class_docstr: Inherits time series methods from TSM class.
  
  **** GENERALIZED AUTOREGRESSIVE SCORE E(X)OGENOUS VARIABLE (GASX) MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the time series data that will be used.
  
  formula : string
      patsy string describing the regression
  
  ar : int
      Field to specify how many AR lags the model will have.
  
  sc : int
      Field to specify how many score lags terms the model will have.
  
  integ : int (default : 0)
      Specifies how many time to difference the time series.
  
  dist : str
      Which distribution to use
docstr: Computes posterior predictive p-value
  
  Parameters
  ----------
  nsims : int (default : 1000)
      How many draws for the PPC
  
  T : function
      A discrepancy measure - e.g. np.mean, np.std, np.max
  
  Returns
  ----------
  - float (posterior predictive p-value)
docstr_tok: ["Computes","posterior","predictive","p-value","Parameters","--","--","--","--","--","nsims",":","int","(","default",":","1000",")","How","many","draws","for","the","PPC","T",":","function","A","discrepancy","measure","-","e.g",".","np.mean",",","np.std",",","np.max","Returns","--","--","--","--","--","-","float","(","posterior","predictive","p-value",")"]
code: def ppc(self, nsims=1000, T=np.mean):
      """Computes posterior predictive p-value
  
      Parameters
      ----------
      nsims : int (default : 1000)
          How many draws for the PPC
  
      T : function
          A discrepancy measure - e.g. np.mean, np.std, np.max
  
      Returns
      ----------
      - float (posterior predictive p-value)
      """
      if self.latent_variables.estimation_method not in ["BBVI", "M-H"]:
          raise Exception("No latent variables estimated!")
      else:
          lv_draws = self.draw_latent_variables(nsims=nsims)
          mus = [self._model(lv_draws[:, i])[0] for i in range(nsims)]
          (
              model_scale,
              model_shape,
              model_skewness,
          ) = self._get_scale_and_shape_sim(lv_draws)
          data_draws = np.array(
              [
                  self.family.draw_variable(
                      self.link(mus[i]),
                      np.repeat(model_scale[i], mus[i].shape[0]),
                      np.repeat(model_shape[i], mus[i].shape[0]),
                      np.repeat(model_skewness[i], mus[i].shape[0]),
                      mus[i].shape[0],
                  )
                  for i in range(nsims)
              ]
          )
          T_sims = T(self.sample(nsims=nsims), axis=1)
          T_actual = T(self.data)
          return len(T_sims[T_sims > T_actual]) / nsims
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  import scipy.special
  from patsy import dmatrices, dmatrix, demo_data
  from ..None import families
  from ..None import tsm
  from ..None import data_check
  from .gas_core_recursions import gasx_recursion
label_desc: Use image node_id 25 for calling the GASX obj's underlying member method code with example usage: obj.ppc(nsims, T) and returns: unknown