Generate multiple draws of a nowcast combining observed and predicted values
Source:R/sample_predictions.R
sample_nowcasts.RdGenerate multiple draws of a nowcast combining observed and predicted values
Usage
sample_nowcasts(
point_nowcast_matrix,
reporting_triangle,
uncertainty_params,
draws = 1000,
...
)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).- draws
Integer indicating the number of draws of the predicted nowcast vector to generate. Default is
1000.- ...
Additional arguments passed to
sample_nowcast.
Value
Dataframe containing information for multiple draws with columns
for the reference date (reference_date), the predicted counts
(pred_count), and the draw number (draw). Returns predictions for all
reference dates in the input reporting_triangle (or fewer if using
ref_time_aggregator).
See also
Probabilistic nowcast generation functions
combine_obs_with_pred(),
sample_nb(),
sample_nowcast(),
sample_prediction(),
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 <- apply_reporting_structure(rep_tri)
uncertainty_params <- estimate_uncertainty_retro(
rep_tri,
n_history_delay = 8,
n_retrospective_nowcasts = 2
)
nowcast_draws <- sample_nowcasts(
point_nowcast_matrix,
reporting_triangle,
uncertainty_params,
draws = 5
)
nowcast_draws
#> pred_count reference_date draw
#> 1 472 2026-03-23 1
#> 2 368 2026-03-24 1
#> 3 534 2026-03-25 1
#> 4 364 2026-03-26 1
#> 5 459 2026-03-27 1
#> 6 406 2026-03-28 1
#> 7 598 2026-03-29 1
#> 8 538 2026-03-30 1
#> 9 490 2026-03-31 1
#> 10 349 2026-04-01 1
#> 11 472 2026-03-23 2
#> 12 368 2026-03-24 2
#> 13 534 2026-03-25 2
#> 14 364 2026-03-26 2
#> 15 459 2026-03-27 2
#> 16 402 2026-03-28 2
#> 17 641 2026-03-29 2
#> 18 505 2026-03-30 2
#> 19 468 2026-03-31 2
#> 20 350 2026-04-01 2
#> 21 472 2026-03-23 3
#> 22 368 2026-03-24 3
#> 23 534 2026-03-25 3
#> 24 364 2026-03-26 3
#> 25 459 2026-03-27 3
#> 26 408 2026-03-28 3
#> 27 676 2026-03-29 3
#> 28 493 2026-03-30 3
#> 29 410 2026-03-31 3
#> 30 520 2026-04-01 3
#> 31 472 2026-03-23 4
#> 32 368 2026-03-24 4
#> 33 534 2026-03-25 4
#> 34 364 2026-03-26 4
#> 35 459 2026-03-27 4
#> 36 408 2026-03-28 4
#> 37 609 2026-03-29 4
#> 38 454 2026-03-30 4
#> 39 448 2026-03-31 4
#> 40 453 2026-04-01 4
#> 41 472 2026-03-23 5
#> 42 368 2026-03-24 5
#> 43 534 2026-03-25 5
#> 44 364 2026-03-26 5
#> 45 459 2026-03-27 5
#> 46 410 2026-03-28 5
#> 47 612 2026-03-29 5
#> 48 422 2026-03-30 5
#> 49 425 2026-03-31 5
#> 50 371 2026-04-01 5