statsmodels.regression.linear_model.RegressionResults.get_prediction#
- RegressionResults.get_prediction(exog=None, transform=True, weights=None, row_labels=None, **kwargs)[source]#
Compute prediction results
- Parameters:
- exogarray_like,
optional The values for which you want to predict. If the model was not fit using a formula, the columns are matched by position and not by name, so a DataFrame must have its columns in the same order as the exog used to fit the model.
- transformbool,
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
Noneor array_like,optional Weights interpreted as in WLS, used for the variance of the predicted residual.
- row_labels
Noneor array_like,optional A list of row labels to use. If not provided, read exog is available.
- pred_kwds
dict,optional Some models can take additional keyword arguments, see the predict method of the model for details.
- exogarray_like,
- Returns:
linear_model.PredictionResultsThe 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.