class_docstr: Results class for Gaussian process regression models.
code: def covariance_group(self, group):
      # Check if the group exists, since _groups_ix is a
      # DefaultDict use len instead of catching a KeyError.
      ix = self.model._groups_ix[group]
      if len(ix) == 0:
          msg = "Group '%s' does not exist" % str(group)
          raise ValueError(msg)
  
      scale_data = self.model.exog_scale[ix, :]
      smooth_data = self.model.exog_smooth[ix, :]
  
      _, scale_names, smooth_names, _ = self.model._split_param_names()
  
      scale_data = pd.DataFrame(scale_data, columns=scale_names)
      smooth_data = pd.DataFrame(smooth_data, columns=smooth_names)
      time = self.model.time[ix]
  
      return self.model.covariance(
          time,
          self.scale_params,
          self.smooth_params,
          scale_data,
          smooth_data,
      )
  
imports: import numpy
  import pandas
  import patsy
  import statsmodels.base.model
  from statsmodels.regression.linear_model import OLS
  import collections
  from scipy.optimize import minimize
  from statsmodels.iolib import summary2
  from statsmodels.tools.numdiff import approx_fprime
  import warnings
label_desc: Use image node_id 4 for calling the ProcessMLEResults obj's underlying member method code with example usage: obj.covariance_group(group) and returns: self