Get a draw of only the predicted elements of the nowcast vector

Get a draw of only the predicted elements of the nowcast vector

Usage

sample_prediction(
  point_nowcast_matrix,
  reporting_triangle,
  uncertainty_params,
  uncertainty_sampler = sample_nb,
  ref_time_aggregator = identity,
  delay_aggregator = function(x) rowSums(x, na.rm = TRUE)
)

Arguments

point_nowcast_matrix

Matrix of point nowcast predictions and observations, with rows representing the reference times and columns representing the delays.

reporting_triangle

A reporting_triangle object with rows representing reference times and columns representing delays. Can be a reporting matrix or incomplete reporting matrix. Can also be a ragged reporting triangle, where multiple columns are reported for the same row (e.g., weekly reporting of daily data).

uncertainty_params

Vector of uncertainty parameters ordered from horizon 1 to the maximum horizon. Note that these will be reversed internally to match the ordering of the point_nowcast_matrix (where a horizon of 1 is the last entry).

uncertainty_sampler

Function that ingests a vector or matrix of predictions and a vector of uncertainty parameters and generates draws from the observation model. Default is sample_nb which expects arguments pred for the vector of predictions and uncertainty parameters for the corresponding vector of uncertainty parameters, and draws from a negative binomial for each element of the vector.

ref_time_aggregator

Function that operates along the rows (reference times) of the retrospective point nowcast matrix before it has been aggregated across columns (delays). Default is identity which does not aggregate across reference times.

delay_aggregator

Function that operates along the columns (delays) of the retrospective point nowcast matrix after it has been aggregated across reference times. Default is function(x) rowSums(x, na.rm = TRUE).

Value

Matrix of predicted draws at each reference date, for all reference dates in the input point_nowcast_matrix (or fewer if using ref_time_aggregator).

See also

Probabilistic nowcast generation functions combine_obs_with_pred(), sample_nb(), sample_nowcast(), sample_nowcasts(), sample_predictions()

Examples

# Generate point nowcast and uncertainty params from example data
data_as_of <- syn_nssp_df[syn_nssp_df$report_date <= "2026-04-01", ]
rep_tri <- as_reporting_triangle(data_as_of) |>
  truncate_to_delay(max_delay = 5) |>
  tail(n = 10)
#> ℹ Using max_delay = 154 from data
#> ℹ Truncating from max_delay = 154 to 5.
point_nowcast_matrix <- estimate_and_apply_delay(rep_tri, n = 10)
reporting_triangle <- construct_triangle(rep_tri)
uncertainty_params <- estimate_uncertainty_retro(
  rep_tri,
  n_history_delay = 8,
  n_retrospective_nowcasts = 2
)
nowcast_pred_draw <- sample_prediction(
  point_nowcast_matrix,
  reporting_triangle,
  uncertainty_params
)
nowcast_pred_draw
#>            [,1]
#>               0
#>               0
#>               0
#>               0
#>               0
#> 2026-03-28   17
#> 2026-03-29   49
#> 2026-03-30  150
#> 2026-03-31  144
#> 2026-04-01  157

# Get draws on the rolling sum
if (requireNamespace("zoo", quietly = TRUE)) {
  nowcast_pred_draw_agg <- sample_prediction(
    point_nowcast_matrix,
    reporting_triangle,
    uncertainty_params,
    ref_time_aggregator = function(x) zoo::rollsum(x, k = 2, align = "right")
  )
  nowcast_pred_draw_agg
}
#>       [,1]
#>  [1,]    0
#>  [2,]    0
#>  [3,]    0
#>  [4,]    0
#>  [5,]   19
#>  [6,]  239
#>  [7,]  207
#>  [8,]  231
#>  [9,]  543