summaryLaplace: Summarize results from Laplace approximation

In nimble: MCMC, Particle Filtering, and Programmable Hierarchical Modeling

Summarize results from Laplace approximation

Description

Process the results of the 'findMLE' method of a nimble Laplace approximation into a more useful format.

Usage

summaryLaplace(
  laplace,
  MLEoutput,
  originalScale = TRUE,
  randomEffectsStdError = FALSE,
  jointCovariance = FALSE
)

Arguments

laplace	The Laplace approximation object, typically the compiled one. This would be the result of compiling an object returned from 'buildLaplace'.
MLEoutput	The maximum likelihood estimate using Laplace approximation, returned from 'laplace$findMLE(...)'. See 'help(buildLaplace)' for more information.
originalScale	Should results be returned using the original parameterization in the model code (TRUE) or the potentially transformed parameterization used internally by the Laplace approximation (FALSE). Transformations are used for any parameters and/or random effects that have constrained ranges of valid values, so that in the transformed parameter space there are no constraints.
randomEffectsStdError	If TRUE, calculate the standard error of the estimates of random effects values.
jointCovariance	If TRUE, calculate the joint covariance matrix of the parameters and random effects together. If FALSE, calculate the covariance matrix of the parameters.

Details

The numbers obtained by this function can be obtained more directly by 'laplace$summary(...)', which calls a (usually compiled) method of the 'laplace' nimbleFunction. The added benefit of 'summaryLaplace' is to arrange the results into data frames (for parameters and random effects), with row names for the model nodes, and also adding row and column names to the covariance matrix.

Value

A list with data frames 'params' and 'randomEffects', each with columns for 'estimate' and (possibly) 'se' (standard error) and row names for model nodes, a matrix 'vcov' with the covariance matrix with row and column names, and 'originalScale' with the input value of 'originalScale' so it is recorded for later use if wanted.

nimble documentation built on July 9, 2023, 5:24 p.m.