get_imputed_ensemble_fits_and_predictions: Calculate ensemble fits after imputing missing forecasts
In reichlab/covidEnsembles: What the Package Does (One Line, Title Case)

get_imputed_ensemble_fits_and_predictions

R Documentation

Calculate ensemble fits after imputing missing forecasts

Description

Calculate ensemble fits after imputing missing forecasts

Usage

get_imputed_ensemble_fits_and_predictions(
  forecasts,
  observed_by_location_target_end_date,
  forecast_week_end_date,
  window_size,
  intercept = FALSE,
  combine_method = c("ew", "median", "convex", "positive", "unconstrained",
    "convex_median", "rel_wis_weighted_median", "rel_wis_weighted_mean"),
  quantile_groups = NULL,
  noncross = "constrain",
  impute_method = "mean",
  weight_transfer_per_group = FALSE,
  weight_transfer_group_factors = "location",
  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,
  top_models = 0,
  max_weight = NULL,
  manual_eligibility_adjust,
  return_all = FALSE,
  return_eligibility = TRUE,
  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.
`impute_method`	character string specifying method for imputing missing forecasts; either 'mean' for mean imputation or 'none' for no imputation
`weight_transfer_per_group`
`weight_transfer_group_factors`
`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
`do_baseline_check`	if TRUE, do baseline quantile check
`do_sd_check`	if TRUE, do sd quantile check (for hospitalization forecasts)
`sd_check_table_path`	where to save hospitalization sd check table results
`sd_check_plot_path`	where to save hospitalization sd check plot results
`max_weight`	numeric value for maximum weight. Ignored unless combine_method is rel_wis_weighted_median or rel_wis_weighted mean and backend is grid_search
`return_all`	if TRUE, return all quantities; if FALSE, return only some useful summaries
`return_eligibility`	if TRUE, return model eligibility