class_docstr: Inherits time series methods from TSM class.
  
  **** LOCAL LINEAR TREND MODEL ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the time series data that will be used.
  
  integ : int (default : 0)
      Specifies how many time to difference the time series.
  
  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","class",".","*","*","*","*","LOCAL","LINEAR","TREND","MODEL","*","*","*","*","Parameters","--","--","--","--","--","data",":","pd.DataFrame","or","np.array","Field","to","specify","the","time","series","data","that","will","be","used",".","integ",":","int","(","default",":","0",")","Specifies","how","many","time","to","difference","the","time","series",".","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
  
          figsize = kwargs.get("figsize", (10, 7))
          plt.figure(figsize=figsize)
          date_index = self.index
          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,
                  self.data,
                  label="Data",
                  c="black",
                  alpha=0.5,
                  linestyle="",
                  marker="s",
              )
          plt.title(self.data_name)
          plt.show()
  
imports: import copy
  import sys
  import numpy
  import pandas
  import scipy.stats
  from ..None import families
  from ..None import tsm
  from ..None import data_check
  from .kalman import *
label_desc: Use image node_id 16 for calling the LLT obj's underlying member method code with example usage: obj.plot_sample(nsims, plot_data) without return types