sample.n.fit {emulator} | R Documentation |
Sample from a Gaussian process and fit an emulator to the points
Description
Sample 'n' fit: sample from an appropriate multivariate Gaussian process in one dimension, then fit an emulator to it.
Usage
sample.n.fit(n = 10, scales.generate = 100, scales.fit = 100, func = regressor.basis, ...)
Arguments
n |
Number of observations to make |
scales.generate |
Scales to generate the data with: small values give uncorrelated observations, large values give correlated observations (hence the points fall on a smooth line) |
scales.fit |
Scales to use to fit the emulator. Small values give an emulator that is the prior with short, sharp excursions to make the emulator go through the points; large values give smooth emulators that exhibit overshoots resembling Gibbs's phenomenon |
func |
Function used to determine basis vectors, defaulting
to |
... |
Further arguments passed to |
Details
The point of this function is to investigate what happens when
inappropriate scales are used for the emulator: that is, when
scales.generate
and scales.fit
are wildly different.
Note that the sampling distribution has a constant expectation (of zero); so the prior should be zero, making it easy to see mispredictions of beta.
Author(s)
Robin K. S. Hankin
Examples
sample.n.fit(main="Default: scales match")
sample.n.fit(scales.generate=5,main="generate scale small")
# Now use a quadratic function instead of the default linear:
f <- function(x){out <- c(1,x,x^2)
names(out) <- c("const","linear","quadratic")
out}
sample.n.fit(main="quadratic prior" , func=f)