Description Usage Arguments Value
Fit a stacking model that assigns weights to component models The weights are a function of observed covariates (which?), and are obtained via gradient tree boosting
1 2 3 4 5 6 7 8 9 | fit_stacked_model(prediction_target, component_model_names,
explanatory_variables = c("analysis_time_season_week",
"kcde_model_confidence", "sarima_model_confidence", "weighted_ili"),
loso_preds_path, seasons_to_leave_out, booster = "gbtree",
subsample = 1, colsample_bytree = 1, colsample_bylevel = 1,
max_depth = 10, min_child_weight = -10^10, eta = 0.3, gamma = 0,
lambda = 0, alpha = 0, nrounds = 10, cv_params = NULL,
cv_folds = NULL, cv_nfolds = 10L, cv_refit = "ttest",
update = NULL, nthread = NULL, verbose = 0)
|
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" |
loso_preds_path |
path to directory with leave-one-season-out predictions from each component model. Predictions should be saved in files named like "kde-National-loso-predictions.rds" |
seasons_to_leave_out |
optional character vector of seasons to leave out of stacking estimation |
booster |
what form of boosting to use? see xgboost documentation |
subsample |
fraction of data to use in bagging. not supported yet. |
colsample_bytree |
fraction of explanatory variables to randomly select in growing each regression tree. see xgboost documentation |
colsample_bylevel |
fraction of explanatory variables to randomly select in growing each level of the regression tree. see xgboost documentation |
max_depth |
maximum depth of regression trees. see xgboost documentation |
min_child_weight |
not recommended for use. see xgboost documentation |
eta |
learning rate. see xgboost documentation |
gamma |
Penalty on number of regression tree leafs. see xgboost documentation |
lambda |
L2 regularization of contribution to model weights in each round. see xgboost documentation |
alpha |
L1 regularization of contribution to model weights in each round. see xgboost documentation |
nrounds |
see xgboost documentation |
cv_params |
optional named list of parameter values to evaluate loss via cross-validation. Each component is a vector of parameter values with name one of "booster", "subsample", "colsample_bytree", "colsample_bylevel", "max_depth", "min_child_weight", "eta", "gamma", "lambda", "alpha", "nrounds" |
cv_folds |
list specifying observation groups to use in cross-validation each list component is a numeric vector of observation indices. |
cv_nfolds |
integer specifying the number of cross-validation folds to use. if cv_folds was provided, cv_nfolds is ignored. if cv_folds was not provided, the data will be randomly partitioned into cv_nfolds groups |
cv_refit |
character describing which of the models specified by the values in cv_params to refit using the full data set. Either "best", "ttest", or "none". |
update |
an object of class xgbstack to update |
nthread |
how many threads to use. see xgboost documentation |
verbose |
how much output to generate along the way. 0 for no logging, 1 for some logging |
a model stacking fit
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