class_docstr: Fit a conditional Poisson 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
  exog : array_like
      The covariates
  groups : array_like
      Codes defining the groups. This is a required keyword parameter.
class_docstr_tok: ["Fit","a","conditional","Poisson","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","exog",":","array_like","The","covariates","groups",":","array_like","Codes","defining","the","groups",".","This","is","a","required","keyword","parameter","."]
code: def loglike(self, params):
      ofs = None
      if hasattr(self, "offset"):
          ofs = self._offset_grp
  
      ll = 0.0
  
      for i in range(len(self._endog_grp)):
          xb = np.dot(self._exog_grp[i], params)
          if ofs is not None:
              xb += ofs[i]
          exb = np.exp(xb)
          y = self._endog_grp[i]
          ll += np.dot(y, xb)
          s = exb.sum()
          ll -= self._sumy[i] * np.log(s)
  
      return ll
  
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 for calling the ConditionalPoisson obj's underlying member method code with example usage: obj.loglike(params) and returns: ll