bhm.constr.resamp: Markov Chain Monte Carlo Estimation (Step 2) of the Bayesian...

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/mederrRank.R

Description

This function represents the "constructor" function for the resampling procedure used in this package. bhm.resample calculates the importance ratios, and performs the sampling, and then this function constructs the resampled model based on that information.

Usage

1
bhm.constr.resamp(model, resample, k, eta)

Arguments

model

an object of class "mederrFit".

resample

an object of class "mederrResample".

k

k (number of degrees of freedom) value to use in the resampling procedure.

eta

η (skewing paramter) value to use in the resampling procedure.

Details

Deviations from the normal, i.e. (k = ∞, η = 1), random effects distribution using a different pair of k and η values are considered. The methodology implemented here is the importance link function resampling approach introduce by MacEachern and Peruggia (2000): based on the (k = ∞, η = 1) chain, new posterior samples under a new set of (k, η) values is obtained.

Value

bhm.constr.resamp returns an object of the class "mederrFit".

Author(s)

Sergio Venturini [email protected],

Jessica A. Myers [email protected]

References

MacEachern, S. and Peruggia, M. (2000), "Importance Link Function Estimation for Markov Chain Monte Carlo Methods", Journal of Computational and Graphical Statistics, 9, 99-121.

Myers, J. A., Venturini, S., Dominici, F. and Morlock, L. (2011), "Random Effects Models for Identifying the Most Harmful Medication Errors in a Large, Voluntary Reporting Database". Technical Report.

See Also

bhm.mcmc, bhm.resample, mederrData, mederrFit.

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
## Not run: 
data("simdata", package = "mederrRank")
summary(simdata)

fit <- bhm.mcmc(simdata, nsim = 1000, burnin = 500, scale.factor = 1.1)
resamp <- bhm.resample(fit, simdata, p.resample = .1,
	k = c(3, 6, 10, 30, 60, Inf), eta = c(.5, .8, 1, 1.25, 2))
fit2 <- bhm.constr.resamp(fit, resamp, k = 3, eta = .8)
plot(fit, fit2, simdata)

theta0 <- c(10, 6, 100, 100, .1)
ans <- mixnegbinom.em(simdata, theta0, 50000, 0.01, se = TRUE,
	stratified = TRUE)

summary(fit2, ans, simdata)

## End(Not run)

mederrRank documentation built on May 2, 2019, 11:27 a.m.