regression - Python Statsmodels QuantReg Intercept -

problem setup in statsmodels quantile regression problem, least absolute deviation summary output shows intercept. in example, using formula

from __future__ import print_function import patsy import numpy np import pandas pd import statsmodels.api sm import statsmodels.formula.api smf import matplotlib.pyplot plt statsmodels.regression.quantile_regression import quantreg  data = sm.datasets.engel.load_pandas().data  mod = smf.quantreg('foodexp ~ income', data) res = mod.fit(q=.5) print(res.summary())                           quantreg regression results                           ============================================================================== dep. variable:                foodexp   pseudo r-squared:               0.6206 model:                       quantreg   bandwidth:                       64.51 method:                 least squares   sparsity:                        209.3 date:                fri, 09 oct 2015   no. observations:                  235 time:                        15:44:23   df residuals:                      233                                         df model:                            1 ==============================================================================                  coef    std err          t      p>|t|      [95.0% conf. int.] ------------------------------------------------------------------------------ intercept     81.4823     14.634      5.568      0.000        52.649   110.315 income         0.5602      0.013     42.516      0.000         0.534     0.586 ==============================================================================  condition number large, 2.38e+03. might indicate there strong multicollinearity or other numerical problems. 

the question

how can achieve summary output intercept without using statsmodels.formula.api smf formula approach?

of course, put question together, figured out. rather delete it, i'll share in case out there ever runs across this.

as suspected, needed add_constant() wasn't sure how. doing dumb , adding constant y (endog) variable instead of x (exog) variable.

the answer

from __future__ import print_function import patsy import numpy np import pandas pd import statsmodels.api sm import matplotlib.pyplot plt statsmodels.regression.quantile_regression import quantreg  data = sm.datasets.engel.load_pandas().data data = sm.add_constant(data)  mod = quantreg(data['foodexp'], data[['const', 'income']]) res = mod.fit(q=.5) print(res.summary())                           quantreg regression results                           ============================================================================== dep. variable:                foodexp   pseudo r-squared:               0.6206 model:                       quantreg   bandwidth:                       64.51 method:                 least squares   sparsity:                        209.3 date:                fri, 09 oct 2015   no. observations:                  235 time:                        22:24:47   df residuals:                      233                                         df model:                            1 ==============================================================================                  coef    std err          t      p>|t|      [95.0% conf. int.] ------------------------------------------------------------------------------ const         81.4823     14.634      5.568      0.000        52.649   110.315 income         0.5602      0.013     42.516      0.000         0.534     0.586 ==============================================================================  condition number large, 2.38e+03. might indicate there strong multicollinearity or other numerical problems. 

as fyi, find interesting add_constant() adds column of 1s data. more information add_constant() can found here.