BayHaz-package {BayHaz}R Documentation

R Functions for Bayesian Hazard Rate Estimation

Description

A suite of R functions for Bayesian estimation of smooth hazard rates via Compound Poisson Process (CPP) priors.

Details

Package: BayHaz
Type: Package
Version: 0.0-6
Date: 2007-01-09
License: GPL Version 2 or later

This package provides UseRs with functions to use CPP prior distributions for Bayesian analysis of times to event; see La Rocca (2005). It deals with prior elicitation, posterior computation, and visualization. For illustrative purposes, a data set in the field of earthquake statistics is supplied. Package 'coda' is suggested for output diagnostics.

Author(s)

Luca La Rocca http://www-dimat.unipv.it/luca

Mantainer: Luca La Rocca luca.larocca@unimore.it

References

Luca La Rocca (2005). On Bayesian Nonparametric Estimation of Smooth Hazard Rates with a View to Seismic Hazard Assessment. Research Report n. 38-05, Department of Social, Cognitive and Quantitative Sciences, Reggio Emilia, Italy.

See Also

CPPpriorElicit, CPPpostSample, CPPplotHR, earthquakes, CPPpost2mcmc

Examples

# set RNG seed (for example reproducibility only)
set.seed(1234)

# select a CPP prior distribution (with default number of CPP jumps)
hypars<-CPPpriorElicit(r0 = 0.1, H = 1, T00 = 50, M00 = 2, extra = 0)

# plot some sample prior hazard rates
CPPplotHR(CPPpriorSample(ss = 10, hyp = hypars), tu = "Year")

# load a data set
data(earthquakes)

# generate a posterior sample
post<-CPPpostSample(hypars, times = earthquakes$ti, obs = earthquakes$ob)

# check that no additional CPP jumps are needed:
# if this probability is not negligible,
# go back to prior selection stage and increase 'extra'
ecdf(post$sgm[,post$hyp$F])(post$hyp$T00+3*post$hyp$sd)

# plot some posterior hazard rate summaries
CPPplotHR(post , tu = "Year")

# save the posterior sample to file for later use
save(post, file = "post.rda")

# convert the posterior sample into an MCMC object
post<-CPPpost2mcmc(post)

# take advantage of package 'coda' for output diagnostics
pdf("diagnostics.pdf")
traceplot(post)
autocorr.plot(post, lag.max = 5)
par(las = 2) # for better readability of the cross-correlation plot
crosscorr.plot(post)
dev.off()

[Package BayHaz version 0.0-6 Index]