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 |
... |
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]