ExposureModel: Create an ExposureModel object, describing an epidemic...

In grantbrown/ABSEIR: Posterior Approximation to Fully Bayesian Spatial SEIR Models Using Multithreaded C++

Description

Create an ExposureModel object, describing an epidemic intensity process

Usage

ExposureModel(
  X,
  nTpt,
  nLoc,
  betaPriorPrecision = NA,
  betaPriorMean = NA,
  offset = NA
)

Arguments

X	an $(n*T)$ by $p$ design matrix, where $n$ is the number of locations, $T$ is the number of time points, and $p$ is the number of exposure process parameters. Each column corresponds to a parameter, while each block of $T$ rows corresponds to the time series of covariate values associated with a location.
nTpt	the number of time points, $T$
nLoc	the number of locations, $N$
betaPriorPrecision	the prior precisions of the $p$ exposure process parameters
betaPriorMean	the prior means of the $p$ exposure process parameters
offset	a vector of $T$ temporal offset terms, capturing the relative ammount of aggregated continuous time corresponding to each recorded discrete time point.

Details

The exposure process allows the inclusion of both spatially and temporally varying covariates, as well as invariant quantities (intercepts, demographic features etc.).

Examples

exposureModel <- ExposureModel(cbind(1, (1:25)/25),
                                         nTpt = 25, nLoc = 1,
                                         betaPriorPrecision = 0.1,
                                         betaPriorMean = 0)

grantbrown/ABSEIR documentation built on Oct. 14, 2021, 2:32 p.m.