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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