| Type: | Package |
| Title: | Bayesian Model to Find Changepoints Based on Rates and Count Data |
| Version: | 1.0.4 |
| Author: | Andrew G. Chapple |
| Maintainer: | Andrew G. Chapple <agc6@rice.edu> |
| Description: | This function fits a reversible jump Bayesian piecewise exponential model that also includes the intensity of each event considered along with the rate of events. |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | Rcpp (≥ 0.12.9) |
| LinkingTo: | Rcpp, RcppArmadillo |
| RoxygenNote: | 6.0.1 |
| NeedsCompilation: | yes |
| Packaged: | 2018-04-24 18:00:14 UTC; Andrew |
| Repository: | CRAN |
| Date/Publication: | 2018-04-24 18:10:19 UTC |
Runs the PieceExpIntensity sampler and returns posterior results.
Description
Returns a list of posterior samples along with summaries for the most visited number of split points.
Usage
PieceExpIntensity(X, Y, B, Poi)
Arguments
X |
Vector containing observed event times. |
Y |
Vector containing poisson count intensities. |
B |
Number of iterations to run the MCMC with half burned in. |
Poi |
Prior mean number of split points. |
Value
A list of all posterior quantities and a summary of the most commonly visited model.
References
Chapple (2017). Modeling ISIL terror attacks and their intensities via flexible Bayesian piecewise models. Currently Under Review.
Examples
B=1000
n=100
X=rexp(n,1)
Y=X
Y[X<.5]=rpois(sum(X<.5),20)
Y[X>.5]=rpois(sum(X>.5),3)
Poi=10
PieceExpIntensity(X,Y,B,Poi)
C++ Sampling Function for MCMC
Description
C++ Sampling Function used in the PieceExpIntensity function.
Usage
PieceExpIntensity2(Y, Rates, B, Poi)
Arguments
Y |
Vector containing observed event times. |
Rates |
Vector containing poisson count intensities. |
B |
Number of iterations to run the MCMC with half burned in. |
Poi |
Prior mean number of split points, |
Value
A list of all posterior quantities.
Examples
B=1000
n=100
Y=rexp(n,1)
Rates=Y
Rates[Y<.5]=rpois(sum(Y<.5),20)
Rates[Y>.5]=rpois(sum(Y>.5),3)
Poi=10
PieceExpIntensity2(Y,Rates,B,Poi)