logLik.mvmeta: Extract Log-Likelihood from mvmeta Objects

In mvmeta: Multivariate and Univariate Meta-Analysis and Meta-Regression

Description

This method function returns the log-likelihood for fitted univariate or multivariate meta-analytical models represented in objects of class "mvmeta".

Usage

## S3 method for class 'mvmeta'
logLik(object, ...)

Arguments

object	an object of class "mvmeta".
...	further arguments passed to or from other methods.

Value

A numeric scalar of class "logLik" with attributes, providing the (restricted) log likelihood of the model. Attributes correspond to the component df of mvmeta objects, namely the following scalars: nall (number of observations used for estimation, excluding missing values), nobs (equal to nall, minus the number of fixed-effects coefficients for REML models, fixed (number of estimated fixed-effects coefficients), random (number of estimated (co)variance terms).

Note

This functions is called by AIC and BIC for computing the Akaike and Bayesian information criteria.

Author(s)

Antonio Gasparrini, [e-mail]

References

Sera F, Armstrong B, Blangiardo M, Gasparrini A (2019). An extended mixed-effects framework for meta-analysis.Statistics in Medicine. 2019;38(29):5429-5444. [Freely available here].

Gasparrini A, Armstrong B, Kenward MG (2012). Multivariate meta-analysis for non-linear and other multi-parameter associations. Statistics in Medicine. 31(29):3821–3839. [Freely available here].

See Also

See the default method logLik. See mvmeta-package for an overview of the package and modelling framework.

Examples

# RUN THE MODEL 
model <- mvmeta(cbind(PD,AL)~pubyear,S=berkey98[5:7],data=berkey98)

# LOG-LIKELIHOOD
ll <- logLik(model)
ll
attributes(ll)

# AIC and BIC
AIC(model)
BIC(model)

mvmeta documentation built on Dec. 10, 2019, 5:07 p.m.