Python中的回归

Question

试图通过pandas和statsmodels进行逻辑回归.不知道为什么我收到错误或如何解决它.

import pandas as pd
import statsmodels.api as sm
x = [1, 3, 5, 6, 8]
y = [0, 1, 0, 1, 1]
d = { "x": pd.Series(x), "y": pd.Series(y)}
df = pd.DataFrame(d)

model = "y ~ x"
glm = sm.Logit(model, df=df).fit()

错误:

Traceback (most recent call last):
  File "regress.py", line 45, in <module>
    glm = sm.Logit(model, df=df).fit()
TypeError: __init__() takes exactly 3 arguments (2 given)

Answer 1

你不能传递公式Logit.做:

In [82]: import patsy

In [83]: f = 'y ~ x'

In [84]: y, X = patsy.dmatrices(f, df, return_type='dataframe')

In [85]: sm.Logit(y, X).fit().summary()
Optimization terminated successfully.
         Current function value: 0.511631
         Iterations 6
Out[85]:
<class 'statsmodels.iolib.summary.Summary'>
"""
                           Logit Regression Results
==============================================================================
Dep. Variable:                      y   No. Observations:                    5
Model:                          Logit   Df Residuals:                        3
Method:                           MLE   Df Model:                            1
Date:                Fri, 30 Aug 2013   Pseudo R-squ.:                  0.2398
Time:                        16:56:38   Log-Likelihood:                -2.5582
converged:                       True   LL-Null:                       -3.3651
                                        LLR p-value:                    0.2040
==============================================================================
                 coef    std err          z      P>|z|      [95.0% Conf. Int.]
------------------------------------------------------------------------------
Intercept     -2.0544      2.452     -0.838      0.402        -6.861     2.752
x              0.5672      0.528      1.073      0.283        -0.468     1.603
==============================================================================
"""

这完全是关于如何完全按照你的要求做的文档.

编辑:您也可以使用公式API,如@ user333700所示:

In [22]: print sm.formula.logit(model, data=df).fit().summary()
Optimization terminated successfully.
         Current function value: 0.511631
         Iterations 6
                           Logit Regression Results
==============================================================================
Dep. Variable:                      y   No. Observations:                    5
Model:                          Logit   Df Residuals:                        3
Method:                           MLE   Df Model:                            1
Date:                Fri, 30 Aug 2013   Pseudo R-squ.:                  0.2398
Time:                        18:14:26   Log-Likelihood:                -2.5582
converged:                       True   LL-Null:                       -3.3651
                                        LLR p-value:                    0.2040
==============================================================================
                 coef    std err          z      P>|z|      [95.0% Conf. Int.]
------------------------------------------------------------------------------
Intercept     -2.0544      2.452     -0.838      0.402        -6.861     2.752
x              0.5672      0.528      1.073      0.283        -0.468     1.603
==============================================================================