get_ensemble_fit_and_predictions: Calculate ensemble fits for a single window size
In reichlab/covidEnsembles: What the Package Does (One Line, Title Case)

get_ensemble_fit_and_predictions

R Documentation

Calculate ensemble fits for a single window size

Description

Calculate ensemble fits for a single window size

Usage

get_ensemble_fit_and_predictions(
  forecasts,
  observed_by_location_target_end_date,
  forecast_week_end_date,
  window_size,
  intercept = FALSE,
  combine_method = c("ew", "convex", "positive", "unconstrained", "median",
    "convex_median", "rel_wis_weighted_median", "rel_wis_weighted_mean",
    "mean_weights_weighted_median"),
  quantile_groups = NULL,
  noncross = "constrain",
  missingness = c("by_location_group", "rescale", "mean_impute"),
  impute_method = "mean",
  backend = "quantmod",
  check_missingness_by_target = FALSE,
  do_q10_check,
  do_nondecreasing_quantile_check,
  do_baseline_check,
  do_sd_check,
  sd_check_table_path = NULL,
  sd_check_plot_path = NULL,
  baseline_tol = 1.2,
  max_weight = NULL,
  top_models = 0,
  manual_eligibility_adjust,
  return_eligibility = TRUE,
  return_all = FALSE,
  partial_save_frequency,
  partial_save_filename
)

Arguments

`forecasts`	data frame with columns 'model', 'location', 'forecast_week_end_date', 'target', 'quantile', and 'value'
`observed_by_location_target_end_date`	data frame with columns 'location', 'base_target', 'target_end_date', and 'observed'
`forecast_week_end_date`	Date object: date of the saturday for the end of the forecast week; week-ahead targets are with respect to this date
`window_size`	size of window
`intercept`	logical specifying whether an intercept is included
`combine_method`	character specifying the approach to model combination: "equal", "convex", "positive", "unconstrained", "median", or "convex_median". The first four form a linear combination of quantiles across component models with varying levels of restrictions on the combination coefficients. "median" takes the median across models at each quantile level, and "convex_median" uses a weighted median with convext constraints on weights
`quantile_groups`	Vector of group labels for quantiles, having the same length as the number of quantiles. Common labels indicate that the ensemble weights for the corresponding quantile levels should be tied together. Default is rep(1,length(quantiles)), which means that a common set of ensemble weights should be used across all levels. This is the argument `tau_groups` for `quantmod::quantile_ensemble`, and may only be supplied if `⁠backend = 'quantmod⁠`
`noncross`	string specifying approach to handling quantile noncrossing: one of "constrain" or "sort". "constrain" means estimation is done subject to constraints ruling out quantile crossing. "sort" means no such constraints are imposed during estimation, but the resulting forecasts are sorted.
`missingness`	character specifying approach to handling missing forecasts: 'by_location_group', 'rescale', and 'impute'
`impute_method`	character string specifying method for imputing missing forecasts; either 'mean' for mean imputation or 'none' for no imputation
`backend`	back end used for optimization.
`check_missingness_by_target`	if TRUE, record missingness for every combination of model, location, forecast week, and target; if FALSE, record missingness only for each model and location
`do_q10_check`	if TRUE, do q10 check
`do_nondecreasing_quantile_check`	if TRUE, do nondecreasing quantile check
`max_weight`	numeric value for maximum weight. Ignored unless qra_model is rel_wis_weighted_median or rel_wis_weighted mean and backend is grid_search
`manual_eligibility_adjust`	character vector of model abbreviations for models eliminated based on visual inspection
`return_eligibility`	if TRUE, return model eligibility
`return_all`	if TRUE, return model fits