class_docstr: Cox Proportional Hazards Regression Model
  
  The Cox PH Model is for right censored data.
  
  Parameters
  ----------
  endog : array_like
      The observed times (event or censoring)
  exog : 2D array_like
      The covariates or exogeneous variables
  status : array_like
      The censoring status values; status=1 indicates that an
      event occurred (e.g. failure or death), status=0 indicates
      that the observation was right censored. If None, defaults
      to status=1 for all cases.
  entry : array_like
      The entry times, if left truncation occurs
  strata : array_like
      Stratum labels.  If None, all observations are taken to be
      in a single stratum.
  ties : str
      The method used to handle tied times, must be either 'breslow'
      or 'efron'.
  offset : array_like
      Array of offset values
  missing : str
      The method used to handle missing data
  
  Notes
  -----
  Proportional hazards regression models should not include an
  explicit or implicit intercept.  The effect of an intercept is
  not identified using the partial likelihood approach.
  
  `endog`, `event`, `strata`, `entry`, and the first dimension
  of `exog` all must have the same length
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docstr: Returns the value of the log partial likelihood function
  evaluated at `params`, using the Breslow method to handle tied
  times.
docstr_tok: ["Returns","the","value","of","the","log","partial","likelihood","function","evaluated","at","`","params","`",",","using","the","Breslow","method","to","handle","tied","times","."]
code: def breslow_loglike(self, params):
      """
      Returns the value of the log partial likelihood function
      evaluated at `params`, using the Breslow method to handle tied
      times.
      """
  
      surv = self.surv
  
      like = 0.0
  
      # Loop over strata
      for stx in range(surv.nstrat):
          uft_ix = surv.ufailt_ix[stx]
          exog_s = surv.exog_s[stx]
          nuft = len(uft_ix)
  
          linpred = np.dot(exog_s, params)
          if surv.offset_s is not None:
              linpred += surv.offset_s[stx]
          linpred -= linpred.max()
          e_linpred = np.exp(linpred)
  
          xp0 = 0.0
  
          # Iterate backward through the unique failure times.
          for i in range(nuft)[::-1]:
              # Update for new cases entering the risk set.
              ix = surv.risk_enter[stx][i]
              xp0 += e_linpred[ix].sum()
  
              # Account for all cases that fail at this point.
              ix = uft_ix[i]
              like += (linpred[ix] - np.log(xp0)).sum()
  
              # Update for cases leaving the risk set.
              ix = surv.risk_exit[stx][i]
              xp0 -= e_linpred[ix].sum()
  
      return like
  
imports: import numpy
  from statsmodels.base import model
  import statsmodels.base.model
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.compat.pandas import Appender
label_desc: Use image node_id 8 for calling the PHReg obj's underlying member method code with example usage: obj.breslow_loglike(params) and returns: like