dic {isotracer}

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)

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).

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.

Examples

# 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)