estimIncub: Estimation of the incubation density based on coarse data

In oswaldogressani/EpiLPS: A Fast and Flexible Bayesian Tool for Estimating Epidemiological Parameters

Estimation of the incubation density based on coarse data

Description

This function computes an estimate of the incubation density based on coarse data constructed from symptom onset times and exposure windows. It uses the Laplacian-P-splines methodology with a Langevinized Gibbs algorithm to sample from the posterior distribution.

Usage

estimIncub(x, K = 10, niter = 1000, tmax = max(x), tgridlen = 500, verbose = FALSE)

Arguments

x	A data frame with the lower and upper bound of incubation interval.
K	Number of B-splines in the basis.
niter	The number of MCMC samples.
tmax	The upper bound for the B-spline basis. Default is the largest data point in x.
tgridlen	The number of grid points on which to evaluate the density.
verbose	Should a message be printed? Default is FALSE.

Value

A list of class incubestim containing summary values for the estimated incubation density.

Author(s)

Oswaldo Gressani [e-mail]

Examples

set.seed(123)
simdat <- incubsim(n = 30, tmax = 20) # Simulate incubation data
data <- simdat$Dobsincub              # Incubation bounds
incubfit <- estimIncub(x = data, niter = 500, tmax = 20, verbose = TRUE)

oswaldogressani/EpiLPS documentation built on Oct. 25, 2024, 8:15 p.m.