as.S4prediction | R Documentation |
RobustGaSP
S3 object
prediction into a S4 object
This function converts a S3 object
prediction into the default S4 object
prediction
as.S4prediction(object, ...)
object |
an object of type list obtained by |
... |
Extra arguments to be passed to the function (not implemented yet). |
The returned value is a S4 object
of class predrgasp-class
with
call
:call
to the function.
mean
:predictive mean for the testing inputs.
lower95
:lower bound of the 95% posterior credible interval.
upper95
:upper bound of the 95% posterior credible interval.
sd
:standard deviation of each testing_input
.
Mengyang Gu [aut, cre], Jesus Palomo [aut], James Berger [aut]
Maintainer: Mengyang Gu <mengyang@pstat.ucsb.edu>
#------------------------
# 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)
# the following use constraints on optimization
# m1<- rgasp(design = input, response = output, lower_bound=TRUE)
# the following use a single start on optimization
# m1<- rgasp(design = input, response = output, lower_bound=FALSE, multiple_starts=FALSE)
# number of points to be predicted
num_testing_input <- 5000
# generate points to be predicted
testing_input <- matrix(runif(num_testing_input*dim_inputs),num_testing_input,dim_inputs)
# Perform prediction
m1.predict<-predict(m1, testing_input, outasS3 = FALSE)
# Notice the call slot of the object
print(m1.predict@call)
# To convert the prediction to a S3 object
m1.predict.aslist <- as.S3prediction(m1.predict)
# To recover back the prediction as a predrgasp-class object
m1.predict.aspredgasp <- as.S4prediction.predict(m1.predict.aslist)
str(m1.predict.aslist)
# Notice that in this case the @call slot is different than the initial
print(m1.predict.aspredgasp@call)
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