library(RobustGaSP)
#------------------------
# a 3 dimensional example
#------------------------
# dimensional of the inputs
dim_inputs <- 3
# number of the inputs
num_obs <- 30
# uniform samples of design
input <- matrix(runif(num_obs*dim_inputs), num_obs,dim_inputs)
# Following codes use maximin Latin Hypercube Design, which is typically better than uniform
# library(lhs)
# input <- maximinLHS(n=num_obs, k=dim_inputs) ##maximin lhd sample
####
# outputs from the 3 dim dettepepel.3.data function
output = matrix(0,num_obs,1)
for(i in 1:num_obs){
output[i]<-dettepepel.3.data (input[i,])
}
# use constant mean basis, with no constraint on optimization
m1<- rgasp(design = input, response = output, lower_bound=FALSE)
##leave one out predict
leave_one_out_m1=leave_one_out_rgasp(m1)
##predictive mean
leave_one_out_m1$mean
##standard deviation
leave_one_out_m1$sd
##standardized error
(leave_one_out_m1$mean-output)/leave_one_out_m1$sd
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