Various intermediate expressions needed by the multivariate emulator
Various intermediate expressions needed by the multivariate emulator
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 user-friendly version. It is a
wrapper for function betahat_mult_Sigma().
The equation for var.matrix() is
Robin K. S. Hankin
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)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.