epidic {EpiILM}

Deviance Information Criterion (DIC)

Description

Computes the Deviance Information Criterion for individual level models

Usage

epidic (burnin, niter, LLchain, LLpostmean)

Arguments

burnin

Burnin period for MCMC

niter

Number of MCMC iterations

LLchain

Loglikelihood values from the MCMC output

LLpostmean

Loglikelihood value of the model with posterior mean of estimates

References

Spiegelhalter, D., Best, N., Carlin, B., Van der Linde, A. (2002). Bayesian Measures of Model Complexity and Fit. Journal of the Royal Statistical Society. Series B (Statistical Methodology), 64(4), 583-639.

Examples

## Example 1:  spatial SI model
# generate 100 individuals

x <- runif(100, 0, 10)

y <- runif(100, 0, 10)

covariate <- runif(100, 0, 2)

out1 <- epidata(type = "SI", n = 100, Sformula = ~covariate, tmax = 15,
              sus.par = c(0.1, 0.3), beta = 5.0, x = x, y = y)

unif_range <- matrix(c(0, 0, 10000, 10000), nrow = 2, ncol = 2)

# estimate parameters
mcmcout <- epimcmc(out1, tmax = 15, niter = 1500,
                    Sformula = ~covariate,   
                    sus.par.ini = c(0.003, 0.01), beta.ini =0.01,
                    pro.sus.var = c(0.1, 0.1),pro.beta.var = 0.5,
                    prior.sus.par = unif_range,
                    prior.sus.dist = c("uniform","uniform"), prior.beta.dist = "uniform",
                    prior.beta.par = c(0, 10000), adapt = TRUE, acc.rate = 0.5 )
# store the estimates
sus.parameters = c(mean(unlist(mcmcout$Estimates[1])), mean(unlist(mcmcout$Estimates[2])))
beta.par = mean(unlist(mcmcout$Estimates[3]))

# likelihood value
loglike <- epilike(out1, tmax = 15, Sformula = ~covariate, sus.par = sus.parameters, 
                   beta = beta.par)

# deviance information criterion calculation for the above epidemic
dic <- epidic(burnin = 500, niter = 1500, LLchain = mcmcout$Loglikelihood,
              LLpostmean = loglike)
dic