1

我花了几个小时试图让 statsmodels 做我的 MANOVA，但没有成功。这是代码：

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，我总是得到一个 SingularMatrixError 所以我想我没有正确使用这个类。

在此先感谢您的帮助。

4

1 回答 1

1

根据Josef 在上述评论中引用的问题 4903 ，以下内容将起作用

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
============================================================
于 2019-06-03T14:54:05.723 回答