class_docstr: Inherits time series methods from TSM parent class.
  
  **** skew BETA-t-EGARCH IN MEAN MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the univariate 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.
  
  target : str (pd.DataFrame) or int (np.array)
      Specifies which column name or array index to use. By default, first
      column/array will be selected as the dependent variable.
class_docstr_tok: ["Inherits","time","series","methods","from","TSM","parent","class",".","*","*","*","*","skew","BETA-t-EGARCH","IN","MEAN","MODELS","*","*","*","*","Parameters","--","--","--","--","--","data",":","pd.DataFrame","or","np.array","Field","to","specify","the","univariate","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",".","target",":","str","(","pd.DataFrame",")","or","int","(","np.array",")","Specifies","which","column","name","or","array","index","to","use",".","By","default",",","first","column\/array","will","be","selected","as","the","dependent","variable","."]
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, theta = 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
  import scipy.special
  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 SEGARCHM obj's underlying member method code with example usage: obj.plot_sample(nsims, plot_data) without return types