class_docstr: Fit a conditional logistic regression model to grouped data.
  
  Every group is implicitly given an intercept, but the model is fit using
  a conditional likelihood in which the intercepts are not present.  Thus,
  intercept estimates are not given, but the other parameter estimates can
  be interpreted as being adjusted for any group-level confounders.
  
  Parameters
  ----------
  endog : array_like
      The response variable, must contain only 0 and 1.
  exog : array_like
      The array of covariates.  Do not include an intercept
      in this array.
  groups : array_like
      Codes defining the groups. This is a required keyword parameter.
class_docstr_tok: ["Fit","a","conditional","logistic","regression","model","to","grouped","data",".","Every","group","is","implicitly","given","an","intercept",",","but","the","model","is","fit","using","a","conditional","likelihood","in","which","the","intercepts","are","not","present",".","Thus",",","intercept","estimates","are","not","given",",","but","the","other","parameter","estimates","can","be","interpreted","as","being","adjusted","for","any","group-level","confounders",".","Parameters","--","--","--","--","--","endog",":","array_like","The","response","variable",",","must","contain","only","0","and","1.","exog",":","array_like","The","array","of","covariates",".","Do","not","include","an","intercept","in","this","array",".","groups",":","array_like","Codes","defining","the","groups",".","This","is","a","required","keyword","parameter","."]
code: def __init__(self, endog, exog, missing="none", **kwargs):
      super().__init__(endog, exog, missing=missing, **kwargs)
  
      if np.any(np.unique(self.endog) != np.r_[0, 1]):
          msg = "endog must be coded as 0, 1"
          raise ValueError(msg)
  
      self.K = self.exog.shape[1]
  
imports: import numpy
  import statsmodels.base.model
  import statsmodels.regression.linear_model
  import statsmodels.base.wrapper
  from statsmodels.discrete.discrete_model import MultinomialResults, MultinomialResultsWrapper
  import collections
  import warnings
  import itertools
label_desc: Use image node_id 1 to create a new ConditionalLogit object from inherited base classes: _ConditionalModel with example: obj = ConditionalLogit(endog, exog, missing)