R心理四色-二分变量

Question

我有一个与人格问卷项目相对应的二分法变量的数据框架。这里是第一行。

  head(mixclinic)
  # A tibble: 6 x 15
    CMS_1 CMS_2 CMS_3 CMS_4 CMS_5 CMS_6 CMS_7 CMS_8 CMS_9 CMS_10 CMS_11
    <fct> <fct> <fct> <fct> <fct> <fct> <fct> <fct> <fct> <fct>  <fct> 
    1 1     1     0     1     0     1     0     0     0     0      0     
    2 1     1     0     1     0     0     0     1     0     0      0     
    3 1     1     0     1     0     1     0     0     0     0      0     
    4 0     1     0     1     0     1     0     1     0     0      0     
    5 0     1     0     1     0     1     0     0     0     0      0     
    6 1     1     0     1     1     1     0     0     0     0      0 

我想进行四次相关，以便找到解释变异性最大部分的因素。在搜索基于R的资源时，我偶然发现了一个名为“function”的包，它的功能是tetrachoric。我阅读了文档，但尽管如此，我还是无法执行分析。似乎缺少教程来帮助解决问题。有没有人可以提供帮助或参考有用的资源？谢谢

Answer 1

当输入数据帧作为参数时，函数可能不能很好地处理因子(可能是将它们全部切换为数字)。然而，它接受一个矩阵作为参数，所以这适用于我创建的数据集。将来，包含一个可重现的示例总是很有帮助的。希望这能有所帮助！

编辑:澄清。我认为问题在于您的数据集由多个因素组成。当变量是因子时，函数似乎不起作用。如果变量是数字或输入的数据是矩阵，它将起作用。因此，无论您选择将dataframe列转换为数字，还是将dataframe转换为矩阵，都是可行的(即，我的代码中的df_matrix <- data.matrix(df)行将数据帧转换为矩阵)。如果你有任何问题，请告诉我。

> # Creating your dataset
> 
> library(tidyverse)
> library(psych)
> 
> df <- data.frame(CMS_1 = sample(2, replace = T, size = 10)-1,
+                  CMS_2 = sample(2, replace = T, size = 10)-1,
+                  CMS_3 = sample(2, replace = T, size = 10)-1,
+                  CMS_4 = sample(2, replace = T, size = 10)-1,
+                  CMS_5 = sample(2, replace = T, size = 10)-1,
+                  CMS_6 = sample(2, replace = T, size = 10)-1,
+                  CMS_7 = sample(2, replace = T, size = 10)-1,
+                  CMS_8 = sample(2, replace = T, size = 10)-1)
> 
> df <- df %>% mutate_if(is.numeric, as.factor)
> str(df)
'data.frame':   10 obs. of  8 variables:
 $ CMS_1: Factor w/ 2 levels "0","1": 1 2 1 2 2 2 1 2 2 2
 $ CMS_2: Factor w/ 2 levels "0","1": 1 2 2 1 2 2 1 2 1 2
 $ CMS_3: Factor w/ 2 levels "0","1": 1 1 2 2 1 1 1 1 1 1
 $ CMS_4: Factor w/ 2 levels "0","1": 2 2 1 2 1 1 2 1 1 2
 $ CMS_5: Factor w/ 2 levels "0","1": 2 1 1 2 2 2 1 2 1 2
 $ CMS_6: Factor w/ 2 levels "0","1": 2 2 1 1 2 2 2 2 1 2
 $ CMS_7: Factor w/ 2 levels "0","1": 2 1 2 1 1 2 1 1 1 2
 $ CMS_8: Factor w/ 2 levels "0","1": 1 2 2 1 1 2 1 1 1 1
> 
> # Covnerting your data.frame to a matrix
> df_matrix <- data.matrix(df)
> 
> 
> tetrachoric(df_matrix)
For i = 6 j = 3  A cell entry of 0 was replaced with correct =  0.5.  Check your data!
For i = 8 j = 2  A cell entry of 0 was replaced with correct =  0.5.  Check your data!

Call: tetrachoric(x = df_matrix)
tetrachoric correlation 
      CMS_1 CMS_2 CMS_3 CMS_4 CMS_5 CMS_6 CMS_7 CMS_8
CMS_1  1.00                                          
CMS_2  0.47  1.00                                    
CMS_3 -0.31 -0.21  1.00                              
CMS_4 -0.37 -0.54 -0.02  1.00                        
CMS_5  0.43  0.27 -0.22  0.02  1.00                  
CMS_6  0.14  0.45 -0.74  0.29  0.44  1.00            
CMS_7 -0.44  0.34  0.22 -0.02  0.29  0.20  1.00      
CMS_8 -0.13  0.58  0.33 -0.33 -0.44 -0.10  0.46  1.00

 with tau of 
CMS_1 CMS_2 CMS_3 CMS_4 CMS_5 CMS_6 CMS_7 CMS_8 
-0.52 -0.25  0.84  0.00 -0.25 -0.52  0.25  0.52 
Warning message:
In cor.smooth(mat) : Matrix was not positive definite, smoothing was done