compare_models {MixSIAR}

Compare the predictive accuracy of 2 or more MixSIAR models

Description

compare_models uses the 'loo' package to compute LOO (leave-one-out cross-validation) or WAIC (widely applicable information criterion) for 2 of more fit MixSIAR models.

Usage

compare_models(x, loo = TRUE)

Arguments

x

list of two or more rjags model objects (output from run_model function)

loo

TRUE/FALSE: compute LOO if TRUE (preferred), compute WAIC if FALSE

Details

LOO and WAIC are "methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bayesian model using the log-likelihood evaluated at the posterior simulations of the parameter values". See Vehtari, Gelman, & Gabry (2017). In brief:

• LOO and WAIC are preferred over AIC or DIC

• LOO is more robust than WAIC

• 'loo' estimates standard errors for the difference in LOO/WAIC between two models

• We can calculate the relative support for each model using LOO/WAIC weights

Value

Data frame with the following columns:

• Model: names of x (input list)

• LOOic / WAIC: LOO information criterion or WAIC

• se_LOOic / se_WAIC: standard error of LOOic / WAIC

• dLOOic / dWAIC: difference between each model and the model with lowest LOOic/WAIC. Best model has dLOOic = 0.

• se_dLOOic / se_dWAIC: standard error of the difference between each model and the model with lowest LOOic/WAIC

• weight: relative support for each model, calculated as Akaike weights (p.75 Burnham & Anderson 2002). Interpretation: "an estimate of the probability that the model will make the best predictions on new data, conditional on the set of models considered" (McElreath 2015).

See Also

'loo' package

Vehtari, A, A Gelman, and J Gabry. 2017. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing.

Pages 75-88 in Burnham, KP and DR Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach. Springer Science & Business Media.

Pages 188-201 in McElreath, R. 2016. Statistical rethinking: a Bayesian course with examples in R and Stan. CRC Press.

Examples

## Not run: 
# Model 1 = wolf diet by Region + Pack
mix.1 <- load_mix_data(filename=mix.filename, 
                     iso_names=c("d13C","d15N"), 
                     factors=c("Region","Pack"), 
                     fac_random=c(TRUE,TRUE), 
                     fac_nested=c(FALSE,TRUE), 
                     cont_effects=NULL)
source.1 <- load_source_data(filename=source.filename, source_factors="Region",
                             conc_dep=FALSE, data_type="means", mix.1)
discr.1 <- load_discr_data(filename=discr.filename, mix.1)

# Run Model 1
jags.1 <- run_model(run="test", mix.1, source.1, discr.1, model_filename, 
                    alpha.prior = 1, resid_err=T, process_err=T)
                    
# Model 2 = wolf diet by Region (no Pack)
mix.2 <- load_mix_data(filename=mix.filename, 
                     iso_names=c("d13C","d15N"), 
                     factors=c("Region"), 
                     fac_random=c(TRUE), 
                     fac_nested=c(FALSE), 
                     cont_effects=NULL)
source.2 <- load_source_data(filename=source.filename, source_factors="Region",
                             conc_dep=FALSE, data_type="means", mix.2)
discr.2 <- load_discr_data(filename=discr.filename, mix.2)

# Run Model 2
jags.2 <- run_model(run="test", mix.2, source.2, discr.2, model_filename, 
                    alpha.prior = 1, resid_err=T, process_err=T)
                    
# Compare models 1 and 2 using LOO
compare_models(x=list(jags.1, jags.2), loo=TRUE)

# Compare models 1 and 2 using WAIC
compare_models(x=list(jags.1, jags.2), loo=FALSE)

# Get WAIC for model 1
compare_models(x=list(jags.1), loo=FALSE)

# Create named list of rjags objects to get model names in summary
x <- list(jags.1, jags.2)
names(x) <- c("Region + Pack", "Region")
compare_models(x)

## End(Not run)