Introduction

Date:	April 21, 2012
Authors:	Whit Armstrong
Contact:	[email protected]
Web site:	http://github.com/armstrtw/rcppbugs
License:	GPL-3

Purpose

rcppbugs is an interface to the CppBugs c++ library designed for MCMC sampling.

Features

rcppbugs attempts to make writing mcmc models as painless as possible. It incorporates features from both WinBugs and PyMC. Users define normal R objects to represent the nodes of their model. Deterministic nodes are represented by R functions with an accompanying argument list.

• rcppbugs makes heavey use of Armadillo (http://arma.sourceforge.net/) which allows for very fast matrix algebra. Heavy use of templates allows the code to be very generic and easily extended. Basic statistical distributions are supported in this release and many more will be implemented. Eventually the packages should be as feature complete as WinBugs and PyMC.

Usage

Here is a simple example of a linear model in rcppbugs.

• define the nodes of your model as simple R objects
• implement a function which updates the deterministic variables (or use a shortcut function for basic cases)
• create a model object with 'create.model'
• run your model with the 'run.model' function

library(rcppbugs)

## set up the test data
NR <- 1e2L
NC <- 2L
y <- matrix(rnorm(NR,1) + 10,nr=NR,nc=1L)
X <- matrix(nr=NR,nc=NC)
X[,1] <- 1
X[,2] <- y + rnorm(NR)/2 - 10

## run a normal linear model w/ lm.fit to check results
lm.res <- lm.fit(X,y)
print(coef(lm.res))

## RCppBugs Model
b <- mcmc.normal(rnorm(NC),mu=0,tau=0.0001)
tau.y <- mcmc.gamma(sd(as.vector(y)),alpha=0.1,beta=0.1)
y.hat <- deterministic(function(X,b) { X %*% b }, X, b)
y.lik <- mcmc.normal(y,mu=y.hat,tau=tau.y,observed=TRUE)
m <- create.model(b, tau.y, y.hat, y.lik)

## run the model
cat("running model...\n")
runtime <- system.time(ans <- run.model(m, iterations=1e5L, burn=1e4L, adapt=1e3L, thin=10L))
print(apply(ans[["b"]],2,mean))

print(runtime)

Below the same model is fit using the 'linear' shortcut, which simply implements the operation X %*% b in templated Armadillo.

library(rcppbugs)

## set up the test data
NR <- 1e2L
NC <- 2L
y <- matrix(rnorm(NR,1) + 10,nr=NR,nc=1L)
X <- matrix(nr=NR,nc=NC)
X[,1] <- 1
X[,2] <- y + rnorm(NR)/2 - 10

## run a normal linear model w/ lm.fit to check results
lm.res <- lm.fit(X,y)
print(coef(lm.res))

## RCppBugs Model
b <- mcmc.normal(rnorm(NC),mu=0,tau=0.0001)
tau.y <- mcmc.gamma(sd(as.vector(y)),alpha=0.1,beta=0.1)
y.hat <- linear(X,b)
y.lik <- mcmc.normal(y,mu=y.hat,tau=tau.y,observed=TRUE)
m <- create.model(b, tau.y, y.hat, y.lik)

## run the model
cat("running model...\n")
runtime <- system.time(ans <- run.model(m, iterations=1e5L, burn=1e4L, adapt=1e3L, thin=10L))
print(apply(ans[["b"]],2,mean))

print(runtime)