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我正在尝试使用 mclust 包中的 Mclust 函数进行潜在类建模。基于糖尿病数据的小插图（https://cran.r-project.org/web/packages/mclust/vignettes/mclust.html ）中描述的示例就像一个魅力，但如果我在我自己的数据上使用它，我得到了很奇怪的结果。这是我的数据的样子：

> head(example)
  HI01_01 HI01_02 HI01_03 HI01_07 HI01_08 HI01_09 HI01_10 HI01_12
1       4       3       3       2       2       1       1       1
2       7       7       7       7       6       1       1       1
3       7       7       7       7       7       7       7       7   
4       7       7       5       7       4       4       5       3
5       1       4       2       5       4       1       1       1
6       2       1       1       1       1       1       1       1
> str(example)
'data.frame':   281 obs. of  8 variables:
 $ HI01_01: num  4 7 7 7 1 2 5 7 6 4 ...
 $ HI01_02: num  3 7 7 7 4 1 5 7 5 2 ...
 $ HI01_03: num  3 7 7 5 2 1 2 7 6 3 ...
 $ HI01_07: num  2 7 7 7 5 1 1 7 1 3 ...
 $ HI01_08: num  2 6 7 4 4 1 1 5 1 1 ...
 $ HI01_09: num  1 1 7 4 1 1 1 3 1 1 ...
 $ HI01_10: num  1 1 7 5 1 1 1 3 1 5 ...
 $ HI01_12: num  1 1 7 3 1 1 1 1 1 1 ...

所以这是我对 mclust 模型的输出——为什么我会得到这些疯狂的 sigma 方差？

 > model1<-Mclust(example)
 > summary(model1, parameters = TRUE)
 ----------------------------------------------------
 Gaussian finite mixture model fitted by EM algorithm 
 ----------------------------------------------------

 Mclust EVE (ellipsoidal, equal volume and orientation) model with 3     
 components:

  log.likelihood   n df       BIC       ICL
  -3155.324 281 76 -6739.163 -6754.531

 Clustering table:
   1   2   3 
 160  91  30 

 Mixing probabilities:
  1         2         3 
 0.5508364 0.3273966 0.1217670 

 Means:
             [,1]     [,2]     [,3]
 HI01_12 1.000000 3.129923 1.601780
 HI01_09 1.519193 3.751240 2.171865
 HI01_10 1.810760 4.080920 2.408739
 HI01_02 4.577985 5.857100 5.651912
 HI01_03 4.781808 6.233245 5.932055
 HI01_01 4.895485 6.157288 5.796994
 HI01_07 3.512875 6.664422 4.115287
 HI01_08 2.243201 5.831780 2.115159

Variances:
[,,1]
[end of output deleted because the variances table was too wide and I 
could not get it to look like in my output; however all values ranged    
between values like -1.279519e+308 and inf]

我收到基于模型的其他代码的错误消息：

> plot(model1, what = "classification")
Error in eigen(sigma, symmetric = TRUE) : 
infinite or missing values in 'x'

我不知道是怎么回事。谁能给我关于我在这里做错了什么的建议？

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