Various intermediate expressions needed by the multivariate emulator
1 2 3 4 5 6 7 8  regressor(x,LoF)
beta_hat(expt,hp,LoF, ...)
betahat_mult(H, Sigmainv, d)
betahat_mult_Sigma(H, Sigma, d)
cstar(x1, x2=x1 , expt, hp, LoF = NULL, Sigmainv=NULL, ...)
eq2.36(H, Sigmainv, d, log=TRUE)
eq2.36_Sigma(H, Sigma, d)
var.matrix(x1,x2=x1,hp, ...)

x,x1,x2 
Objects of class 
H 
Matrix of regressors (create this with 
d 
Vector of observations, possibly not all of the same dimensions (eg some elements might be Kelvin, others millimeters of rain per year) 
expt 
Object of class 
Sigma 
The variance matrix of 
log 
Boolean, with 
Sigmainv 
The inverse of the variance matrix of 
LoF 
A list of functions with default 
hp 
Object of class 
... 
Extra arguments which are
passed (via 
Function regressor()
creates a (sort of) direct sum of
regressor matrices for an overall regressor matrix. It returns a
matrix whose rows are the regressor functions for each row in the
df
argument. Each type of observation has its own
‘slot’ of columns, the others being filled with zeros.
The emulator package should have used this method (rather than
messing about with regressor.basis()
and
regressor.multi()
).
To get the regression coefficients, the user should use function
beta_hat()
, which is the userfriendly version. It is a
wrapper for function betahat_mult_Sigma()
.
The equation for var.matrix()
is
ommittedsee a LaTeXed file
Robin K. S. Hankin
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  data(mtoys)
H < regressor(toy_mm, toy_LoF)
Sigma < var.matrix(toy_mm, hp=toy_mhp)
Sigmainv < solve(Sigma)
jj < toy_mm_maker(34,35,36)
expt < experiment(jj,obs_maker(jj,toy_mhp,toy_LoF,toy_beta))
x1 < jj[c(20,40,100),]
xold(x1) < 0.2
x2 < jj[c(11,21:24,40:42),]
xold(x2) < xold(x2)+0.1
#primary function of package:
multem(x=x1, expt, hp=toy_mhp, LoF=toy_LoF)
# conditional covariance matrix:
cstar(x1,x2, expt, hp=toy_mhp, LoF=toy_LoF)

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