infer_surface: Infer a (summary) likelihood or tail probability surface from...

In Infusion: Inference Using Simulation

Infer a (summary) likelihood or tail probability surface from inferred likelihoods

Description

These functions are either for the primitive workflow or experimental. For standard use of Infusion see instead the functions used in the up-to-date workflow (example_reftable)

The logLs method uses a standard smoothing method (prediction under linear mixed models, a.k.a. Kriging) to infer a likelihood surface, using as input likelihood values themselves inferred with some error for different parameter values. The tailp method use a similar approach for smoothing binomial response data, using the algorithms implemented in the spaMM package for fitting GLMMs with autocorrelated random effects.

Usage

## S3 method for class 'logLs'
infer_surface(object, method="REML",verbose=interactive(),allFix=NULL,...)
## S3 method for class 'tailp'
infer_surface(object, method="PQL",verbose=interactive(),allFix,...)

Arguments

object	A data frame with attributes, containing independent prediction of logL or of LR tail probabilities for different parameter points, as produced by infer_logLs or infer_tailp.
method	methods used to estimate the smoothing parameters. If method="GCV", a generalized cross-validation procedure is used (for logLs method only). Other methods are as described in the HLfit documentation.
verbose	Whether to display some information about progress or not.
allFix	Fixed values in the estimation of smoothing parameters. For development purposes, not for routine use. For infer_surface.logLs, this should typically include values of all parameters fitted by fitme (\rho,\nu,\phi,\lambda, and $etaFix=\beta).
...	further arguments passed to or from other methods (currently not used).

Value

An object of class SLik or SLikp, which is a list including the fit object returned by fitme, and additional members not documented here.

Examples

## see main documentation page for the package

Infusion documentation built on Sept. 30, 2024, 9:16 a.m.