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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