Description Usage Arguments Details Value References See Also Examples
Performs the final re-sampling step in the Bayesian Melding with IMIS procedure for the eight Heligman-Pollard parameters.
1 2 3 | final.resamp(K, B1, H.new, H.k, log.like, d.keep, prior, h.mu, h.sig,
nrisk, ndeath, B = 400, theta.dim = 8, age = c(1e-05, 1,
seq(5, 100, 5)))
|
K |
The number of iterations of the importance sampling stage |
B1 |
sample size at the importance sampling stage multiplied by the number of local optimums |
H.new |
A matrix with dimensions (B*d.keep) x 8 containing the B*d.keep inputs drawn from the multivariate gaussians |
H.k |
A matrix containing the prior plus new inputs from the multivariate gaussians |
log.like |
A vector of log-likelihoods corresponding to each row of H.k |
d.keep |
The number of local optimums found in the optimizer step |
prior |
A matrix containing the prior |
h.mu |
A d.keep x 8 matrix containing the results of the optimizer step |
h.sig |
An array containing the covariance matrix for each row of h.mu |
nrisk |
A vector containing the number of persons at risk in each age group |
ndeath |
A vector containing the number of deaths in each age group |
B |
sample size at the importance sampling stage |
theta.dim |
The number of columns of the prior matrix |
age |
A vector containing the ages at which each age interval begins |
The function, hp.bm.imis, will perform this along with all other steps in a single function
H.new |
A |
vwts |
A vector containing the variance of the rescaled weights at each IMIS iteration |
ewts |
A vector containing the entropy of the rescaled weights at each IMIS iteration |
mwts |
A vector containing the maximum of the rescaled weights at each IMIS iteration |
nup |
A vector containing the expected number of unique points at each IMIS iteration |
frac.up |
A vector containing the proportion of unique points in the final resample at each IMIS iteration |
wts.k |
A vector containing the importance weights for the final iteration |
mwt.case |
The maximum weight value and associated case |
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.
Poole, David and Adrian Raftery. 2000. "Inference for Deterministic Simulation Models: The Bayesian Melding Approach." Journal of the American Statistical Association 95:1244–1255.
Heligman, Larry and John H. Pollard. 1980 "The Age Pattern of Mortality." Journal of the Institute of Actuaries 107:49–80.
loop.optim
, samp.postopt
, like.resamp
, hp.bm.imis
, entropy.wts
, expt.upts
, var.rwts
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | ## Not run:
data(HPprior)
lx
dx
summary(q0)
age
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$log.like.0
wts.0 <- opt.result$wts.0
samp.po <- samp.postopt(opt.cov.d = opt.cov.d, opt.mu.d = opt.mu.d,
prior = q0, d.keep = d.keep)
H.k <- samp.po$H.k
H.new <- samp.po$H.new
B1 <- samp.po$B1
ll.postopt <- like.resamp(K = 10, log.like.0 = log.like.0,
opt.cov.d = opt.cov.d, opt.mu.d = opt.mu.d, d.keep = d.keep)
h.mu <- ll.postopt$h.mu
h.sig <- ll.postopt$h.sig
log.like <- ll.postopt$log.like
K <- ll.postopt$K
result <- final.resamp(K = K, B1 = B1, H.new = H.new, H.k = H.k,
log.like = log.like, d.keep = d.keep, prior = q0,
h.mu = h.mu, h.sig = h.sig, nrisk=lx, ndeath=dx, age=age)
## End(Not run)
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