View source: R/diagnose_model.R
diagnose_model | R Documentation |
This function operates a diagnostic of the fit EcoDiet model.
A message is printed to provide the number of variables for which the Gelman-Rubin diagnostic exceeds specific thresholds (> 1.01, > 1.05, >1.1). The list of the 10 worst variables in terms of convergence is also printed.
You need to have run the run_model
function before using this function.
The design of this function is substantially inspired from a function with a similar objective
in the MixSIAR package [(Stock et al. 2018)](https://doi.org/10.7717/peerj.5096), for which code is available online on
the [MixSIAR GitHub repository](https://github.com/brianstock/MixSIAR).
The diagnostic plots are generated using the ggmcmc
package [(Fernández-i-Marín, 2016)](https://CRAN.R-project.org/package=ggmcmc).
diagnose_model(jags_output, var.to.diag = "all", save = FALSE, save_path = ".")
jags_output |
The MCMC output summarized in the class jagsUI object
output by |
var.to.diag |
The list of variables for which diagnostic plots should be produced and save. By default, this argument is "all" hence is run for all the variables. |
save |
Indicates whether diagnostic plots should be produced and saved. |
save_path |
The path indicating where to save the diagnostic plots. |
A matrix containing the Gelman diagnostic for all the variables monitored by the run_model
function (variables_to_save argument
).
run_model
to run the model
realistic_biotracer_data <- read.csv(system.file("extdata", "realistic_biotracer_data.csv", package = "EcoDiet")) realistic_stomach_data <- read.csv(system.file("extdata", "realistic_stomach_data.csv", package = "EcoDiet")) data <- preprocess_data(biotracer_data = realistic_biotracer_data, trophic_discrimination_factor = c(0.8, 3.4), literature_configuration = FALSE, stomach_data = realistic_stomach_data) write_model(literature_configuration = FALSE) mcmc_output <- run_model("EcoDiet_model.txt", data, run_param="test") Gelman_test <- diagnose_model(mcmc_output) Gelman_test
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