docstr: The overall log-density: log p(y, fe, vc, vcp).
  
  This differs by an additive constant from the log posterior
  log p(fe, vc, vcp | y).
docstr_tok: ["The","overall","log-density",":","log","p","(","y",",","fe",",","vc",",","vcp",")",".","This","differs","by","an","additive","constant","from","the","log","posterior","log","p","(","fe",",","vc",",","vcp","|","y",")","."]
code: def logposterior(self, params):
      """
      The overall log-density: log p(y, fe, vc, vcp).
  
      This differs by an additive constant from the log posterior
      log p(fe, vc, vcp | y).
      """
  
      fep, vcp, vc = self._unpack(params)
  
      # Contributions from p(y | x, vc)
      lp = 0
      if self.k_fep > 0:
          lp += np.dot(self.exog, fep)
      if self.k_vc > 0:
          lp += self.exog_vc.dot(vc)
  
      mu = self.family.link.inverse(lp)
      ll = self.family.loglike(self.endog, mu)
  
      if self.k_vc > 0:
          # Contributions from p(vc | vcp)
          vcp0 = vcp[self.ident]
          s = np.exp(vcp0)
          ll -= 0.5 * np.sum(vc**2 / s**2) + np.sum(vcp0)
  
          # Contributions from p(vc)
          ll -= 0.5 * np.sum(vcp**2 / self.vcp_p**2)
  
      # Contributions from p(fep)
      if self.k_fep > 0:
          ll -= 0.5 * np.sum(fep**2 / self.fe_p**2)
  
      return ll
  
imports: import numpy
  from scipy.optimize import minimize
  from scipy import sparse
  import statsmodels.base.model
  from statsmodels.iolib import summary2
  from statsmodels.genmod import families
  import pandas
  import warnings
  import patsy
label_desc: Use image node_id 3 for calling the _BayesMixedGLM obj's underlying member method code with example usage: obj.logposterior(params) and returns: ll