“calculate_bartlett_sphericity”测试输出 nan 值

Question

我有一个数据框

V

如下：

       ECON1     ECON2     ECON3     FOOD1     FOOD2     FOOD3      ENV1  \
28  0.310071  0.096913  0.228500  0.234986  0.260894  0.267858  0.489309   
28  0.353609  0.045075  0.222571  0.222803  0.248388  0.330560  0.060107   
28  0.280600  0.170201  0.232027  0.226792  0.233379  0.316765  0.114550   
28  0.299062  0.127866  0.198080  0.189948  0.222982  0.327082  0.052881   
28  0.346291  0.645534  0.371397  0.389068  0.380557  0.386004  0.186583   

        ENV2      HEA1      HEA2      HEA3     PERS1     PERS2     PERS3  \
28  0.206320  0.252537  0.266968  0.248452  0.184450  0.093345  0.173952   
28 -0.206570  0.263673  0.126182  0.265908  0.134481  0.191341  0.113324   
28  0.237818  0.257337  0.102037  0.214423  0.159002  0.321451  0.165960   
28  0.345857  0.272412  0.069192  0.251301  0.130606  0.132732  0.174925   
28  0.372713  0.382155  0.373531  0.468293  0.364305  0.299510  0.350822   

        COM1      COM2      POL1      POL2  
28  0.781430  0.487822  0.361886  0.233124  
28  0.083918  0.005381  0.266604  0.237078  
28  0.395897  0.257888  0.330607  0.229079  
28  0.000000  0.000000  0.307907  0.238908  
28  0.188402  0.101147  0.410619  0.385933  

我希望进行

bartlett_sphericity

测试，以使用观察到的相关矩阵与单位矩阵来检查观察到的变量（数据框

V

）是否相互关联。

from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity
chi_square_value, p_value=calculate_bartlett_sphericity(V)
print(chi_square_value, p_value)

我发现的问题是输出如下所示：

nan nan

我不确定我做错了什么。

V

中的所有值都是数字。有人可以评论一下吗？

Answer 1

Bartlett 球形度测试返回 NaN 值：

• 如果数据集中有 NaN/Inf 值
• 如果数据没有变化
• 如果存在完全相关的变量
• 如果存在高度相关的变量

您的案例似乎是最后一个。

加载数据：

from io import StringIO
data = """
ECON1,ECON2,ECON3,FOOD1,FOOD2,FOOD3,ENV1,ENV2,HEA1,HEA2,HEA3,PERS1,PERS2,PERS3,COM1,COM2,POL1,POL2
0.310071,0.096913,0.2285,0.234986,0.260894,0.267858,0.489309,0.20632,0.252537,0.266968,0.248452,0.18445,0.093345,0.173952,0.78143,0.487822,0.361886,0.233124
0.353609,0.045075,0.222571,0.222803,0.248388,0.33056,0.060107,-0.20657,0.263673,0.126182,0.265908,0.134481,0.191341,0.113324,0.083918,0.005381,0.266604,0.237078
0.2806,0.170201,0.232027,0.226792,0.233379,0.316765,0.11455,0.237818,0.257337,0.102037,0.214423,0.159002,0.321451,0.16596,0.395897,0.257888,0.330607,0.229079
0.299062,0.127866,0.19808,0.189948,0.222982,0.327082,0.052881,0.345857,0.272412,0.069192,0.251301,0.130606,0.132732,0.174925,0.0,0.0,0.307907,0.238908
0.346291,0.645534,0.371397,0.389068,0.380557,0.386004,0.186583,0.372713,0.382155,0.373531,0.468293,0.364305,0.29951,0.350822,0.188402,0.101147,0.410619,0.385933
"""
# Convert the string data to a file-like object
data_io = StringIO(data)
# Read the data into a pandas DataFrame
V = pd.read_csv(data_io)

检查每个变量的相关性大于 0.95 的次数：

(V.corr() > .95).sum(1).sort_values(ascending=False)

POL2	经济2	PERS1	经济3	食物1	食物2	PERS3	HEA1	HEA3	COM2	COM1	POL1	经济1	PERS2	ENV2	ENV1	食物3	HEA2
9	7	7	6	6	6	4	4	4	2	2	1	1	1	1	1	1	1

让我们从数据集中删除案例较多的变量，看看 Bartlett 测试是否返回正确的值：

for c in ['POL2','ECON2','PERS1']:
    V_fix = V.drop(c, axis=1)
    chi_square_value, p_value = calculate_bartlett_sphericity(V_fix)
    print(c, chi_square_value, p_value)

POL2 nan nan
ECON2 -1181.9125463026403 1.0
PERS1 -1182.5543638437994 1.0