bhm.constr.resamp | R Documentation |
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.
bhm.constr.resamp(model, resample, k, eta)
model |
an object of class "mederrFit". |
resample |
an object of class "mederrResample". |
k |
|
eta |
|
Deviations from the normal, i.e. (k = \infty, \eta = 1)
, random effects distribution using a different pair of k
and \eta
values are considered. The methodology implemented here is the importance link function resampling approach introduce by MacEachern and Peruggia (2000): based on the (k = \infty, \eta = 1)
chain, new posterior samples under a new set of (k, \eta)
values is obtained.
bhm.constr.resamp
returns an object of the class "mederrFit".
Sergio Venturini sergio.venturini@unicatt.it,
Jessica A. Myers jmyers6@partners.org
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.
bhm.mcmc
,
bhm.resample
,
mederrData
,
mederrFit
.
## 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)
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