CLIC: Composite Likelihood Information Criterion

In lacm: Latent Autoregressive Count Models

Description

Calculates the composite likelihood information criterion for a latent autoregressive count model fitted through maximum pairwise likelihood.

Usage

CLIC(object, ...)

Arguments

object	a fitted model object of class "lacm".
...	optional arguments.

Details

Function CLIC computes the composite likelihood information criterion (Varin and Vidoni, 2005) for a latent autoregressive count model estimated by maximum pairwise likelihood. See Pedeli and Varin (2020) for details.

When comparing models fitted by maximum pairwise likelihood to the same data, the smaller the CLIC, the better the fit.

Value

a numeric value with the corresponding CLIC.

Author(s)

Xanthi Pedeli and Cristiano Varin.

References

Pedeli, X. and Varin, C. (2020). Pairwise likelihood estimation of latent autoregressive count models. Statistical Methods in Medical Research.doi: 10.1177/0962280220924068.

Varin, C. and Vidoni, P. (2005). A note on composite likelihood inference and model selection. Biometrika, 92, 519–528.

See Also

lacm.

Examples

data("polio", package = "lacm")
## model components
trend <- 1:length(polio)
sin.term <- sin(2 * pi * trend / 12)
cos.term <- cos(2 * pi * trend / 12)
sin2.term <- sin(2 * pi * trend / 6)
cos2.term <- cos(2 * pi * trend / 6)
## fit model with pairwise likelihood of order 1
mod1 <- lacm(polio ~ I(trend * 10^(-3)) + sin.term + cos.term + sin2.term + cos2.term)
CLIC(mod1)

lacm documentation built on July 1, 2020, 6:53 p.m.