discretise: Discretise a <dist_spec>

In epiforecasts/EpiNow2: Estimate Real-Time Case Counts and Time-Varying Epidemiological Parameters

Discretise a <dist_spec>

Description

Usage

## S3 method for class 'dist_spec'
discretise(x, strict = TRUE, ...)

discretize(x, ...)

Arguments

x	A ⁠<dist_spec>⁠
strict	Logical; If TRUE (default) an error will be thrown if a distribution cannot be discretised (e.g., because no finite maximum has been specified or parameters are uncertain). If FALSE then any distribution that cannot be discretised will be returned as is.
...	ignored

Value

A ⁠<dist_spec>⁠ where all distributions with constant parameters are nonparametric.

Methodological details

The probability mass function of the discretised probability distribution is a vector where the first entry corresponds to the integral over the (0,1] interval of the corresponding continuous distribution (probability of integer 0), the second entry corresponds to the (0,2] interval (probability mass of integer 1), the third entry corresponds to the (1, 3] interval (probability mass of integer 2), etc. This approximates the true probability mass function of a double censored distribution which arises from the difference of two censored events.

References

Charniga, K., et al. “Best practices for estimating and reporting epidemiological delay distributions of infectious diseases using public health surveillance and healthcare data”, arXiv e-prints, 2024. https://doi.org/10.48550/arXiv.2405.08841 Park, S. W., et al., "Estimating epidemiological delay distributions for infectious diseases", medRxiv, 2024. https://doi.org/10.1101/2024.01.12.24301247

Examples

# A fixed gamma distribution with mean 5 and sd 1.
dist1 <- Gamma(mean = 5, sd = 1, max = 20)

# An uncertain lognormal distribution with mean 3 and sd 2
dist2 <- LogNormal(mean = Normal(3, 0.5), sd = Normal(2, 0.5), max = 20)

# The maxf the sum of two distributions
discretise(dist1 + dist2, strict = FALSE)

epiforecasts/EpiNow2 documentation built on Aug. 15, 2024, 8:12 a.m.