class_docstr: Inherits time series methods from TSM class.
  
  **** BETA-t-EGARCH IN MEAN REGRESSION MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame
      Field to specify the time series data that will be used.
  
  p : int
      Field to specify how many GARCH terms the model will have.
  
  q : int
      Field to specify how many SCORE terms the model will have.
  
  formula : string
      patsy string describing the regression
class_docstr_tok: ["Inherits","time","series","methods","from","TSM","class",".","*","*","*","*","BETA-t-EGARCH","IN","MEAN","REGRESSION","MODELS","*","*","*","*","Parameters","--","--","--","--","--","data",":","pd.DataFrame","Field","to","specify","the","time","series","data","that","will","be","used",".","p",":","int","Field","to","specify","how","many","GARCH","terms","the","model","will","have",".","q",":","int","Field","to","specify","how","many","SCORE","terms","the","model","will","have",".","formula",":","string","patsy","string","describing","the","regression"]
docstr: Plots draws from the posterior predictive density against the data
  
  Parameters
  ----------
  nsims : int (default : 1000)
      How many draws from the posterior predictive distribution
  
  plot_data boolean
      Whether to plot the data or not
docstr_tok: ["Plots","draws","from","the","posterior","predictive","density","against","the","data","Parameters","--","--","--","--","--","nsims",":","int","(","default",":","1000",")","How","many","draws","from","the","posterior","predictive","distribution","plot_data","boolean","Whether","to","plot","the","data","or","not"]
code: def plot_sample(self, nsims=10, plot_data=True, **kwargs):
      """
      Plots draws from the posterior predictive density against the data
  
      Parameters
      ----------
      nsims : int (default : 1000)
          How many draws from the posterior predictive distribution
  
      plot_data boolean
          Whether to plot the data or not
      """
  
      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
  
          alpha = 1.0
          figsize = kwargs.get("figsize", (10, 7))
          plt.figure(figsize=figsize)
          date_index = self.index[max(self.p, self.q) :]
          sigma2, Y, scores, _ = self._model(
              self.latent_variables.get_z_values()
          )
          draws = self.sample(nsims).T
          plt.plot(
              date_index, draws, label="Posterior Draws", alpha=1.0
          )
          if plot_data is True:
              plt.plot(
                  date_index,
                  Y,
                  label="Data",
                  c="black",
                  alpha=0.5,
                  linestyle="",
                  marker="s",
              )
          plt.title(self.data_name)
          plt.show()
  
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 gas
  from ..None import data_check
label_desc: Use image node_id 19 for calling the EGARCHMReg obj's underlying member method code with example usage: obj.plot_sample(nsims, plot_data) without return types