我正在尝试使用统计模型计算 p_value

Question

这是我获取所需号码的代码

import statsmodels.api as sm
from statsmodels.stats.proportion import proportions_ztest

convert_old = len(df2[df2['group'] == 'control']['converted'] == 1)
convert_new = len(df2[df2['group'] == 'treatment']['converted'] == 1)
n_old =  len(df2[df2['group'] == 'control'])
n_new = len(df2[df2['group'] == 'treatment'])

实际型号为：

stat, pval = proportions_ztest([convert_new ,convert_old], [n_new, n_old])

我得到了这个结果：

p值为：nan

我也收到警告：

/opt/conda/lib/python3.6/site-packages/statsmodels/stats/weightstats.py:670:

 RuntimeWarning: invalid value encountered in double_scalars 
      zstat = value / std_diff

/opt/conda/lib/python3.6/site-packages/statsmodels/stats/weightstats.py:672:

 RuntimeWarning: invalid value encountered in absolute
      pvalue = stats.norm.sf(np.abs(zstat))*2

Answer 1

我认为问题在于如何获得 convert_old 和 convert_new 的数字。通过设置 ['converted'] == 1，您将根据每个单独的值获得一个带有 True/False 的系列，因此长度不会受到影响，并且您将始终拥有相同的长度。为了获得合适的长度，您可以尝试：

convert_old = len(df2[(df2['group'] == 'control') & (df2['converted'] == 1)]
convert_new = len(df2[(df2['group'] == 'treatment') & (df2['converted'] == 1)]

我正在尝试使用统计模型计算 p_value

I am trying to calculate the p_value using the stats model

python

pandas

statsmodels