https://github.com/armstrtw/rcppbugs

R interface for CppBugs
https://github.com/armstrtw/rcppbugs

Last synced: 20 days ago
JSON representation

R interface for CppBugs

• Host: GitHub
• URL: https://github.com/armstrtw/rcppbugs
• Owner: armstrtw
• Created: 2012-04-21T13:28:06.000Z (over 12 years ago)
• Default Branch: master
• Last Pushed: 2012-09-22T02:47:55.000Z (about 12 years ago)
• Last Synced: 2024-07-21T05:36:39.107Z (4 months ago)
• Language: C++
• Size: 294 KB
• Stars: 15
• Watchers: 5
• Forks: 5
• Open Issues: 4
• Metadata Files:
  • Readme: README.rst

Awesome Lists containing this project

README

        
************

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)