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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")

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
\$ 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
.\$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
}
}

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
.\$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/).