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”, pp345363 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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