summary.gvs: Some diagnostics for fitted dose-response models

In pjbouchet/espresso: Bayesian Inference For Multi-Species Behavioural Dose-response Functions

Some diagnostics for fitted dose-response models

Description

Summary method for objects of class gvs, as returned by gibbs. Produces a text-based summary of: (1) effective sample sizes, (2) acceptance rates, (3) model convergence, and (4) posterior model probabilities. Note that gibbs does not implement covariate selection. As a result, posterior inclusion probabilities (PIPs) are not returned here, contrary to summary.rjtrace.

Usage

## S3 method for class 'gvs'
summary(
  gvs.obj,
  eff.n = TRUE,
  accept.rate = TRUE,
  convergence = TRUE,
  gelman.rubin = 1.1,
  model.ranks = TRUE,
  n.top = NULL
)

Arguments

gvs.obj	Input trace object, as returned by gibbs.
eff.n	Logical. If TRUE, returns estimates of the effective sample size for each model parameter.
accept.rate	Logical. If TRUE, returns the acceptance rate (calculated after burn-in) for each model parameter.
convergence	Logical. If TRUE, assesses convergence using the multivariate potential scale reduction factor (Gelman-Rubin statistic), as implemented in gelman.diag.
gelman.rubin	Threshold for determining convergence based on the Gelman-Rubin statistic. Defaults to 1.1.
model.ranks	Logical. If TRUE, returns a summary of posterior model probabilities and associated model rankings.
n.top	Number of top-ranking models to display when model.ranks = TRUE.

Value

A detailed summary, printed to the R console.

Author(s)

Phil J. Bouchet

See Also

simulate_data example_brs summary.brsdata

Examples

## Not run: 
library(espresso)

# Simulate data for two species
mydat <- simulate_data(n.species = 2,
                       n.whales = 16,
                       min.trials = 1,
                       max.trials = 3,
                       covariates = list(exposed = c(0, 5), range = 0.5),
                       mu = c(101, 158),
                       phi = 20,
                       sigma = 20,
                       Lc = c(60, 65),
                       Rc = c(210, 211),
                       seed = 58697)
summary(mydat)

# Model selection by GVS
gvs.model <- gibbs(dat = mydat,
             random.effects = FALSE,
             include.covariates = FALSE,
             mcmc.n = 1000,
             burnin = 500)

summary(gvs.model)

## End(Not run)

pjbouchet/espresso documentation built on July 27, 2024, 12:31 p.m.