Statsmodels MANOVA:IndexError:索引1超出了大小为1的轴0的范围 [英] Statsmodels MANOVA : IndexError: index 1 is out of bounds for axis 0 with size 1

问题描述

我花了数小时试图使statsmodels不能成功执行MANOVA. 这是代码:

I have spent hours trying to make statsmodels do my MANOVA without success. Here is the code:

from statsmodels.multivariate.manova import MANOVA 

df = data
feats_list = ['col1', 'col2', 'col3' ... 'col4']
var_list = ['col5', 'col6']
endog, exog = np.asarray(df[feats_list]), np.asarray(df[var_list])
manov = MANOVA(endog, exog)
manov.mv_test()

提供:

    ---------------------------------------------------------------------------
    IndexError                                Traceback (most recent call last)
    <ipython-input-16-c3fc1d1f16f6> in <module>()
          1 manov = MANOVA(endog, exog)
    ----> 2 manov.mv_test()

    ~\Anaconda3\lib\site-packages\statsmodels\multivariate\manova.py in mv_test(self, hypotheses)
         68                     name = 'x%d' % (i)
         69                     L = np.zeros([1, self.exog.shape[1]])
    ---> 70                     L[i] = 1
         71                     hypotheses.append([name, L, None])
         72 

    IndexError: index 1 is out of bounds for axis 0 with size 

1

我也尝试自己提出假设，并且总是收到SingularMatrixError，所以我想我没有正确使用该类.

I tried also to put the hypotheses by myself and I always get a SingularMatrixError so I suppose that I am not using the class correctly.

预先感谢您的帮助.

推荐答案

基于问题4903 在上述评论中引用了约瑟夫(Josef)，以下方法将起作用

Based on issue 4903 referenced by Josef in the comments above, the following would work

from statsmodels.multivariate.manova import MANOVA 

feats_list = ['col1', 'col2', 'col3', 'col4']
var_list = ['col5', 'col6']
df = pd.DataFrame(
         np.random.random_sample(size=(100,6)),
         columns=feats_list + var_list
       )
endog, exog = np.asarray(df[feats_list]), np.asarray(df[var_list])


mod = MANOVA.from_formula('col1 + col2 + col3 + col4 ~ col5 + col6', data=df)
r = mod.mv_test()
print(r)

                 Multivariate linear model
============================================================

------------------------------------------------------------
       Intercept        Value  Num DF  Den DF F Value Pr > F
------------------------------------------------------------
          Wilks' lambda 0.3420 4.0000 94.0000 45.2047 0.0000
         Pillai's trace 0.6580 4.0000 94.0000 45.2047 0.0000
 Hotelling-Lawley trace 1.9236 4.0000 94.0000 45.2047 0.0000
    Roy's greatest root 1.9236 4.0000 94.0000 45.2047 0.0000
------------------------------------------------------------

------------------------------------------------------------
          col5          Value  Num DF  Den DF F Value Pr > F
------------------------------------------------------------
          Wilks' lambda 0.9297 4.0000 94.0000  1.7775 0.1399
         Pillai's trace 0.0703 4.0000 94.0000  1.7775 0.1399
 Hotelling-Lawley trace 0.0756 4.0000 94.0000  1.7775 0.1399
    Roy's greatest root 0.0756 4.0000 94.0000  1.7775 0.1399
------------------------------------------------------------

------------------------------------------------------------
          col6          Value  Num DF  Den DF F Value Pr > F
------------------------------------------------------------
          Wilks' lambda 0.9891 4.0000 94.0000  0.2590 0.9035
         Pillai's trace 0.0109 4.0000 94.0000  0.2590 0.9035
 Hotelling-Lawley trace 0.0110 4.0000 94.0000  0.2590 0.9035
    Roy's greatest root 0.0110 4.0000 94.0000  0.2590 0.9035
============================================================

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