Description Usage Arguments Details Author(s) References Examples
Returns estimator for a priori sigma^2
1 |
H |
Regression basis function (eg that returned by |
Ainv |
inv(A) where A is a correlation matrix (eg that
returned by |
d |
Vector of data points |
s0 |
Optional offset |
fast.but.opaque |
Boolean, with default |
See O'Hagan's paper (ref below), equation 12 for details and context.
Robin K. S. Hankin
A. O'Hagan 1992. “Some Bayesian Numerical Analysis”, pp345-363 of Bayesian Statistics 4 (ed J. M. Bernardo et al), Oxford University Press
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | # example has 10 observations on 6 dimensions.
# function is just sum( (1:6)*x) where x=c(x_1, ... , x_2)
data(toy)
val <- toy
colnames(val) <- letters[1:6]
H <- regressor.multi(val)
d <- apply(H,1,function(x){sum((0:6)*x)})
# create A matrix and its inverse:
A <- corr.matrix(val,scales=rep(1,ncol(val)))
Ainv <- solve(A)
# add some suitably correlated noise:
d <- as.vector(rmvnorm(n=1, mean=d, 0.1*A))
# now evaluate s.chi():
s.chi(H, Ainv, d)
# assess accuracy:
s.chi(H, Ainv, d, fast=TRUE) - s.chi(H, Ainv, d, fast=FALSE)
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