LinkedGASP (version 1.0)

NGASPmetrics: GASP performance assessment measures

Description

Evaluates frequentist performance of the GASP.

Usage

NGASPmetrics(GASP, true_output, ref_output)

Arguments

GASP

GASP emulator.

true_output

Output from the simulator.

ref_output

Heuristic emulator output.

Value

List of performance measures.

RMSPE_base

Root mean square predictive error with respect to the heuristic emulator output.

RMSPE

Root mean square predictive error for the emulator output

ratio

ratio of RMSPE_base to RMSPE. Ratio = RMSPE_base/RMSPE

CIs

95% central credible intervals

emp_cov

95% empirical coverage within the CIs

length_CIs

Average lenght of 95% central credible intervals

References

Ksenia N. Kyzyurova, James O. Berger, and Robert L. Wolpert. Coupling computer models through linking their statistical emulators. SIAM/ASA Journal on Uncertainty Quantification, 6(3): 1151-1171, 2018

Examples

# NOT RUN {
## Function f1 is a simulator
f1<-function(x){sin(pi*x)}

## One-dimensional inputs are x1
x1 <- seq(-1,1,.37)

## The following contains the list of data inputs (training) and outputs (fD) together with 
## the assumed fixed smoothness of a computer model output.
data.f1 <- list(training = x1,fD = f1(x1), smooth = 1.99)

## Evaluation of GASP parameters
f1_MLEs = eval_GASP_RFP(data.f1,list(function(x){x^0},function(x){x^1}),1,FALSE)

## Evaluate the emulator
xn = seq(-1,1,.01)
GASP_type2_f1 <- eval_type2_GASP(as.matrix(xn),f1_MLEs)

## Plot the emulator
par(mar = c(6.1, 6.1, 5.1, 2.1))
GASP_plot(GASP_type2_f1,data = data.f1,emul_type = "",ylim = ylim, plot_training = TRUE)

## Measure performance of an emulator
NGASPmetrics(GASP_type2_f1,f1(xn),mean(f1(xn)))
# }