Extract Included Realizations from an amerasfit Object

Extracts the indices of dose realizations included in model averaging for FMA and/or BMA results of a fitted amerasfit object. Realizations are excluded from FMA if the optimization did not converge or the Hessian was not positive definite, or if the realization receives no samples after integer sample allocation.

Usage

included_realizations(x, ...)

# S3 method for class 'amerasfit'
included_realizations(x,
                              methods = c("FMA", "BMA"),
                              ...)

Arguments

x

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

methods

Character vector specifying which method(s) to extract included realizations for. One or both of "FMA" and "BMA". Defaults to both. Only methods that were run are returned.

...

Additional arguments, currently unused.

Value

If a single method is requested or only one method was run, an integer vector of indices of the included dose realizations. If both FMA and BMA results are present and both are requested, a named list with elements FMA and BMA, each containing an integer vector of included realization indices.

Details

For FMA, realizations are excluded if the optimization did not converge or the Hessian was not invertible or not positive definite. Realizations with model-averaging weights too small to receive any samples for the requested MFMA are also excluded from the final FMA result. Warnings and messages are issued during fitting when realizations are excluded or when the final FMA result is based on very few realizations.

For BMA, all realizations are included by default. A subset of realizations can be specified via the included.realizations.BMA argument to ameras, for example by passing the indices returned by included_realizations(fit, methods="FMA") from a prior FMA fit.

See also

ameras, Rhat.amerasfit

Examples

# \donttest{
data("data", package="ameras")
dosevars <- paste0("V", 1:10)

## FMA only
fit <- ameras(Y.binomial ~ dose(all_of(dosevars), model="ERR"),
              data=data, family="binomial", methods="FMA")
#> Fitting FMA
included_realizations(fit)
#>  [1]  1  2  3  4  5  6  7  8  9 10
length(included_realizations(fit))
#> [1] 10

## Both FMA and BMA
fit2 <- ameras(Y.binomial ~ dose(all_of(dosevars), model="ERR"),
               data=data, family="binomial", methods=c("FMA", "BMA"))
#> Note: BMA may require extensive computation time
#> Fitting FMA
#> 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...
#> |-------------|-------------|-------------|-------------|
#> |-------------------------------------------------------|
included_realizations(fit2)
#> $FMA
#>  [1]  1  2  3  4  5  6  7  8  9 10
#> 
#> $BMA
#>  [1]  1  2  3  4  5  6  7  8  9 10
#> 
included_realizations(fit2, methods="FMA")
#>  [1]  1  2  3  4  5  6  7  8  9 10
included_realizations(fit2, methods="BMA")
#>  [1]  1  2  3  4  5  6  7  8  9 10
# }