Description Usage Arguments Value
Assemble a data frame of inputs to stacking
1 2 3 | assemble_stacking_inputs(regions, seasons = paste0(2010:2016, "-",
2011:2017), prediction_target, component_model_names,
explanatory_variables, include_model_performance = FALSE, preds_path)
|
regions |
string with region: either "National" or in the format "Regionk" for k in 1, ..., 10 |
seasons |
character vector specifying seasons for which to get predictions, "2011/2012" or "2011-2012" or "*" for all seasons |
prediction_target |
string with either "onset", "peak_week", "peak_inc", "ph1_inc", ..., "ph4_inc" |
component_model_names |
character vector with names of component models |
explanatory_variables |
character vector with names of explanatory variables to include for weights; a non-empty subset of "analysis_time_season_week", "kcde_model_confidence", "sarima_model_confidence", "weighted_ili" |
include_model_performance |
boolean; should measures of model performance be included in the return result? Generally, should be TRUE if we're getting inputs for model training and FALSE if we're just getting covariates to calculate model weights. |
preds_path |
path to directory with leave-one-season-out or test phase predictions from each component model. Predictions should be saved in files named like "kde-National-loso-predictions.rds" |
a data frame with covariates that model weights depend on (as specified by explanatory_variables) and possibly log scores from each component model
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