get_by_location_group_ensemble_fits_and_predictions: Calculate ensemble fits separately by location group
In reichlab/covidEnsembles: What the Package Does (One Line, Title Case)

get_by_location_group_ensemble_fits_and_predictions

R Documentation

Calculate ensemble fits separately by location group

Description

Calculate ensemble fits separately by location group

Usage

get_by_location_group_ensemble_fits_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"),
  quantile_groups = NULL,
  noncross = "constrain",
  backend = "quantmod",
  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,
  manual_eligibility_adjust,
  return_all = FALSE,
  return_eligibility = TRUE
)

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.
`backend`	back end used for optimization.
`do_q10_check`	if TRUE, do q10 check
`do_nondecreasing_quantile_check`	if TRUE, do nondecreasing quantile check
`return_all`	if TRUE, return all quantities; if FALSE, return only some useful summaries
`return_eligibility`	if TRUE, return model eligibility