isotracer (version 1.1.8)

dic: Calculate DIC from a model output

Description

Note that DIC might not be indicated for network models, as the posteriors are often not multinormal distributions.

Usage

dic(..., weight = TRUE)

Value

A tibble with one row per mcmc.list object provided in

.... This tibble is sorted by DIC, so the row order might be different from the mcmc.list objects order.

Arguments

...

One or several mcmc.list objects, output(s) from run_mcmc.

weight

Boolean, if TRUE calculate DIC weights based on Link and Barker 2010 (Link, W. A., and R. J. Barker. 2010. Bayesian Inference With Ecological Applications. Amsterdam Boston Heidelberg London: Elsevier/Academic Press).

Details

LOO is probably not a good choice either since the data is akin to a time series (so data points are not independent). Maybe WAIC could be an option? (TODO: read about this.)

DIC is calculated as:

DIC = Dbar + pD

where D are deviance values calculated as -2 * loglik for each MCMC iteration, Dbar is the mean deviance value and pD is the effective number of parameters in the model and can be calculated as var(D)/2 (Gelman 2003).

Examples

# \donttest{
# Define two different models
m1 <- aquarium_mod
m2 <- set_topo(m1, c("NH4 -> algae -> daphnia -> NH4", "algae -> NH4"))
m2 <- set_priors(m2, priors(m1))
m2 <- set_priors(m2, normal_p(0, 0.5), "upsilon_algae_to_NH4")
# Run the models
r1 <- run_mcmc(m1, chains = 2)
r2 <- run_mcmc(m2, chains = 2)
# Model comparison with DIC
dic(r1, r2)
# }