Description Usage Arguments Value Note References See Also Examples
Runs all the necessary functions to estimate the eight Heligman-Pollard parameters in one step via Bayesian Melding with IMIS and optimization. In this order and with the proper arguments imputed the functions run are loop.optim
, samp.postopt
, like.resamp
, final.resamp
.
1 2 | hp.bm.imis(prior, nrisk, ndeath, K, d = 10,
B = 400, age = c(1e-05, 1, seq(5, 100, 5)), CI=95)
|
prior |
A matrix with dimensions 8000 x |
nrisk |
A vector containing the total number of individuals at risk of death in each age group. Length should equal the length of |
ndeath |
A vector containing the total number of deaths in each age group. Length should equal the length of |
K |
The number of IMIS iterations |
d |
The number of optimizer iterations |
B |
The sample size at each importance sampling iteration |
age |
A vector of the ages at which the probabilities of death will be calculated |
CI |
Defines the width of the credible interval (Defaults to 95 percent). A summary table is printed with the median estimate and lower and upper confidence bounds. Setting |
out |
A summary table of the results with the median parameter values in the middle column, the lower bound results in the left column, and upper bound result in the right column |
H.final |
A |
h.mu |
The sets of parameters found in the optimizer step |
h.sig |
The covariance matrix for each set of parameters in |
log.like |
A vector of likelihoods for the prior plus resamples |
log.like.0 |
A vector of the likelihoods for the prior |
wts.0 |
A vector of importance weights for each set of parameters in the prior |
d.keep |
The number of optimizer runs where the likelihood exceeded the maximum likelihood of the prior |
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 |
mwt.case |
The maximum weight and associated case |
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 IMIS iteration |
Because there are multiple sampling steps sometimes with upper and lower bound restricitions, this function can take several minutes to run depending on the sample size, K
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 Generalized Epidemics Using Incremental Mixture Importance Sampling." Technical Report 560, Department of Statistics, University of Washington.
loop.optim
, samp.postopt
, like.resamp
, final.resamp
1 2 3 4 5 6 7 8 9 10 11 | ##a prior##
## Not run: data(HPprior)
q0 <- HPprior
##number of deaths in each age group##
dx <- c(68, 47, 16, 10, 13, 29, 92, 151, 188, 179, 156, 155, 147, 150,
122, 106, 88, 113, 63, 38, 32, 8)
##number at risk in each age group##
lx <- c(1974, 1906, 1860, 1844, 1834, 1823, 1793, 1700, 1549, 1361,
1181, 1025, 870, 721, 571, 450, 344, 256, 142, 79, 41, 8)
result <- hp.bm.imis(prior=q0, K=10, nrisk=lx, ndeath=dx)
## End(Not run)
|
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