# leave_one_out_rgasp: leave-one-out fitted values and standard deviation for robust... In RobustGaSP: Robust Gaussian Stochastic Process Emulation

## Description

A function to calculate leave-one-out fitted values and the standard deviation of the prediction on robust GaSP models after the robust GaSP model has been constructed.

## Usage

 `1` ```leave_one_out_rgasp(object) ```

## Arguments

 `object` an object of class `rgasp`.

## Value

A list of 2 elements with

 `mean ` leave one out fitted values. `sd ` standard deviation of each prediction.

## Author(s)

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

Maintainer: Mengyang Gu <[email protected]>

## References

Mengyang Gu. (2016). Robust Uncertainty Quantification and Scalable Computation for Computer Models with Massive Output. Ph.D. thesis. Duke University.

`rgasp`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37``` ```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 ```