class_docstr: Class to hold results from fitting an DynamicFactor model.
  
  Parameters
  ----------
  model : DynamicFactor instance
      The fitted model instance
  
  Attributes
  ----------
  specification : dictionary
      Dictionary including all attributes from the DynamicFactor model
      instance.
  coefficient_matrices_var : ndarray
      Array containing autoregressive lag polynomial coefficient matrices,
      ordered from lowest degree to highest.
  
  See Also
  --------
  statsmodels.tsa.statespace.kalman_filter.FilterResults
  statsmodels.tsa.statespace.mlemodel.MLEResults
class_docstr_tok: ["Class","to","hold","results","from","fitting","an","DynamicFactor","model",".","Parameters","--","--","--","--","--","model",":","DynamicFactor","instance","The","fitted","model","instance","Attributes","--","--","--","--","--","specification",":","dictionary","Dictionary","including","all","attributes","from","the","DynamicFactor","model","instance",".","coefficient_matrices_var",":","ndarray","Array","containing","autoregressive","lag","polynomial","coefficient","matrices",",","ordered","from","lowest","degree","to","highest",".","See","Also","--","--","--","--","statsmodels.tsa.statespace.kalman_filter.FilterResults","statsmodels.tsa.statespace.mlemodel.MLEResults"]
docstr: Coefficients of determination (:math:`R^2`) from regressions of
  individual estimated factors on endogenous variables.
  
  Returns
  -------
  coefficients_of_determination : ndarray
      A `k_endog` x `k_factors` array, where
      `coefficients_of_determination[i, j]` represents the :math:`R^2`
      value from a regression of factor `j` and a constant on endogenous
      variable `i`.
  
  Notes
  -----
  Although it can be difficult to interpret the estimated factor loadings
  and factors, it is often helpful to use the coefficients of
  determination from univariate regressions to assess the importance of
  each factor in explaining the variation in each endogenous variable.
  
  In models with many variables and factors, this can sometimes lend
  interpretation to the factors (for example sometimes one factor will
  load primarily on real variables and another on nominal variables).
  
  See Also
  --------
  plot_coefficients_of_determination
code: def coefficients_of_determination(self):
      """
      Coefficients of determination (:math:`R^2`) from regressions of
      individual estimated factors on endogenous variables.
  
      Returns
      -------
      coefficients_of_determination : ndarray
          A `k_endog` x `k_factors` array, where
          `coefficients_of_determination[i, j]` represents the :math:`R^2`
          value from a regression of factor `j` and a constant on endogenous
          variable `i`.
  
      Notes
      -----
      Although it can be difficult to interpret the estimated factor loadings
      and factors, it is often helpful to use the coefficients of
      determination from univariate regressions to assess the importance of
      each factor in explaining the variation in each endogenous variable.
  
      In models with many variables and factors, this can sometimes lend
      interpretation to the factors (for example sometimes one factor will
      load primarily on real variables and another on nominal variables).
  
      See Also
      --------
      plot_coefficients_of_determination
      """
      from statsmodels.tools import add_constant
  
      spec = self.specification
      coefficients = np.zeros((spec.k_endog, spec.k_factors))
      which = "filtered" if self.smoothed_state is None else "smoothed"
  
      for i in range(spec.k_factors):
          exog = add_constant(self.factors[which][i])
          for j in range(spec.k_endog):
              endog = self.filter_results.endog[j]
              coefficients[j, i] = OLS(endog, exog).fit().rsquared
  
      return coefficients
  
imports: import numpy
  from .mlemodel import MLEModel, MLEResults, MLEResultsWrapper
  from .tools import is_invertible, prepare_exog, constrain_stationary_univariate, unconstrain_stationary_univariate, constrain_stationary_multivariate, unconstrain_stationary_multivariate
  from statsmodels.multivariate.pca import PCA
  from statsmodels.regression.linear_model import OLS
  from statsmodels.tsa.vector_ar.var_model import VAR
  from statsmodels.tsa.arima.model import ARIMA
  from statsmodels.tools.tools import Bunch
  from statsmodels.tools.data import _is_using_pandas
  from statsmodels.tsa.tsatools import lagmat
  from statsmodels.tools.decorators import cache_readonly
  import statsmodels.base.wrapper
  from statsmodels.compat.pandas import Appender
label_desc: Use image node_id 3 for calling the DynamicFactorResults obj's underlying member method code with example usage: obj.coefficients_of_determination() and returns: coefficients