simulate {RobustGaSP}R Documentation

Sample for Robust GaSP model

Description

Function to sample Robust GaSP after the Robust GaSP model has been constructed.

Usage

## S4 method for signature 'rgasp'
simulate(object, testing_input, num_sample=1,
testing_trend= matrix(1,dim(testing_input)[1],1),...)

Arguments

object

an object of class rgasp.

testing_input

a matrix containing the inputs where the rgasp is to sample.

num_sample

number of samples one wants.

testing_trend

a matrix of mean/trend for prediction.

...

Extra arguments to be passed to the function (not implemented yet).

Value

The returned value is a matrix where each column is a sample on the prespecified inputs.

Author(s)

Mengyang Gu [aut, cre], Jesus Palomo [aut], James Berger [aut]

Maintainer: Mengyang Gu <mgu6@jhu.edu>

References

M. Gu. (2016). Robust uncertainty quantification and scalable computation for computer models with massive output. Ph.D. thesis. Duke University.

Examples

  #------------------------
  # a 1 dimensional example
  #------------------------
  
###########1dim higdon.1.data 
p1 = 1     ###dimensional of the inputs
dim_inputs1 <- p1
n1 = 15   ###sample size or number of training computer runs you have 
num_obs1 <- n1
input1 = 10*matrix(runif(num_obs1*dim_inputs1), num_obs1,dim_inputs1) ##uniform
#####lhs is better
#library(lhs)
#input1 = 10*maximinLHS(n=num_obs1, k=dim_inputs1)  ##maximin lhd sample
output1 = matrix(0,num_obs1,1)
for(i in 1:num_obs1){
  output1[i]=higdon.1.data (input1[i])
}




m1<- rgasp(design = input1, response = output1, lower_bound=FALSE)

#####locations to samples
testing_input1 = seq(0,10,1/50) 
testing_input1=as.matrix(testing_input1)
#####draw 10 samples
m1_sample=simulate(m1,testing_input1,num_sample=10)

#####plot these samples
matplot(testing_input1,m1_sample, type='l',xlab='input',ylab='output')
lines(input1,output1,type='p')



[Package RobustGaSP version 0.5.7 Index]