class_docstr: Class to contain results of fitting a Cox proportional hazards
  survival model.
  
  PHregResults inherits from statsmodels.LikelihoodModelResults
  
  Parameters
  ----------
  See statsmodels.LikelihoodModelResults
  
  Attributes
  ----------
  model : class instance
      PHreg model instance that called fit.
  normalized_cov_params : ndarray
      The sampling covariance matrix of the estimates
  params : ndarray
      The coefficients of the fitted model.  Each coefficient is the
      log hazard ratio corresponding to a 1 unit difference in a
      single covariate while holding the other covariates fixed.
  bse : ndarray
      The standard errors of the fitted parameters.
  
  See Also
  --------
  statsmodels.LikelihoodModelResults
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code: def __init__(
      self,
      model,
      params,
      cov_params,
      scale=1.0,
      covariance_type="naive",
  ):
      # There is no scale parameter, but we need it for
      # meta-procedures that work with results.
  
      self.covariance_type = covariance_type
      self.df_resid = model.df_resid
      self.df_model = model.df_model
  
      super().__init__(
          model, params, scale=1.0, normalized_cov_params=cov_params
      )
  
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 1 to create a new PHRegResults object from inherited base classes: base with example: obj = PHRegResults(model, params, cov_params, scale, covariance_type)