emul.predict: To predict using an emulator (Deprecated)
In scrim-network/Stilt-Rpkge: Separable Gaussian Process Interpolation (Emulation)

Description Usage Arguments Details Value Note References See Also Examples

To predict using an emulator. This function is deprecated. Use predict.emul instead.

1	emul.predict(emul, theta.star)

`emul`	Standard emulator object, as output, for example, by the 'emulator' function.
`theta.star`	Parameter setting at which to predict. Must have the same number of elements as there are columns in the `emul$Theta.mat`. Vector (or, for a 1 parameter ensemble, a single numeric value).

Emulator prediction follows standard formulation in Gaussian Process theory. For more details, see References.

Prediction at the parameter setting theta.star for all times specified by emul$t.vec. List with components

`mean`	Posterior mean. n*1 matrix. Rows correspond to the times of `emul$t.vec`.
`covariance`	Posterior covariance matrix. n*n matrix. Diagonal elements represent variances at each time in `emul$t.vec`.

Prediction outside the emulator range ("extrapolation") is currently not allowed.

R. Olson and W. Chang (2013): Mathematical framework for a separable Gaussian Process Emulator. Tech. Rep., available from
www.scrimhub.org/resources/stilt/Olson_and_Chang_2013_Stilt_Emulator_Technical_Report.pdf.

rsurface.plot, test.csv, emulator, predict.emul

## Not run: 
# Predict using the SICOPOLIS model at a mid-range parameter setting, and plot
# the prediction and associated uncertainty
data(emul.Sicopolis)
pred <- emul.predict(emul.Sicopolis, c(3, 10, 50, 3, 12.5))
plot.default(NA, xlim=range(emul.Sicopolis$t.vec),
     ylim=range(pred$mean), xlab="Year",
     ylab="Ice Mass Loss relative to year 2003")
lines(emul.Sicopolis$t.vec, pred$mean, col="brown", lwd=3)
std <- sqrt(diag(pred$covariance))
lines(emul.Sicopolis$t.vec, pred$mean + std, col="brown", lty=2)
lines(emul.Sicopolis$t.vec, pred$mean - std, col="brown", lty=2)


# Fit an emulator to the 1-parameter test ensemble data, predict at
# Theta*=8, and plot the prediction
data(Data.1D.par)
data(Data.1D.model)
emul.1D <- emulator(Data.1D.par, Data.1D.model, TRUE, TRUE, 100, 0.1)
pred.1D <- emul.predict(emul.1D, 8)
plot(emul.1D$t.vec, pred.1D$mean, xlab="Year",
     ylab="Sample Model Output at Theta*=8")

## End(Not run)