class_docstr: Inherits time series methods from TSM parent class.
  
  **** NEURAL NETWORK AUTOREGRESSIVE (NNAR) MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.ndarray
      Field to specify the univariate time series data that will be used.
  
  ar : int
      Field to specify how many AR lags the model will have.
  
  units : int
      How many units for the neural network
  
  layers : int
      How many layers for the neural networ
  
  activation : function
      E.g. np.tanh, np.array (linear)
  
  family: family object
      E.g. pf.Normal()
class_docstr_tok: ["Inherits","time","series","methods","from","TSM","parent","class",".","*","*","*","*","NEURAL","NETWORK","AUTOREGRESSIVE","(","NNAR",")","MODELS","*","*","*","*","Parameters","--","--","--","--","--","data",":","pd.DataFrame","or","np.ndarray","Field","to","specify","the","univariate","time","series","data","that","will","be","used",".","ar",":","int","Field","to","specify","how","many","AR","lags","the","model","will","have",".","units",":","int","How","many","units","for","the","neural","network","layers",":","int","How","many","layers","for","the","neural","networ","activation",":","function","E.g",".","np.tanh",",","np.array","(","linear",")","family",":","family","object","E.g",".","pf.Normal","(",")"]
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
  from patsy import dmatrices, dmatrix, demo_data
  from ..None import families
  from ..None import output
  from ..None import tests
  from ..None import tsm
  from ..None import data_check
  from .nn_architecture import neural_network_tanh, neural_network_tanh_mb
label_desc: Use image node_id 22 for calling the NNARX obj's underlying member method code with example usage: obj.ppc(nsims, T) and returns: unknown