BayHaz-package {BayHaz} | R Documentation |
A suite of R functions for Bayesian estimation of smooth hazard rates via Compound Poisson Process (CPP) priors.
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.
Luca La Rocca http://www-dimat.unipv.it/luca
Mantainer: Luca La Rocca luca.larocca@unimore.it
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.
CPPpriorElicit
, CPPpostSample
, CPPplotHR
,
earthquakes
, CPPpost2mcmc
# 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()