TIC {SpatialExtremes} | R Documentation |
Computes a "generalization" of the Takeuchi's information criterion when the model is miss-specified.
## S3 method for class 'maxstab': TIC(object, ...) ## S3 method for class 'spatgev': TIC(object, ...)
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. |
TIC is like AIC so that when comparing models one wants to get the lowest TIC score.
Numeric.
Mathieu Ribatet
Varin, C. and Vidoni, P. (2005) A note on composite likelihood inference and model selection. Biometrika 92(3):519–528.
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)