check.TAAG: Check TAAG

In TAG: Transformed Additive Gaussian Processes

Description

This function provides a table for checking whther the TAAG process fits the data well better than an ordinary kriging model.

Usage

check.TAAG(object)

Arguments

object

object of class inheriting from "TAAG".

Value

A table of the fitted negative likelihood values and cross validation errors obtained from TAAG and the ordinary kriging models from dicekriging and mlegp is returned. Note that, for both criteria, the model with a smaller value are a better model.

References

Lin, L.-H. and Joseph, V. R. (2020) "Transformation and Additivity in Gaussian Processes",Technometrics, 62, 525-535. DOI:10.1080/00401706.2019.1665592.

See Also

TAAG for the estimates of the parameters in the TAG and TAAG, respectively.

Examples

n <- 20
p <- 2
library(randtoolbox)
X <-  sobol(n, dim = p, init = TRUE, scrambling = 2, seed = 20, normal = FALSE)
y <- exp(2*sin(0.5*pi*X[,1]) + 0.5*cos(2.5*pi*X[,2]))
ini.TAG <- initial.TAG(y, X)
par.TAG <- TAG(ini.TAG)
N <- 1000
X.test <- sobol(N, dim = p, init = TRUE, scrambling = 2, seed = 5, normal = FALSE)
ytrue  <- exp(2*sin(0.5*pi*X.test[,1]) + 0.5*cos(2.5*pi*X.test[,2]))
pre.TAG <- pred.TAG(par.TAG, X.test)
library(DiceKriging)
set.seed(2)
temp.m <- km(formula=~1, design=X, response=par.TAG$ty,
             covtype="gauss",nugget = (10^-15), multistart = 4,
             control = list(trace = FALSE))
nu.est <- sqrt(2*(coef(temp.m)$range^2))
par.TAAG <- TAAG(par.TAG, nu.est)

check.table <- check.TAAG(par.TAAG)
check.table

TAG documentation built on June 8, 2021, 1:06 a.m.