当我尝试拟合线性混合效应模型时，为什么 statsmodels 会抛出 IndedxError？

Question

给定代码：

import statsmodels.api as sm
import statsmodels.formula.api as smf

df.reset_index(drop=True, inplace=True)
display(df.describe())
md = smf.mixedlm("c ~ iscorr", df, groups=df.subnum)
mdf = md.fit()

如果df 是pandas.DataFrame，我从smf.mixedlm 中得到以下错误：

IndexError                                Traceback (most recent call last)
<ipython-input-34-5373fe9b774a> in <module>()
      4 df.reset_index(drop=True, inplace=True)
      5 display(df.describe())
----> 6 md = smf.mixedlm("c ~ iscorr", df, groups=df.subnum)
      7 # mdf = md.fit()

/home/lthibault/.pyenv/versions/3.5.0/lib/python3.5/site-packages/statsmodels/regression/mixed_linear_model.py in from_formula(cls, formula, data, re_formula, subset, *args, **kwargs)
    651                                                subset=None,
    652                                                exog_re=exog_re,
--> 653                                                *args, **kwargs)
    654 
    655         # expand re names to account for pairs of RE

/home/lthibault/.pyenv/versions/3.5.0/lib/python3.5/site-packages/statsmodels/base/model.py in from_formula(cls, formula, data, subset, *args, **kwargs)
    148         kwargs.update({'missing_idx': missing_idx,
    149                        'missing': missing})
--> 150         mod = cls(endog, exog, *args, **kwargs)
    151         mod.formula = formula
    152 

/home/lthibault/.pyenv/versions/3.5.0/lib/python3.5/site-packages/statsmodels/regression/mixed_linear_model.py in __init__(self, endog, exog, groups, exog_re, use_sqrt, missing, **kwargs)
    537 
    538         # Split the data by groups
--> 539         self.endog_li = self.group_list(self.endog)
    540         self.exog_li = self.group_list(self.exog)
    541         self.exog_re_li = self.group_list(self.exog_re)

/home/lthibault/.pyenv/versions/3.5.0/lib/python3.5/site-packages/statsmodels/regression/mixed_linear_model.py in group_list(self, array)
    671         if array.ndim == 1:
    672             return [np.array(array[self.row_indices[k]])
--> 673                     for k in self.group_labels]
    674         else:
    675             return [np.array(array[self.row_indices[k], :])

/home/lthibault/.pyenv/versions/3.5.0/lib/python3.5/site-packages/statsmodels/regression/mixed_linear_model.py in <listcomp>(.0)
    671         if array.ndim == 1:
    672             return [np.array(array[self.row_indices[k]])
--> 673                     for k in self.group_labels]
    674         else:
    675             return [np.array(array[self.row_indices[k], :])

IndexError: index 7214 is out of bounds for axis 1 with size 7214

为什么会出现这个错误？ len(df) 报告有 7296 行，因此索引第 7214 行应该没有问题，并且显式重新索引确保索引范围从零到 7295。

如果你愿意，可以下载dfhere来玩弄它。

Answer 1

iscorr 中有 82 个空值：

>>> df.iscorr.isnull().sum()
82

放下它们，你会没事的：

df = df[df.iscorr.notnull()]

根据函数的文档字符串：

    Notes
    ------
    `data` must define __getitem__ with the keys in the formula
    terms args and kwargs are passed on to the model
    instantiation. E.g., a numpy structured or rec array, a
    dictionary, or a pandas DataFrame.

    If `re_formula` is not provided, the default is a random
    intercept for each group.

    This method currently does not correctly handle missing
    values, so missing values should be explicitly dropped from
    the DataFrame before calling this method.
    """

输出：

>>> mdf.params
Intercept         0.032000
iscorr[T.True]    0.030670
Intercept RE     -0.057462