summary.rpql: Summary of GLMM fitted using regularized PQL.

In rpql: Regularized PQL for Joint Selection in GLMMs

Summary of GLMM fitted using regularized PQL.

Description

A summary of the results from applying rpql.

Usage

## S3 method for class 'rpql'
summary(object, ...)

## S3 method for class 'summary.rpql'
print(x,...)

Arguments

object	An object of class "rpql".
x	An object of class "rpql".
...	Not used.

Value

A list (some of which is printed) containing the following elements:

Call	The matched call.
fixed	Estimated fixed effects coefficients.
ranef	A list with each element being a matrix of estimated random effects coefficients.
ran.cov	A list with each element being a estimated random effects covariance matrix.
logLik	PQL log-likelihood value at convergence.
family	The family argument, i.e. response type.
pen.type,lambda	Penalties used for selection and the corresponding tuning parameter values.
ics	A vector containing the number of estimated, non-zero parameters, and three information criterion. Please see the help file for rpql for details on these criteria.
id	The id argument, i.e. list of IDs.
nonzero.fixef	A vector indexing which of the estimated fixed effect coefficients are non-zero.
nonzero.ranef	A list with each element being a vector indexing which of the estimated random effects are non-zero, i.e. which of the diagonal elements in the corresponding element of ran.cov are non-zero.

Author(s)

Francis K.C. Hui <[e-mail]>, with contributions from Samuel Mueller <[e-mail]> and A.H. Welsh <[e-mail]>

Maintainer: Francis Hui <[e-mail]>

See Also

rpql for fitting and performing model selection in GLMMs using regularized PQL.

Examples

## Please see other examples in help file for the \code{rpql} function.

rpql documentation built on Aug. 20, 2023, 1:08 a.m.