Combine several nowcasts into an ensemble

Takes the nowcasts produced by different modelling packages on the same tbl_now and combines them into a single tbl_nowcast. Ensembles are routinely better calibrated than any of their members, and because run_nowcast() puts every backend on the same tidy footing, combining them needs no reshaping on your side.

Usage

nowcast_ensemble(
  ...,
  type = c("quantile", "linear_pool"),
  weights = "equal",
  backtest = NULL,
  quantile_levels = NULL,
  n_draws = 4000L,
  name = "ensemble",
  verbose = TRUE
)

Arguments

...

tbl_nowcast objects, or a single list of them. Named arguments rename the members.

type

How to combine the members:

"quantile" (default)

Average the members' predictive quantiles level by level (Vincentization). Applicable to every backend.

"linear_pool"

Pool the members' posterior draws into a mixture distribution and re-summarise it. Requires that every member returned draws, and generally yields wider intervals.

weights

Either the string "equal" (default), a numeric vector (named by method, or in the order the members were given), or one of "inverse_score" / "optim". The last two require backtest and are passed to nowcast_weights().

backtest

A nowcast_backtest() object, required when weights is "inverse_score" or "optim".

quantile_levels

Quantile levels to report the ensemble at. Defaults to the levels shared by all members.

n_draws

Number of draws in the pooled sample when type = "linear_pool". Default 4000.

name

Name to record as the ensemble's method. Default "ensemble".

verbose

Logical. Whether to report the weights that were used.

Value

A tbl_nowcast whose fit property is the list of member nowcasts and whose metadata holds the weights and the combination type.

See also

run_nowcast() to produce the nowcasts being combined; nowcast_backtest() and nowcast_weights() to decide how much to trust each one, instead of weighting them equally; score_nowcast() to check the ensemble beats its members. The One call, many models article builds one end to end.

Examples

toy <- function(method, shift) {
  predictions <- data.frame(
    onset_week = as.Date("2020-01-05"),
    .quantile_level = c(0.25, 0.5, 0.75),
    .value = c(8, 10, 13) + shift
  )
  tbl_nowcast(predictions = predictions, method = method, event_date = "onset_week")
}

nowcast_ensemble(toy("a", 0), toy("b", 4), verbose = FALSE)
#> ── A <tbl_nowcast> from method "ensemble" ──────────────────────────────────────
#> • now: 
#> • event dates: 1
#> • quantile levels: 0.25, 0.5, and 0.75
#> • draws: none (quantiles only)
#> 
#> Nowcast at "2020-01-05" (q50, 25-75% interval):
#> • 12 [10, 15]
#> 
#> # A tibble: 3 × 3
#>   onset_week .quantile_level .value
#>   <date>               <dbl>  <dbl>
#> 1 2020-01-05            0.25     10
#> 2 2020-01-05            0.5      12
#> 3 2020-01-05            0.75     15

# Unequal weights
nowcast_ensemble(toy("a", 0), toy("b", 4), weights = c(a = 0.75, b = 0.25), verbose = FALSE)
#> ── A <tbl_nowcast> from method "ensemble" ──────────────────────────────────────
#> • now: 
#> • event dates: 1
#> • quantile levels: 0.25, 0.5, and 0.75
#> • draws: none (quantiles only)
#> 
#> Nowcast at "2020-01-05" (q50, 25-75% interval):
#> • 11 [9, 14]
#> 
#> # A tibble: 3 × 3
#>   onset_week .quantile_level .value
#>   <date>               <dbl>  <dbl>
#> 1 2020-01-05            0.25      9
#> 2 2020-01-05            0.5      11
#> 3 2020-01-05            0.75     14