AIC.remix {REMixed}R Documentation

AIC for remix object

Description

Computes akaike information criterion from the output of remix as

AIC = -2\mathcal{LL}_{y}(\hat\theta,\hat\alpha)+k\times P

where P is the total number of parameters estimated and \mathcal{LL}_{y}(\hat\theta,\hat\alpha) the log-likelihood of the model.

Usage

## S3 method for class 'remix'
AIC(object, ..., k)

Arguments

object

output of remix.

...

additional arguments.

k

numeric, the penalty per parameter to be used; the default k = 2 is the classical AIC.

Value

AIC.

References

Akaike, H. 1998. Information theory and an extension of the maximum likelihood principle, Selected papers of hirotugu akaike, 199-213. New York: Springer.

Examples

## Not run: 
project <- getMLXdir()

ObsModel.transfo = list(S=list(AB=log10),
                        linkS="yAB",
                        R=rep(list(S=function(x){x}),5),
                        linkR = paste0("yG",1:5))

alpha=list(alpha0=NULL,
           alpha1=setNames(paste0("alpha_1",1:5),paste0("yG",1:5)))

y = c(S=5,AB=1000)
lambda = 1440

res = remix(project = project,
            dynFUN = dynFUN_demo,
            y = y,
            ObsModel.transfo = ObsModel.transfo,
            alpha = alpha,
            selfInit = TRUE,
            eps1=10**(-2),
            eps2=1,
            lambda=lambda)

AIC(res)

## End(Not run)

[Package REMixed version 0.1.0 Index]