p.eqn8.supp {calibrator} | R Documentation |
Function to determine the a-postiori probability of hyperparameters rho, lambda and psi2, given observations and psi1.
p.eqn8.supp(theta, D1, D2, H1, H2, d, include.prior=FALSE, lognormally.distributed=FALSE, return.log=FALSE, phi) p.eqn8.supp.vector(theta, D1, D2, H1, H2, d, include.prior=FALSE, lognormally.distributed=FALSE, return.log=FALSE, phi)
theta |
Parameters |
D1 |
Matrix of code run points |
D2 |
Matrix of observation points |
H1 |
Regression function for D1 |
H2 |
Regression function for D2 |
d |
Vector of code output values and observations |
include.prior |
Boolean, with TRUE
meaning to include the prior PDF for theta and default
FALSE meaning return the likelihood, multiplied by an
undetermined constant |
lognormally.distributed |
Boolean, with TRUE meaning to
assume prior is lognormal (see prob.theta() for more info) |
return.log |
Boolean, with default FALSE meaning to return
the probability; TRUE means to return the (natural) logarithm
of the answer |
phi |
Hyperparameters |
The user should always use p.eqn8.supp()
, which is a wrapper
for p.eqn8.supp.vector()
. The forms differ in their treatment
of theta. In the former, theta must be a
vector; in the latter, theta may be a matrix, in which
case p.eqn8.supp.vector()
is applied to the rows
Robin K. S. Hankin
data(toys) p.eqn8.supp(theta=theta.toy, D1=D1.toy, D2=D2.toy, H1=H1.toy, H2=H2.toy, d=d.toy, phi=phi.toy) ## Now try using the true hyperparameters, and data directly drawn from ## the appropriate multivariate distn: phi.true <- phi.true.toy(phi=phi.toy) jj <- create.new.toy.datasets(D1.toy , D2.toy) d.toy <- jj$d.toy p.eqn8.supp(theta=theta.toy, D1=D1.toy, D2=D2.toy, H1=H1.toy, H2=H2.toy, d=d.toy, phi=phi.true) ## Now try p.eqn8.supp() with a vector of possible thetas: p.eqn8.supp(theta=sample.theta(n=11,phi=phi.true), D1=D1.toy, D2=D2.toy, H1=H1.toy, H2=H2.toy, d=d.toy, phi=phi.true)