class_docstr: Inherits time series methods from TSM class.
  
  **** GENERALIZED AUTOREGRESSIVE SCORE (GAS) REGRESSION MODELS ****
  
  Parameters
  ----------
  
  formula : string
      patsy string describing the regression
  
  data : pd.DataFrame or np.array
      Field to specify the data that will be used
  
  family : GAS family object
      Which distribution to use, e.g. GASNormal()
class_docstr_tok: ["Inherits","time","series","methods","from","TSM","class",".","*","*","*","*","GENERALIZED","AUTOREGRESSIVE","SCORE","(","GAS",")","REGRESSION","MODELS","*","*","*","*","Parameters","--","--","--","--","--","formula",":","string","patsy","string","describing","the","regression","data",":","pd.DataFrame","or","np.array","Field","to","specify","the","data","that","will","be","used","family",":","GAS","family","object","Which","distribution","to","use",",","e.g",".","GASNormal","(",")"]
docstr: Makes dynamic in-sample predictions with the estimated model
  
  Parameters
  ----------
  h : int (default : 5)
      How many steps would you like to forecast?
  
  fit_once : boolean
      (default: True) Fits only once before the in-sample prediction; if False, fits after every new datapoint
  
  fit_method : string
      Which method to fit the model with
  
  intervals : boolean (default: False)
      Whether to return prediction intervals
  
  Returns
  ----------
  - pd.DataFrame with predicted values
docstr_tok: ["Makes","dynamic","in-sample","predictions","with","the","estimated","model","Parameters","--","--","--","--","--","h",":","int","(","default",":","5",")","How","many","steps","would","you","like","to","forecast","?","fit_once",":","boolean","(","default",":","True",")","Fits","only","once","before","the","in-sample","prediction",";","if","False",",","fits","after","every","new","datapoint","fit_method",":","string","Which","method","to","fit","the","model","with","intervals",":","boolean","(","default",":","False",")","Whether","to","return","prediction","intervals","Returns","--","--","--","--","--","-","pd.DataFrame","with","predicted","values"]
code: def predict_is(
      self, h=5, fit_once=True, fit_method="MLE", intervals=False
  ):
      """Makes dynamic in-sample predictions with the estimated model
  
      Parameters
      ----------
      h : int (default : 5)
          How many steps would you like to forecast?
  
      fit_once : boolean
          (default: True) Fits only once before the in-sample prediction; if False, fits after every new datapoint
  
      fit_method : string
          Which method to fit the model with
  
      intervals : boolean (default: False)
          Whether to return prediction intervals
  
      Returns
      ----------
      - pd.DataFrame with predicted values
      """
  
      predictions = []
  
      for t in range(0, h):
          data1 = self.data_original.iloc[: -h + t, :]
          data2 = self.data_original.iloc[-h + t :, :]
          x = GASReg(
              formula=self.formula,
              data=self.data_original[: (-h + t)],
              family=self.family,
          )
  
          if fit_once is False:
              x.fit(method=fit_method, printer=False)
          if t == 0:
              if fit_once is True:
                  x.fit(method=fit_method, printer=False)
                  saved_lvs = x.latent_variables
              predictions = x.predict(
                  h=1, oos_data=data2, intervals=intervals
              )
          else:
              if fit_once is True:
                  x.latent_variables = saved_lvs
              predictions = pd.concat(
                  [
                      predictions,
                      x.predict(
                          h=1, oos_data=data2, intervals=intervals
                      ),
                  ]
              )
  
      predictions.rename(columns={0: self.y_name}, inplace=True)
      predictions.index = self.index[-h:]
  
      return predictions
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  import scipy.special
  from patsy import dmatrices, dmatrix, demo_data
  from ..None import families
  from ..None import tsm
  from ..None import data_check
  from .gas_core_recursions import gas_reg_recursion
label_desc: Use image node_id 17 for calling the GASReg obj's underlying member method code with example usage: obj.predict_is(h, fit_once, fit_method, intervals) and returns: predictions