| EBMApredict {EBMAforecast} | R Documentation |
EBMApredict
Description
Function allows users to create new predictions given an already estimated EBMA model This function produces predictions based on EBMA model weights and component model predictions.
Usage
EBMApredict(EBMAmodel, Predictions, Outcome = NULL, ...)
## S4 method for signature 'ForecastData'
EBMApredict(EBMAmodel, Predictions, Outcome = NULL, ...)
Arguments
EBMAmodel |
An estimated EBMA model object |
Predictions |
A matrix with a column for each component model's predictions. |
Outcome |
An optional vector containing the true values of the dependent variable for all observations in the test period. |
... |
Not implemented |
Value
Returns a data of class 'FDatFitLogit' or FDatFitNormal, a subclass of 'ForecastData', with the following slots:
predTest |
A matrix containing the predictions of all component models and the EBMA model for all observations in the test period. |
period |
The period, "calibration" or "test", for which the statistics were calculated. |
outcomeTest |
An optional vector containing the true values of the dependent variable for all observations in the test period. |
modelNames |
A character vector containing the names of all component models. If no model names are specified, names will be assigned automatically. |
modelWeights |
A vector containing the model weights assigned to each model. |
Author(s)
Michael D. Ward <michael.d.ward@duke.edu> and Jacob M. Montgomery <jacob.montgomery@wustl.edu> and Florian M. Hollenbach <florian.hollenbach@tamu.edu>
References
Montgomery, Jacob M., Florian M. Hollenbach and Michael D. Ward. (2015). Calibrating ensemble forecasting models with sparse data in the social sciences. International Journal of Forecasting. 31(3): 930-942.
Montgomery, Jacob M., Florian M. Hollenbach and Michael D. Ward. (2012). Improving Predictions Using Ensemble Bayesian Model Averaging. Political Analysis. 20: 271-291.