Get a dataframe of multiple draws of only the predicted elements of the nowcast vector
Source:R/sample_predictions.R
sample_predictions.RdGet a dataframe of multiple draws of only the predicted elements of the nowcast vector
Usage
sample_predictions(
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_prediction.
Value
Dataframe containing the predicted point nowcast vectors indexed by
predicted count (pred_count), reference date (reference_date), and
the draw index (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_nowcasts(),
sample_prediction()
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_pred_draws <- sample_predictions(
point_nowcast_matrix,
reporting_triangle,
uncertainty_params,
draws = 5
)
nowcast_pred_draws
#> pred_count reference_date draw
#> 1 0 2026-03-23 1
#> 2 0 2026-03-24 1
#> 3 0 2026-03-25 1
#> 4 0 2026-03-26 1
#> 5 0 2026-03-27 1
#> 6 9 2026-03-28 1
#> 7 72 2026-03-29 1
#> 8 47 2026-03-30 1
#> 9 129 2026-03-31 1
#> 10 247 2026-04-01 1
#> 11 0 2026-03-23 2
#> 12 0 2026-03-24 2
#> 13 0 2026-03-25 2
#> 14 0 2026-03-26 2
#> 15 0 2026-03-27 2
#> 16 13 2026-03-28 2
#> 17 69 2026-03-29 2
#> 18 140 2026-03-30 2
#> 19 72 2026-03-31 2
#> 20 176 2026-04-01 2
#> 21 0 2026-03-23 3
#> 22 0 2026-03-24 3
#> 23 0 2026-03-25 3
#> 24 0 2026-03-26 3
#> 25 0 2026-03-27 3
#> 26 15 2026-03-28 3
#> 27 25 2026-03-29 3
#> 28 93 2026-03-30 3
#> 29 144 2026-03-31 3
#> 30 147 2026-04-01 3
#> 31 0 2026-03-23 4
#> 32 0 2026-03-24 4
#> 33 0 2026-03-25 4
#> 34 0 2026-03-26 4
#> 35 0 2026-03-27 4
#> 36 9 2026-03-28 4
#> 37 39 2026-03-29 4
#> 38 92 2026-03-30 4
#> 39 75 2026-03-31 4
#> 40 150 2026-04-01 4
#> 41 0 2026-03-23 5
#> 42 0 2026-03-24 5
#> 43 0 2026-03-25 5
#> 44 0 2026-03-26 5
#> 45 0 2026-03-27 5
#> 46 15 2026-03-28 5
#> 47 18 2026-03-29 5
#> 48 77 2026-03-30 5
#> 49 97 2026-03-31 5
#> 50 135 2026-04-01 5
# Get nowcast pred draws over rolling sum
if (requireNamespace("zoo", quietly = TRUE)) {
nowcast_pred_draws_rolling_df <- sample_predictions(
point_nowcast_matrix,
reporting_triangle,
uncertainty_params,
draws = 5,
ref_time_aggregator = function(x) zoo::rollsum(x, k = 2, align = "right")
)
nowcast_pred_draws_rolling_df
}
#> pred_count reference_date draw
#> 1 NA 2026-03-23 1
#> 2 0 2026-03-24 1
#> 3 0 2026-03-25 1
#> 4 0 2026-03-26 1
#> 5 0 2026-03-27 1
#> 6 28 2026-03-28 1
#> 7 126 2026-03-29 1
#> 8 207 2026-03-30 1
#> 9 239 2026-03-31 1
#> 10 460 2026-04-01 1
#> 11 NA 2026-03-23 2
#> 12 0 2026-03-24 2
#> 13 0 2026-03-25 2
#> 14 0 2026-03-26 2
#> 15 0 2026-03-27 2
#> 16 23 2026-03-28 2
#> 17 133 2026-03-29 2
#> 18 91 2026-03-30 2
#> 19 145 2026-03-31 2
#> 20 293 2026-04-01 2
#> 21 NA 2026-03-23 3
#> 22 0 2026-03-24 3
#> 23 0 2026-03-25 3
#> 24 0 2026-03-26 3
#> 25 0 2026-03-27 3
#> 26 22 2026-03-28 3
#> 27 131 2026-03-29 3
#> 28 79 2026-03-30 3
#> 29 186 2026-03-31 3
#> 30 514 2026-04-01 3
#> 31 NA 2026-03-23 4
#> 32 0 2026-03-24 4
#> 33 0 2026-03-25 4
#> 34 0 2026-03-26 4
#> 35 0 2026-03-27 4
#> 36 36 2026-03-28 4
#> 37 155 2026-03-29 4
#> 38 96 2026-03-30 4
#> 39 291 2026-03-31 4
#> 40 273 2026-04-01 4
#> 41 NA 2026-03-23 5
#> 42 0 2026-03-24 5
#> 43 0 2026-03-25 5
#> 44 0 2026-03-26 5
#> 45 0 2026-03-27 5
#> 46 11 2026-03-28 5
#> 47 145 2026-03-29 5
#> 48 120 2026-03-30 5
#> 49 131 2026-03-31 5
#> 50 313 2026-04-01 5