Software

Going full Bayesian with mixed effects regression models

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Thanks to some recently developed tools, it’s becoming very convenient to do full Bayesian inference for generalized linear mixed-effects models. First, Andrew Gelman et al. have developed Stan, a general-purpose sampler (like BUGS/JAGS) with a nice R interface which samples from models with correlated parameters much more efficiently than BUGS/JAGS. Second, Richard McElreath has written glmer2stan, an R package that essentially provides a drop-in replacement for the lmer command that runs Stan on a generalized linear mixed-effects model specified with a lme4-style model formula.

This means that, in many cases, you simply simply replace calls to (g)lmer() with calls to glmer2stan():

library(glmer2stan)
library(lme4)
lmer.fit <- glmer(accuracy ~ (1|item) + (1+condition|subject) + condition, 
                  data=data, family='binomial')
summary(lmer.fit)
library(glmer2stan)
library(rstan)
stan.fit <- glmer2stan(accuracy ~ (1|item) + (1+condition|subject) + condition, 
                       data=data, family='binomial')
stanmer(stan.fit)

There’s the added benefit that you get a sample from the full, joint posterior distribution of the model parameters

Read on for more about the advantage of this approach and how to use it.

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Using plyr to get intimate with your data

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I gave a short tutorial [pdf slides] at the LSA summer institute on one of my favorite R packages: plyr (another brilliant Hadley Wickham creation). This package provides a set of very nice and semantically clean functions for exploring and manipulating data. The basic process that these functions carry out is to split data up in some way, do something to each piece, and then combine the results from each piece back together again.

One of the most common tasks that I use this for is to do some analysis to data from each subject in an experiment, and collect the results in a data frame. For instance, to calculate the mean and variance of each subject’s reaction time, you could use:

ddply(my.data, "subject.number", function(d) {
  return(data.frame(mean.RT=mean(d$RT), var.RT=mean(d$RT)))
})

Plyr also provides a whole host of convenience functions. For instance, you could accomplish the same thing using a one-liner:

ddply(my.data, "subject.number", summarise, mean.RT=mean(RT), var.RT=var(RT))

There are lots more examples (as well as more background on functional programming in general and the other use cases for plyr) in the slides [pdf] (knitr source is here, too).

Running phonetic (adaptation) experiments online

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I’ve developed some JavaScript code that somewhat simplifies running experiments online (over, e.g., Amazon’s Mechanical Turk). There’s a working demo, and you can download or fork the source code to tinker with yourself. The code for the core functionality which controls stimulus display, response collection, etc. is also available in its own repository if you just want to build around that.

If you notice a bug, or have a feature request, open an issue on the issue tracker (preferred), or comment here with questions and ideas. And, of course, if you want to contribute, please go ahead and submit a pull request. Everything’s written in HTML, CSS, and JavaScript (+JQuery) and aims to be as extensible as possible. Happy hacking!

If you find this code useful for your purposes, please refer others to this page. If you’d like to cite something to acknowledge this code or your own code based on this code, the following is the paper in which we first used this paradigm:

  1. Kleinschmidt, D. F., and Jaeger, T. F. 2012. A continuum of phonetic adaptation: Evaluating an incremental belief-updating model of recalibration and selective adaptation. Proceedings of the 34th Annual Meeting of the Cognitive Science Society (CogSci12), 605-610. Austin, TX: Cognitive Science Society.

A more detailed journal paper is currently under review. If you’re interested, subscribe to this post and get the update when we post the paper here once it’s out (or contact me if you can’t wait).

New R library for multilevel modeling

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This might be of interest to many of you. MLwiN, a software package for multilevel modeling developed at Bristol that includes functions beyond those present in, e.g., lmer, now has an interface for R (kinda like WinBugs, etc.), so that you can continue to use R while taking advantage of the powerful tools in MLwiN. The package is called R2MLwiN. For more details, see below.

Dear all,
We are pleased to announce a new R package, R2MLwiN (Zhang et al. 2012)
that allows R users access to the functionality within MLwiN directly from
within the R package. This package has been developed as part of the e-STAT
ESRC digital social research programme grant along with the Stat-JR package.
See <http://www.bristol.ac.uk/cmm/software/r2mlwin/> for more details
including examples taken from the book MCMC Estimation in MLwiN.

Feedback gratefully received by either me or Zhengzheng Zhang (Z.Zhang@bristol.ac.uk).

Best wishes,
  Bill Browne.