Description Details Author(s) References See Also Examples

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

Package: | BayHaz |

Type: | Package |

Version: | 0.1-3 |

Date: | 2007-10-07 |

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 also handles first order autoregressive BPS hazard rates, based on Hennerfeind *et al.* (2006).
Prior elicitation, posterior computation, and visualization are dealt with. 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

La Rocca, L. (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.

Hennerfeind, A., Brezger, A. \& Fahrmeir, L. (2006). Geoadditive survival models.
*Journal of the American Statistical Association* 101, 1065–1075.

`CPPpriorElicit`

, `CPPpostSample`

, `CPPplotHR`

,
`BPSpriorElicit`

, `BPSpostSample`

, `BPSplotHR`

,
`earthquakes`

, `CPPpost2mcmc`

, `BPSpost2mcmc`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | ```
# the following analysis uses CPP hazard rates but can be easily adapted to BPS hazard rates
# 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()
``` |

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