Extract MCMC Convergence Diagnostics from an amerasfit Object

Extracts the Gelman-Rubin convergence diagnostics from the BMA results of a fitted amerasfit object.

Usage

Rhat(x, ...)

# S3 method for class 'amerasfit'
Rhat(x, ...)

Arguments

x

A fitted model object of class amerasfit, as returned by ameras, with BMA results present.

...

Additional arguments, currently unused.

Value

A data frame with columns Rhat and n.eff, containing the Gelman-Rubin statistic and effective sample size for each parameter. Values of Rhat substantially above 1.05 indicate potential convergence problems, in which case longer chains via niter.BMA and nburnin.BMA are recommended. For single-chain BMA fits, convergence diagnostics cannot be estimated and these columns are returned as NA.

See also

ameras, summary.amerasfit, included_realizations.amerasfit

Examples

# \donttest{
data("data", package="ameras")
dosevars <- paste0("V", 1:10)
fit <- ameras(Y.binomial ~ dose(all_of(dosevars), model="ERR"),
              data=data, family="binomial", methods="BMA")
#> Note: BMA may require extensive computation time
#> Fitting BMA
#> Defining model
#> Building model
#> Setting data and initial values
#> Running calculate on model
#>   [Note] Any error reports that follow may simply reflect missing values in model variables.
#> Checking model sizes and dimensions
#>   [Note] This model is not fully initialized. This is not an error.
#>          To see which variables are not initialized, use model$initializeInfo().
#>          For more information on model initialization, see help(modelInitialization).
#> Compiling
#>   [Note] This may take a minute.
#>   [Note] Use 'showCompilerOutput = TRUE' to see C++ compilation details.
#> Compiling
#>   [Note] This may take a minute.
#>   [Note] Use 'showCompilerOutput = TRUE' to see C++ compilation details.
#> running chain 1...
#> |-------------|-------------|-------------|-------------|
#> |-------------------------------------------------------|
#> running chain 2...
#> |-------------|-------------|-------------|-------------|
#> |-------------------------------------------------------|
Rhat(fit)
#>             Rhat n.eff
#> (Intercept) 1.03  1236
#> dose        1.02   969
# }