class_docstr: Inherits time series methods from TSM class.
  
  **** GENERALIZED AUTOREGRESSIVE SCORE LOCAL LINEAR TREND MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the univariate 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.
  
  dist : str
      Which distribution to use
docstr: Plots the fit of the model
  
  Returns
  ----------
  None (plots data and the fit)
docstr_tok: ["Plots","the","fit","of","the","model","Returns","--","--","--","--","--","None","(","plots","data","and","the","fit",")"]
code: def plot_fit(self, intervals=False, **kwargs):
      """Plots the fit of the model
  
      Returns
      ----------
      None (plots data and the fit)
      """
      import matplotlib.pyplot as plt
      import seaborn as sns
  
      figsize = kwargs.get("figsize", (10, 7))
  
      if self.latent_variables.estimated is False:
          raise Exception("No latent variables estimated!")
      else:
          date_index = self.index[1:]
          mu, mu_t, Y, scores = self._model(
              self.latent_variables.get_z_values()
          )
  
          plt.figure(figsize=figsize)
          plt.subplot(3, 1, 1)
          plt.title("Model fit for " + self.data_name)
  
          # Catch specific family properties (imply different link functions/moments)
          if self.model_name2 == "Exponential":
              values_to_plot = 1.0 / self.link(mu)
          elif self.model_name2 == "Skewt":
              t_params = self.transform_z()
              (
                  model_scale,
                  model_shape,
                  model_skewness,
              ) = self._get_scale_and_shape(t_params)
              m1 = (
                  np.sqrt(model_shape)
                  * sp.gamma((model_shape - 1.0) / 2.0)
              ) / (np.sqrt(np.pi) * sp.gamma(model_shape / 2.0))
              additional_loc = (
                  (model_skewness - (1.0 / model_skewness))
                  * model_scale
                  * m1
              )
              values_to_plot = mu + additional_loc
          else:
              values_to_plot = self.link(mu)
  
          plt.plot(date_index, Y, label="Data")
          plt.plot(
              date_index, values_to_plot, label="GAS Filter", c="black"
          )
          plt.legend(loc=2)
  
          plt.subplot(3, 1, 2)
          plt.title("Local Level for " + self.data_name)
          plt.plot(date_index, mu, label="GAS Filter", c="black")
          plt.legend(loc=2)
  
          plt.subplot(3, 1, 3)
          plt.title("Local Trend for " + self.data_name)
          plt.plot(date_index, mu_t, label="GAS Filter", c="black")
          plt.legend(loc=2)
  
          plt.show()
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  import scipy.special
  from ..None import families
  from ..None import tsm
  from ..None import data_check
  from .gas_core_recursions import gas_llt_recursion
label_desc: Use image node_id 18 for calling the GASLLT obj's underlying member method code with example usage: obj.plot_fit(intervals) without return types