get.IC_2S: Compute Information Criteria for a Bayesian...

In BayesPIM: Bayesian Prevalence-Incidence Mixture Model

Compute Information Criteria for a Bayesian Prevalence-Incidence Mixture Model

Description

Computes and returns information criteria for a fitted Bayesian prevalence-incidence mixture model, including the Widely Applicable Information Criterion 1 (WAIC-1), WAIC-2, and the Deviance Information Criterion (DIC). These criteria are commonly used for model comparison and evaluation in Bayesian analysis. See Gelman et al. (2014) for further details on these criteria.

Usage

get.IC_2S(mod, samples = nrow(mod$par.X.bi[[1]]), cores = NULL)

Arguments

mod	A fitted prevalence-incidence mixture model of class bayes.2S.
samples	The number of MCMC samples to use. Maximum is the number of post-burn-in samples available in the bayes.2S object.
cores	The number of cores for parallel processing using foreach. If NULL (default), all available cores will be used.

Details

This function calculates information criteria for a fitted Bayesian prevalence-incidence mixture model (bayes.2S). The information criteria include:

• WAIC-1: Based on the sum of posterior variances of log-likelihood contributions.

• WAIC-2: Similar to WAIC-1 but incorporates an alternative variance estimate.

• DIC: Measures model fit by penalizing complexity via the effective number of parameters.

The computation is performed by evaluating log-likelihood values for MCMC samples. By default, all MCMC samples after burn-in are used, though a subset can be specified via the samples argument.

Parallelization is available via the foreach package, utilizing multiple cores if cores is set accordingly. If cores = NULL, all available cores will be used.

Value

A matrix containing WAIC-1, WAIC-2, and DIC values for the model.

References

Gelman, A., Hwang, J., & Vehtari, A. (2014). Understanding predictive information criteria for Bayesian models. Stat Comput, 24(6), 997–1016.

Examples

# Generate data according to the Klausch et al. (2024) PIM
set.seed(2025)
dat = gen.dat(kappa = 0.7, n= 1e3, theta = 0.2,
              p = 1, p.discrete = 1,
              beta.X = c(0.2,0.2), beta.W = c(0.2,0.2),
              v.min = 20, v.max = 30, mean.rc = 80,
              sigma.X = 0.2, mu.X=5, dist.X = "weibull",
              prob.r  = 1)

# An initial model fit with fixed test sensitivity kappa (approx. 1-3 minutes, depending on machine)
mod = bayes.2S( Vobs = dat$Vobs,
                Z.X = dat$Z,
                Z.W = dat$Z,
                r= dat$r,
                kappa = 0.7,
                update.kappa = FALSE,
                ndraws= 1e4,
                chains = 2,
                prop.sd.X = 0.008,
                parallel = TRUE,
                dist.X = 'weibull'
)

# Get information criteria
get.IC_2S(mod, samples = 1e3, cores = 2)

BayesPIM documentation built on April 12, 2025, 1:59 a.m.