p.page4 {calibrator} | R Documentation |
Function to determine a postiori probability of hyperparameters rho, lambda and psi2, given observations and psi1.
p.page4(D1, D2, H1, H2, V, y, z, E.theta, Edash.theta, extractor, include.prior=FALSE, lognormally.distributed=FALSE, return.log=FALSE, phi)
D1 |
Matrix of code run points |
D2 |
Matrix of observation points |
H1 |
Basis function (vectorized) |
H2 |
Regression function for D2 |
V |
Covariance matrix; default value of NULL results in
the function evaluating it (but this takes a long time, so supply
V if known) |
y |
Vector of code outputs |
z |
Vector of observation values |
E.theta |
Expectation over theta |
Edash.theta |
Expectation over theta WRT E' |
extractor |
Function to extract independent variables and parameters from D1 |
include.prior |
Boolean, with TRUE
meaning to include the prior PDF for theta and default
value of FALSE meaning to return the likelihood multiplied by an
undetermined constant |
lognormally.distributed |
Boolean with TRUE meaning to assume
lognormality. See prob.psi1 for details |
return.log |
Boolean, with default FALSE meaning to return
the probability, and TRUE meaning to return the (natural)
logarithm of the probability (which is useful when considering very
small probabilities) |
phi |
Hyperparameters |
Robin K. S. Hankin
data(toys) p.page4(D1=D1.toy, D2=D2.toy, H1=H1.toy, H2=H2.toy, V=NULL, y=y.toy, z=z.toy,E.theta=E.theta.toy, Edash.theta=Edash.theta.toy, extractor=extractor.toy, phi=phi.toy) ## Now compare the above value with p.page4() calculated with phi ## differing only in psi2: phi.toy.new <- phi.change(phi.fun=phi.fun.toy, old.phi = phi.toy, psi2=c(8,8,8)) p.page4(D1=D1.toy, D2=D2.toy, H1=H1.toy, H2=H2.toy, V=V.toy, y=y.toy, z=z.toy, E.theta=E.theta.toy, Edash.theta=Edash.theta.toy, extractor=extractor.toy, phi=phi.toy.new) ## different!