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: 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
  
          figsize = kwargs.get("figsize", (10, 7))
          plt.figure(figsize=figsize)
          date_index = self.index[self.ar : self.data_length]
          mu, Y = 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 data_check
  from .nn_architecture import neural_network_tanh, neural_network_tanh_mb
label_desc: Use image node_id 21 for calling the NNARX obj's underlying member method code with example usage: obj.plot_sample(nsims, plot_data) without return types