SpatialExtremes (version 1.2-0)

TIC: Takeuchi's information criterion

Description

Computes a "generalization" of the Takeuchi's information criterion when the model is miss-specified.

Usage

## S3 method for class 'maxstab':
TIC(object, \dots)
## S3 method for class 'spatgev':
TIC(object, \dots)

Arguments

object

An object of class maxstab or spatgev. Often, it will be the output of the fitmaxstab or fitspatgev function.

...

Additional objects of class maxstab or spatgev for which TIC should be computed.

Value

• Numeric.

Details

TIC is like AIC so that when comparing models one wants to get the lowest TIC score.

References

Varin, C. and Vidoni, P. (2005) A note on composite likelihood inference and model selection. Biometrika 92(3):519--528.

See Also

fitmaxstab, AIC

Examples

require(RandomFields)

##Define the coordinate of each location
n.site <- 50
locations <- matrix(runif(2*n.site, 0, 100), ncol = 2)
colnames(locations) <- c("lon", "lat")

##Simulate a max-stable process - with unit Frechet margins
ms0 <- MaxStableRF(locations[,1], locations[,2], grid=FALSE, model="stable",
                   param=c(0,1,.2,30, .5), maxstable="extr",
                   n = 40)
ms0 <- t(ms0)
M0 <- fitmaxstab(ms0, locations, "powexp", std.err.type = "score",
                     fit.marge = FALSE)
M1 <- fitmaxstab(ms0, locations, "cauchy", std.err.type = "score",
                     fit.marge = FALSE)
TIC(M0, M1)