pred.TAAG {TAG}

Prediction from the TAAG Process

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)