Description Usage Arguments Details Value References See Also Examples
Samples the eight Heligman-Pollard parameters from the mvnorm distribution for each run of optimizer step where the likelihood for that run exceeds the maximum likelihood from the prior
1 2 | samp.postopt(opt.cov.d, opt.mu.d, d.keep, prior, B = 400,
B0 = 8000, d = 10)
|
opt.cov.d |
An array containing a covariance matrix for each run of optimizer where the likelihood for that run exceeds the maximum likelihood from the prior |
opt.mu.d |
A matrix containing the results of the optimizer step |
d.keep |
Number of runs of optimizer where the likelihood for that run exceeds the maximum likelihood from the prior |
prior |
A matrix containing the prior distribution (see prior.form and prior.mle) |
B |
sample size at the importance sampling stage |
B0 |
Sample size of the prior. This is equal to |
d |
Number of optimizer iterations |
For use within the function hp.bm.imis
H.k |
The prior plus new samples |
H.new |
The new samples from the multivariate normal |
B1 |
The number of new samples - should be equal to |
Heligman, Larry and John H. Pollard. 1980 "The Age Pattern of Mortality." Journal of the Institute of Actuaries 107:49–80.
Poole, David and Adrian Raftery. 2000. "Inference for Deterministic Simulation Models: The Bayesian Melding Approach." Journal of the American Statistical Association 95:1244–1255.
Raftery, Adrian and Le Bao. 2009. "Estimating and Projecting Trends in HIV/AIDS Gen- eralized Epidemics Using Incremental Mixture Importance Sampling." Technical Report 560, Department of Statistics, University of Washington.
hp.bm.imis
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ## Not run: data(HPprior)
q0 <- HPprior
lx <- c(1974, 1906, 1860, 1844, 1834, 1823, 1793, 1700, 1549, 1361,
1181, 1025, 870, 721, 571, 450, 344, 256, 142, 79, 41, 8)
dx <- c(68, 47, 16, 10, 13, 29, 92, 151, 188, 179, 156, 155, 147, 150,
122, 106, 88, 113, 63, 38, 32, 8)
opt.result <- loop.optim(prior = q0, nrisk=lx, ndeath=dx)
opt.mu.d <- opt.result$opt.mu.d
opt.cov.d <- opt.result$opt.cov.d
theta.new <- opt.result$theta.new
d.keep <- opt.result$d.keep
log.like.0 <- opt.result$log.like.0
wts.0 <- opt.result$log.like.0
samp.po <- samp.postopt(opt.cov.d = opt.cov.d, opt.mu.d = opt.mu.d,
prior = q0, d.keep = d.keep)
## End(Not run)
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