Nothing

```
#############
# Simulated example
#############
library("SAVE")
#######
# load data
#######
data(synthfield, package = "SAVE")
data(synthmodel, package = "SAVE")
##############
# create the SAVE object which describes the problem and
# compute the corresponding mle estimates
##############
synth <- SAVE(response.name = "y", controllable.names = "x", calibration.names = "v",
field.data = synthfield, model.data = synthmodel, mean.formula = ~1 + x, bestguess = list(v = 1.5))
##############
# Bayesian fit
##############
set.seed(0)
synth <- bayesfit(object = synth, prior = uniform(var.name = "v", lower = 0, upper = 3),
n.iter = 20000)
summary(synth)
# Fig 9 Histogram of the posterior for v
plot(synth, option = "calibration")
# Fig 10 Histogram of the precisions
plot(synth, option = "precision")
#############
# validate at a grid of x points
##############
xnew <- data.frame(x = seq(from = 0.05, to = 3.05, length = 25))
valsynth <- validate(object = synth, newdesign = xnew, n.burnin = 100)
# summary and plot of the validation process
summary(valsynth)
plot(valsynth)
# Fig 11 A plot to compare the bias corrected prediction, the actual reality
# curve the computer model evaluated at the posterior mean and at the least
# square estimate
par(mfrow = c(1, 1))
a <- 0
b <- 5
av.real <- (valsynth@validate)[, "bias.corrected"]
delta <- (valsynth@validate)[, "tau.bc"]
# Bias corrected prediction and tolerance bars
plot(xnew$x, av.real, ty = "n", ylim = c(a, b), main = "Predictions", xlab = "x",
ylab = "y")
lines(xnew$x, av.real, lty = 1)
lines(xnew$x, av.real + delta, lty = 2)
lines(xnew$x, av.real - delta, lty = 2)
points(synthfield$x, synthfield$y, pch = "*")
# Representation of reality
yR <- function(x) {
3.5 * exp(-1.7 * x) + 1.5
}
lines(xnew$x, yR(xnew$x), col = 2)
# Representation of Computer model at the least square estimate
yM <- function(x) {
5 * exp(-x[1] * x[2])
}
vls <- 0.63
tmp <- cbind(xnew$x, vls)
lines(xnew$x, apply(tmp, 1, FUN = yM), col = 3)
# Representation of Computer model at the posterior mean
vmean <- mean(synth@mcmcsample[-(1:100), "v"])
tmp <- cbind(xnew$x, vmean)
lines(xnew$x, apply(tmp, 1, yM), col = 4)
```

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