pred.TAAG: Prediction from the TAAG Process

In TAG: Transformed Additive Gaussian Processes

Description

This function provides predictions from a TAAG process.

Usage

pred.TAAG(object, newX, predict.CI = FALSE,  zalpha = 1.96)

Arguments

object	object of class inheriting from "TAAG".
newX	matrix of new values of x at which predictions are needed.
predict.CI	logical value indicating if the confidence interval at each prediction point is required.
zalpha	normal critical value for the confidence interval. Default is 1.96 for 95 % confidence intervals. The zalpha works only when predict.CI is TRUE.

Value

The function returns predictions at newX and the confidence intervals (if predict.CI is TRUE). If predict.CI is TRUE, the values returned from the function is a list containing:

Prediction	the prediction at newX.
ConfidenceLB	the lower bound of the prediction confidence interval at newX.(Note that the default is 95 %.)
ConfidenceUB	the upper bound of the prediction confidence interval at newX.

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 TAAG.

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 # size of testing samples
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$ty,
   covtype="gauss",nugget = (10^-15), multistart = 4,
   control = list(trace = FALSE, verbose = FALSE))
nu.est <- sqrt(2*(coef(temp.m)$range^2))
par.TAAG <- TAAG(par.TAG, nu.est)
pre.TAAG <- pred.TAAG(par.TAAG, X.test)
mean((pre.TAAG$Prediction-ytrue)^2)

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