663L, 664L, 665L, 666L, 667L, 668L,
669L, 670L, 671L, 672L, 673L, 674L, 675L, 676L, 677L, 678L, 679L,
680L, 681L, 682L, 683L, 684L, 685L, 686L, 687L, 688L, 690L, 691L,
692L, 693L, 694L, 695L, 696L, 698L, 699L, 700L, 701L, 703L, 704L,
705L, 706L, 707L, 708L, 709L, 710L, 711L, 712L, 713L, 714L, 715L,
717L, 718L, 719L, 720L, 721L, 722L, 723L, 724L, 725L, 726L, 727L,
728L, 729L, 730L, 731L, 732L, 733L, 734L, 735L, 736L, 737L, 738L,
739L, 740L, 741L, 742L, 743L, 744L, 745L, 746L, 747L, 748L, 749L,
750L, 751L, 752L, 753L, 755L, 756L, 757L, 758L, 759L, 760L, 761L,
762L, 763L, 764L, 765L, 766L, 767L, 768L, 769L, 770L, 771L, 772L,
773L, 774L, 775L, 776L, 777L, 778L, 779L, 780L, 781L, 782L, 783L,
784L, 785L, 786L, 787L, 788L, 789L, 790L, 791L, 792L, 793L, 794L,
795L, 796L, 797L, 798L, 799L, 800L, 801L, 802L, 803L, 804L, 806L,
808L, 809L, 810L, 811L, 812L, 813L, 814L, 815L, 816L, 817L, 818L,
819L, 820L, 821L, 822L, 823L, 824L, 825L, 826L, 827L, 828L, 829L,
830L, 831L, 832L, 833L, 834L, 835L, 836L, 837L, 838L, 839L, 841L,
842L, 843L, 844L, 845L, 846L, 847L, 848L, 849L, 850L, 851L, 852L,
853L, 854L, 855L, 856L, 857L, 858L, 859L, 860L, 861L, 862L, 863L,
864L, 865L, 866L, 867L, 869L, 870L, 871L, 873L, 874L, 875L, 876L,
877L), class = "data.frame", na.action = structure(c(`34` = 34L,
`41` = 41L, `42` = 42L, `70` = 70L, `75` = 75L, `83` = 83L, `131` = 131L,
`140` = 140L, `150` = 150L, `161` = 161L, `173` = 173L, `176` = 176L,
`178` = 178L, `183` = 183L, `185` = 185L, `193` = 193L, `213` = 213L,
`226` = 226L, `255` = 255L, `270` = 270L, `279` = 279L, `306` = 306L,
`307` = 307L, `323` = 323L, `324` = 324L, `349` = 349L, `356` = 356L,
`359` = 359L, `362` = 362L, `365` = 365L, `384` = 384L, `389` = 389L,
`413` = 413L, `426` = 426L, `452` = 452L, `454` = 454L, `459` = 459L,
`481` = 481L, `494` = 494L, `545` = 545L, `637` = 637L, `638` = 638L,
`639` = 639L, `640` = 640L, `644` = 644L, `689` = 689L, `697` = 697L,
`702` = 702L, `716` = 716L, `754` = 754L, `805` = 805L, `807` = 807L,
`840` = 840L, `868` = 868L, `872` = 872L), class = "omit"))