Generate 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: Matrix of the reporting triangle, with rows representing the time points of reference and columns representing the 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 time (time), the predicted counts (pred_count), and the draw number (draw).

Examples

point_nowcast_matrix <- matrix(
  c(
    80, 50, 25, 10,
    100, 50, 30, 20,
    90, 45, 25, 16.8,
    80, 40, 21.2, 19.5,
    70, 34.5, 15.4, 9.1
  ),
  nrow = 5,
  byrow = TRUE
)
reporting_triangle <- construct_triangle(point_nowcast_matrix)
disp <- c(0.8, 12.4, 9.1)
nowcast_draws <- sample_nowcasts(
  point_nowcast_matrix,
  reporting_triangle,
  disp,
  draws = 5
)
nowcast_draws
#>    pred_count time draw
#> 1         165    1    1
#> 2         200    2    1
#> 3         174    3    1
#> 4         158    4    1
#> 5          70    5    1
#> 6         165    1    2
#> 7         200    2    2
#> 8         180    3    2
#> 9         164    4    2
#> 10        257    5    2
#> 11        165    1    3
#> 12        200    2    3
#> 13        180    3    3
#> 14        170    4    3
#> 15        230    5    3
#> 16        165    1    4
#> 17        200    2    4
#> 18        168    3    4
#> 19        173    4    4
#> 20        106    5    4
#> 21        165    1    5
#> 22        200    2    5
#> 23        171    3    5
#> 24        150    4    5
#> 25         71    5    5