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Preprocess negative values in the reporting triangle

Source: R/preprocess.R
preprocess_negative_values.Rd

Takes in a reporting triangle and returns a matrix in the same format as the input triangle, but with negative values of reporting handled by redistributing them to earlier delays (from longer delay to shorter). This is useful when dealing with reporting corrections that can result in negative incremental counts.

When negative values are detected, they are set to zero and the negative amount is subtracted from the count at the next earlier delay (moving from right to left in each row). This process continues until either the negative value is fully absorbed or the first delay is reached.

This code was adapted from code written (under an MIT license) by the Karlsruhe Institute of Technology RESPINOW German Hospitalization Nowcasting Hub. Modified from https://github.com/KITmetricslab/RESPINOW-Hub/blob/main/code/baseline/functions.R #nolint

Usage

preprocess_negative_values(triangle)

Arguments

triangle

Matrix of the reporting triangle, with rows representing the time points of reference and columns representing the delays.

Value

Matrix of positive integers with negative values of reporting handled via redistribution to earlier delays.

Details

Use this function when:

  • Your data contains reporting corrections that result in negative counts

  • You want to preserve the total count while handling negatives

  • You need a delay distribution that sums to 1 or a CDF that is weakly increasing

Set preprocess = NULL in estimate_delay() when:

  • Your data naturally has negative PMF entries (e.g., from differencing)

  • You want to preserve the original structure including negatives

  • You are working with corrections that should be reflected as negative probabilities

See also

Delay distribution estimation functions estimate_delay()

Examples

# Triangle with negative values from corrections
triangle_with_neg <- matrix(c(
  10, 5, -2, 3,
  8, -3, 4, 2,
  1, 6, 3, -1
), nrow = 3, byrow = TRUE)

# Preprocess to handle negatives
preprocessed <- preprocess_negative_values(triangle_with_neg)
#> ℹ Negative values detected in reporting triangle and will be corrected
preprocessed
#>      [,1] [,2] [,3] [,4]
#> [1,]   10    3    0    3
#> [2,]    5    0    4    2
#> [3,]    1    6    2    0