class_docstr: Class to hold results from fitting a recursive least squares model.
  
  Parameters
  ----------
  model : RecursiveLS instance
      The fitted model instance
  
  Attributes
  ----------
  specification : dictionary
      Dictionary including all attributes from the recursive least squares
      model instance.
  
  See Also
  --------
  statsmodels.tsa.statespace.kalman_filter.FilterResults
  statsmodels.tsa.statespace.mlemodel.MLEResults
class_docstr_tok: ["Class","to","hold","results","from","fitting","a","recursive","least","squares","model",".","Parameters","--","--","--","--","--","model",":","RecursiveLS","instance","The","fitted","model","instance","Attributes","--","--","--","--","--","specification",":","dictionary","Dictionary","including","all","attributes","from","the","recursive","least","squares","model","instance",".","See","Also","--","--","--","--","statsmodels.tsa.statespace.kalman_filter.FilterResults","statsmodels.tsa.statespace.mlemodel.MLEResults"]
docstr: rsquared
docstr_tok: ["rsquared"]
code: def rsquared(self):
      """rsquared"""
      if self.k_constant:
          return 1 - self.ssr / self.centered_tss
      else:
          return 1 - self.ssr / self.uncentered_tss
  
imports: import numpy
  import pandas
  from statsmodels.compat.pandas import Appender
  from statsmodels.tools.data import _is_using_pandas
  from statsmodels.tsa.statespace.mlemodel import MLEModel, MLEResults, MLEResultsWrapper, PredictionResults, PredictionResultsWrapper
  from statsmodels.tsa.statespace.tools import concat
  from statsmodels.tools.tools import Bunch
  from statsmodels.tools.decorators import cache_readonly
  import statsmodels.base.wrapper
label_desc: Use image node_id 12 for calling the RecursiveLSResults obj's underlying member method code with example usage: obj.rsquared() and returns: unknown, unknown