Fun with the proto package: building an MCMC sampler for Bayesian regression

The proto package is my latest favourite R goodie. It brings prototype-based programming to the R language – a style of programming that lets you do many of the things you can do with classes, but with a lot less up-front work. Louis Kates and Thomas Petzoldt provide an excellent introduction to using proto in the package vignette.

As a learning exercise I concocted the example below involving Bayesian logistic regression. It was inspired by an article on Matt Shotwell’s blog about using R environments rather than lists to store the state of a Markov Chain Monte Carlo sampler. Here I use proto to create a parent class-like object (or trait in proto-ese) to contain the regression functions and create child objects to hold both data and results for individual analyses.

First here’s an example session…

# Make up some data with a continuous predictor and binary response
nrec <- 500
x <- rnorm(nrec)
y <- rbinom(nrec, 1, plogis(2 - 4*x))

# Predictor matrix with a col of 1s for intercept
pred <- matrix(c(rep(1, nrec), x), ncol=2)
colnames(pred) <- c("intercept", "X")

# Load the proto package
library(proto)

# Use the Logistic parent object to create a child object which will 
# hold the data and run the regression (the $ operator references 
# functions and data within a proto object)
lr <- Logistic$new(pred, y)
lr$run(5000, 1000)

# lr now contains both data and results
str(lr)

proto object 
 $ cov      : num [1:2, 1:2] 0.05 -0.0667 -0.0667 0.1621 
  ..- attr(*, "dimnames")=List of 2 
 $ prior.cov: num [1:2, 1:2] 100 0 0 100 
 $ prior.mu : num [1:2] 0 0 
 $ beta     : num [1:5000, 1:2] 2.09 2.09 2.09 2.21 2.21 ... 
  ..- attr(*, "dimnames")=List of 2 
 $ adapt    : num 1000 
 $ y        : num [1:500] 0 1 1 1 1 1 1 1 1 1 ... 
 $ x        : num [1:500, 1:2] 1 1 1 1 1 1 1 1 1 1 ... 
  ..- attr(*, "dimnames")=List of 2 
 parent: proto object 

# Use the Logistic summary function to tabulate and plot results
lr$summary()

From 5000 samples after 1000 iterations burning in
   intercept           X         
 Min.   :1.420   Min.   :-5.296  
 1st Qu.:1.840   1st Qu.:-3.915  
 Median :2.000   Median :-3.668  
 Mean   :1.994   Mean   :-3.693  
 3rd Qu.:2.128   3rd Qu.:-3.455  
 Max.   :2.744   Max.   :-2.437  

And here's the code for the Logistic trait...

Logistic <- proto()

Logistic$new <- function(., x, y) {
  # Creates a child object to hold data and results
  #
  # x - a design matrix (ie. predictors
  # y - a binary reponse vector

  proto(., x=x, y=y)
}

Logistic$run <- function(., niter, adapt=1000) {
  # Perform the regression by running the MCMC
  # sampler
  #
  # niter - number of iterations to sample
  # adapt - number of prior iterations to run
  #         for the 'burning in' period

  require(mvtnorm)

  # Set up variables used by the sampler
  .$adapt <- adapt
  total.iter <- niter + adapt  
  .$beta <- matrix(0, nrow=total.iter, ncol=ncol(.$x))
  .$prior.mu <- rep(0, ncol(.$x))
  .$prior.cov <- diag(100, ncol(.$x))
  .$cov <- diag(ncol(.$x))

  # Run the sampler
  b <- rep(0, ncol(.$x))
  for (i in 1:total.iter) {
    b <- .$update(i, b)
    .$beta[i,] <- b
  }
  
  # Trim the results matrix to remove the burn-in
  # period
  if (.$adapt > 0) {
    .$beta <- .$beta[(.$adapt + 1):total.iter,]
  }
}

Logistic$update <- function(., it, beta.old) {
  # Perform a single iteration of the MCMC sampler using
  # Metropolis-Hastings algorithm.
  # Adapted from code by Brian Neelon published at:
  # http://www.duke.edu/~neelo003/r/
  #
  # it -       iteration number
  # beta.old - vector of coefficient values from 
  #            the previous iteration

  # Update the coefficient covariance if we are far
  # enough through the sampling
  if (.$adapt > 0 & it > 2 * .$adapt) {
    .$cov <- cov(.$beta[(it - .$adapt):(it - 1),])
  }
  
  # generate proposed new coefficient values
  beta.new <- c(beta.old + rmvnorm(1, sigma=.$cov))
  
  # calculate prior and current probabilities and log-likelihood
  if (it == 1) {
    .$..log.prior.old <- dmvnorm(beta.old, .$prior.mu, .$prior.cov, log=TRUE)
    .$..probs.old <- plogis(.$x %*% beta.old)
    .$..LL.old <- sum(log(ifelse(.$y, .$..probs.old, 1 - .$..probs.old)))
  }
  log.prior.new <- dmvnorm(beta.new, .$prior.mu, .$prior.cov, log=TRUE)
  probs.new <- plogis(.$x %*% beta.new)  
  LL.new <- sum(log(ifelse(.$y, probs.new, 1-probs.new)))
  
  # Metropolis-Hastings acceptance ratio (log scale)
  ratio <- LL.new + log.prior.new - .$..LL.old - .$..log.prior.old
  
  if (log(runif(1)) < ratio) {
   .$..log.prior.old <- log.prior.new
   .$..probs.old <- probs.new
   .$..LL.old <- LL.new
    return(beta.new)
  } else {
    return(beta.old)
  }
}

Logistic$summary <- function(., show.plot=TRUE) {
  # Summarize the results

  cat("From", nrow(.$beta), "samples after", .$adapt, "iterations burning in\n")
  base::print(base::summary(.$beta))
  
  if (show.plot) {
    par(mfrow=c(1, ncol(.$beta)))
    for (i in 1:ncol(.$beta)) {
      plot(density(.$beta[,i]), main=colnames(.$beta)[i])
    }
  }
}

Now that's probably not the greatest design in the world, but it only took me a few minutes to put it together and it's incredibly easy to modify or extend. Try it !

Thanks to Brian Neelon for making his MCMC logistic regression code available (http://www.duke.edu/~neelo003/r/).