fit_stacked_model: Fit a stacking model that assigns weights to component models...
In reichlab/2017-2018-cdc-flu-contest: code for cdc flu contest 2017-2018

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

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