multinomial logit python
In [1]: import statsmodels.api as st
In [2]: iris = st.datasets.get_rdataset('iris', 'datasets')
In [3]: ### get the y
In [4]: y = iris.data.Species
In [5]: print y.head(3)
0 setosa
1 setosa
2 setosa
Name: Species, dtype: object
In [6]: ### get the x
In [7]: x = iris.data.ix[:, 0]
In [8]: x = st.add_constant(x, prepend = False)
In [9]: print x.head(3)
Sepal.Length const
0 5.1 1
1 4.9 1
2 4.7 1
In [10]: ### specify the model
In [11]: mdl = st.MNLogit(y, x)
In [12]: mdl_fit = mdl.fit()
Optimization terminated successfully.
Current function value: 0.606893
Iterations 8
In [13]: ### print model summary ###
In [14]: print mdl_fit.summary()
MNLogit Regression Results
==============================================================================
Dep. Variable: Species No. Observations: 150
Model: MNLogit Df Residuals: 146
Method: MLE Df Model: 2
Date: Fri, 23 Aug 2013 Pseudo R-squ.: 0.4476
Time: 22:22:58 Log-Likelihood: -91.034
converged: True LL-Null: -164.79
LLR p-value: 9.276e-33
=====================================================================================
Species=versicolor coef std err z P>|z| [95.0% Conf. Int.]
--------------------------------------------------------------------------------------
Sepal.Length 4.8157 0.907 5.310 0.000 3.038 6.593
const -26.0819 4.889 -5.335 0.000 -35.665 -16.499
--------------------------------------------------------------------------------------
Species=virginica coef std err z P>|z| [95.0% Conf. Int.]
-------------------------------------------------------------------------------------
Sepal.Length 6.8464 1.022 6.698 0.000 4.843 8.850
const -38.7590 5.691 -6.811 0.000 -49.913 -27.605
=====================================================================================
In [15]: ### marginal effects ###
In [16]: mdl_margeff = mdl_fit.get_margeff()
In [17]: print mdl_margeff.summary()
MNLogit Marginal Effects
=====================================
Dep. Variable: Species
Method: dydx
At: overall
=====================================================================================
Species=setosa dy/dx std err z P>|z| [95.0% Conf. Int.]
--------------------------------------------------------------------------------------
Sepal.Length -0.3785 0.003 -116.793 0.000 -0.385 -0.372
--------------------------------------------------------------------------------------
Species=versicolor dy/dx std err z P>|z| [95.0% Conf. Int.]
--------------------------------------------------------------------------------------
Sepal.Length 0.0611 0.022 2.778 0.005 0.018 0.104
--------------------------------------------------------------------------------------
Species=virginica dy/dx std err z P>|z| [95.0% Conf. Int.]
-------------------------------------------------------------------------------------
Sepal.Length 0.3173 0.022 14.444 0.000 0.274 0.360
=====================================================================================
In [18]: ### aic and bic ###
In [19]: print mdl_fit.aic
190.06793279
In [20]: print mdl_fit.bic
202.110473966
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