glm_model_select: Model selection for bg_GLM objects

In cwatson/brainGraph: Graph Theory Analysis of Brain MRI Data

Model selection for bg_GLM objects

Description

These functions compute the log-likelihood and Akaike's An Information Criterion (AIC) of a bg_GLM object. See logLik.lm and extractAIC for details.

Usage

## S3 method for class 'bg_GLM'
logLik(object, REML = FALSE, ...)

## S3 method for class 'bg_GLM'
extractAIC(fit, scale = 0, k = 2, ...)

Arguments

object, fit	A bg_GLM object
REML	Logical indicating whether to return the restricted log-likelihood. Default: FALSE
...	Unused
scale	Should be left at its default
k	Numeric; the weight of the equivalent degrees of freedom

Details

The functions AIC and BIC will also work for bg_GLM objects because they each call logLik.

Value

logLik returns an object of class logLik with several attributes. extractAIC returns a numeric vector in which the first element is the equivalent degrees of freedom and the remaining are the AIC's for each region

cwatson/brainGraph documentation built on Feb. 21, 2024, 6:33 p.m.