class_docstr: Inherits time series methods from TSM parent class.
  
  **** AUTOREGRESSIVE INTEGRATED MOVING AVERAGE EXOGENOUS (ARIMAX) MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame
      Field to specify the univariate time series data that will be used.
  
  formula : string
      patsy string notation describing the regression
  
  ar : int
      Field to specify how many AR lags the model will have.
  
  ma : int
      Field to specify how many MA lags the model will have.
  
  integ : int (default : 0)
      Specifies how many times to difference the time series.
  
  family : family object
      E.g pf.Normal(), pf.t(), pf.Laplace()...
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docstr: Plots histogram of the discrepancy from draws of the posterior
  
  Parameters
  ----------
  nsims : int (default : 1000)
      How many draws for the PPC
  
  T : function
      A discrepancy measure - e.g. np.mean, np.std, np.max
docstr_tok: ["Plots","histogram","of","the","discrepancy","from","draws","of","the","posterior","Parameters","--","--","--","--","--","nsims",":","int","(","default",":","1000",")","How","many","draws","for","the","PPC","T",":","function","A","discrepancy","measure","-","e.g",".","np.mean",",","np.std",",","np.max"]
code: def plot_ppc(self, nsims=1000, T=np.mean, **kwargs):
      """Plots histogram of the discrepancy from draws of the posterior
  
      Parameters
      ----------
      nsims : int (default : 1000)
          How many draws for the PPC
  
      T : function
          A discrepancy measure - e.g. np.mean, np.std, np.max
      """
      if self.latent_variables.estimation_method not in ["BBVI", "M-H"]:
          raise Exception("No latent variables estimated!")
      else:
          import matplotlib.pyplot as plt
          import seaborn as sns
  
          figsize = kwargs.get("figsize", (10, 7))
  
          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_sim = T(self.sample(nsims=nsims), axis=1)
          T_actual = T(self.data)
  
          if T == np.mean:
              description = " of the mean"
          elif T == np.max:
              description = " of the maximum"
          elif T == np.min:
              description = " of the minimum"
          elif T == np.median:
              description = " of the median"
          else:
              description = ""
  
          plt.figure(figsize=figsize)
          ax = plt.subplot()
          ax.axvline(T_actual)
          sns.distplot(T_sim, kde=False, ax=ax)
          ax.set(
              title="Posterior predictive" + description,
              xlabel="T(x)",
              ylabel="Frequency",
          )
          plt.show()
  
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 output
  from ..None import tests
  from ..None import tsm
  from ..None import data_check
  from .arma_recursions import arimax_recursion
label_desc: Use image node_id 26 for calling the ARIMAX obj's underlying member method code with example usage: obj.plot_ppc(nsims, T) without return types