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Generate retrospective nowcasts

Source: R/fill_triangles.R
fill_triangles.Rd

This function ingests a list of incomplete reporting triangles and generates a list of point nowcast matrices, based on the delay estimated in each triangle or the corresponding delay passed in. It uses the specified n number of reference times to estimate the delay in each retrospective reporting triangle.

Usage

fill_triangles(
  retro_reporting_triangles,
  n = min(sapply(retro_reporting_triangles, nrow)),
  delay_pmf = NULL,
  preprocess = preprocess_negative_values,
  validate = TRUE
)

Arguments

retro_reporting_triangles

List of n truncated reporting triangle matrices with as many rows as available given the truncation.

n

Integer indicating the number of reference times (number of rows) to use to estimate the delay distribution for each reporting triangle. Default is the minimum of the number of rows of all the matrices in the list_of_rts.

delay_pmf

Vector or list of vectors of delays assumed to be indexed starting at the first delay column in each of the matrices in retro_reporting_triangles. If a list, must of the same length as retro_reporting_triangles, with elements aligning. Default is NULL

preprocess

Function to apply to the truncated triangle before estimation, or NULL to skip preprocessing. Default is preprocess_negative_values(), which handles negative values by redistributing them to earlier delays. Set to NULL if you want to preserve negative PMF entries (e.g., when working with downward corrections where negative probabilities reflect systematic adjustments). Custom preprocess functions must accept a validate parameter (defaults to TRUE) to enable validation optimisation in internal function chains.

validate

Logical. If TRUE (default), validates the object. Set to FALSE only when called from functions that already validated.

Value

point_nowcast_matrices List of the same number of elements as the input retro_reporting_trianglesbut with each reporting triangle filled in based on the delay estimated in that reporting triangle.

See also

Point nowcast generation functions apply_delay(), fill_triangle()

Examples

# Generate retrospective nowcasts using larger triangle
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 = 25) |>
  tail(n = 50)
#> ℹ Using max_delay = 154 from data
#> ℹ Truncating from max_delay = 154 to 25.
trunc_rts <- truncate_triangles(rep_tri, n = 2)
retro_rts <- construct_triangles(trunc_rts)
retro_pt_nowcast_mat_list <- fill_triangles(retro_rts, n = 30)
retro_pt_nowcast_mat_list[1:2]
#> [[1]]
#> Reporting Triangle
#> ℹ The reporting triangle does not contain any missing values.
#> Delays unit: days
#> Reference dates: 2026-02-11 to 2026-03-31
#> Max delay: 25
#> Structure: 0
#> 
#> Showing last 10 of 49 rows
#> Showing first 10 of 26 columns
#> 
#>              0         1        2        3        4        5        6
#> 2026-03-22 246  95.00000 57.00000 19.00000 38.00000 14.00000 10.00000
#> 2026-03-23 210 131.00000 34.00000 50.00000 35.00000 12.00000  1.00000
#> 2026-03-24 221  96.00000 22.00000 10.00000 13.00000  6.00000  0.00000
#> 2026-03-25 291 129.00000 17.00000 26.00000 42.00000 29.00000 23.00000
#> 2026-03-26 179  96.00000 22.00000 50.00000  9.00000  8.00000 11.83200
#> 2026-03-27 284  40.00000 41.00000 54.00000 28.00000 16.55964 15.06626
#> 2026-03-28 217  78.00000 46.00000 14.00000 15.66955 13.73347 12.49481
#> 2026-03-29 336 161.00000 62.00000 37.81203 26.33456 23.08154 21.00036
#> 2026-03-30 296  53.00000 34.74663 25.96444 18.08256 15.84851 14.41923
#> 2026-03-31 210  81.31027 29.00940 21.67661 15.09603 13.23077 12.03741
#>                   7        8         9
#> 2026-03-22  0.00000  0.00000 10.000000
#> 2026-03-23 25.00000 20.00000  8.524254
#> 2026-03-24  5.00000 12.90819  6.351134
#> 2026-03-25 18.96593 19.92891  9.805748
#> 2026-03-26 12.79932 13.44889  6.617189
#> 2026-03-27 16.29823 17.12562  8.426363
#> 2026-03-28 13.51636 14.20237  6.987948
#> 2026-03-29 22.71789 23.87156 11.745771
#> 2026-03-30 15.59825 16.39007  8.064427
#> 2026-03-31 13.02154 13.68240  6.732091
#> 
#> Use print(x, n_rows = NULL, n_cols = NULL) to see all data
#> 
#> [[2]]
#> Reporting Triangle
#> ℹ The reporting triangle does not contain any missing values.
#> Delays unit: days
#> Reference dates: 2026-02-11 to 2026-03-30
#> Max delay: 25
#> Structure: 0
#> 
#> Showing last 10 of 48 rows
#> Showing first 10 of 26 columns
#> 
#>              0        1        2        3        4        5        6
#> 2026-03-21 280 155.0000 83.00000 46.00000 15.00000 22.00000  3.00000
#> 2026-03-22 246  95.0000 57.00000 19.00000 38.00000 14.00000 10.00000
#> 2026-03-23 210 131.0000 34.00000 50.00000 35.00000 12.00000  1.00000
#> 2026-03-24 221  96.0000 22.00000 10.00000 13.00000  6.00000  0.00000
#> 2026-03-25 291 129.0000 17.00000 26.00000 42.00000 29.00000 17.52451
#> 2026-03-26 179  96.0000 22.00000 50.00000  9.00000 13.45815 12.12692
#> 2026-03-27 284  40.0000 41.00000 54.00000 18.61340 16.54126 14.90528
#> 2026-03-28 217  78.0000 46.00000 23.15962 16.17885 14.37758 12.95547
#> 2026-03-29 336 161.0000 48.11429 37.00906 25.85500 22.97716 20.70501
#> 2026-03-30 296 115.3655 39.83053 30.63657 21.40276 19.02027 17.13925
#>                   7        8         9
#> 2026-03-21  4.00000 25.00000  0.000000
#> 2026-03-22  0.00000  0.00000  7.920419
#> 2026-03-23 25.00000 16.27173  8.503475
#> 2026-03-24 12.72228 12.44109  6.501506
#> 2026-03-25 19.06357 18.64268  9.742577
#> 2026-03-26 13.19168 12.90015  6.741420
#> 2026-03-27 16.21418 15.85607  8.286240
#> 2026-03-28 14.09304 13.78165  7.202111
#> 2026-03-29 22.52355 22.02644 11.510995
#> 2026-03-30 18.64445 18.23280  9.528365
#> 
#> Use print(x, n_rows = NULL, n_cols = NULL) to see all data
#>