GLMResults.get_prediction(exog=None, exposure=None, offset=None, transform=True, linear=False, row_labels=None)[source]

compute prediction results

  • exog (array-like, optional) – The values for which you want to predict.
  • transform (bool, optional) – If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a data structure that contains x1 and x2 in their original form. Otherwise, you’d need to log the data first.
  • weights (array_like, optional) – Weights interpreted as in WLS, used for the variance of the predicted residual.
  • kwargs (args,) – Some models can take additional arguments or keywords, see the predict method of the model for the details.

prediction_results – The prediction results instance contains prediction and prediction variance and can on demand calculate confidence intervals and summary tables for the prediction of the mean and of new observations.

Return type: