andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1682 knowledge-graph by maker-knowledge-mining
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Introduction: Richard Morey writes: You and your blog readers may be interested to know that a we’ve released a major new version of the BayesFactor package to CRAN. The package computes Bayes factors for linear mixed models and regression models. Of course, I’m aware you don’t like point-null model comparisons, but the package does more than that; it also allows sampling from posterior distributions of the compared models, in much the same way that your arm package does with lmer objects. The sampling (both for the Bayes factors and posteriors) is quite fast, since the back end is written in C. Some basic examples using the package can be found here , and the CRAN page is here . Indeed I don’t like point-null model comparisons . . . but maybe this will be useful to some of you!
sentIndex sentText sentNum sentScore
1 Richard Morey writes: You and your blog readers may be interested to know that a we’ve released a major new version of the BayesFactor package to CRAN. [sent-1, score-1.3]
2 The package computes Bayes factors for linear mixed models and regression models. [sent-2, score-1.251]
3 Of course, I’m aware you don’t like point-null model comparisons, but the package does more than that; it also allows sampling from posterior distributions of the compared models, in much the same way that your arm package does with lmer objects. [sent-3, score-2.483]
4 The sampling (both for the Bayes factors and posteriors) is quite fast, since the back end is written in C. [sent-4, score-0.781]
5 Some basic examples using the package can be found here , and the CRAN page is here . [sent-5, score-0.985]
6 Indeed I don’t like point-null model comparisons . [sent-6, score-0.34]
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Introduction: Richard Morey writes: You and your blog readers may be interested to know that a we’ve released a major new version of the BayesFactor package to CRAN. The package computes Bayes factors for linear mixed models and regression models. Of course, I’m aware you don’t like point-null model comparisons, but the package does more than that; it also allows sampling from posterior distributions of the compared models, in much the same way that your arm package does with lmer objects. The sampling (both for the Bayes factors and posteriors) is quite fast, since the back end is written in C. Some basic examples using the package can be found here , and the CRAN page is here . Indeed I don’t like point-null model comparisons . . . but maybe this will be useful to some of you!
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Introduction: R has zillions of packages, and people are submitting new ones each day . The volunteers who keep R going are doing an incredibly useful service to the profession, and they’re busy . A colleague sends in some suugestions based on a recent experience with a package update: 1. Always use the R dev version to write a package. Not the current stable release. The R people use the R dev version to check your package anyway. If you don’t use the R dev version, there is chance that your package won’t pass the check. In my own experience, every time R has a major change, it tends to have new standards and find new errors in your package with these new standards. So better use the dev version to find out the potential errors in advance. 2. After submission, write an email to claim it. I used to submit the package to the CRAN without writing an email. This was standard operating procedure, but it has changed. Writing an email to claim about the submission is now a requir
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5 0.20714197 2069 andrew gelman stats-2013-10-19-R package for effect size calculations for psychology researchers
Introduction: Dan Gerlanc writes: I read your post the other day [now the other month, as our blog is on a bit of a delay] on helping psychologists do research and thought you might be interested in our R package, “bootES”, for robust effect size calculation and confidence interval estimation using resampling techniques. The package provides one function, ‘bootES’, that makes a variety of effect size calculations fairly straightforward for researchers with limited programming experience. The majority of the implemented are not available in R or SPSS without custom coding. Kris Kirby (Williams College) and I have published a paper in Behavioral Research Methods describing the methods and providing a tutorial on use of the package: http://bit.ly/YIM6VD. We hope that it’s useful to psychologists and other social science researchers! I haven’t tried this out but it might be of interest for some of you.
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Introduction: This post is an (unpaid) advertisement for the following extremely useful resource: Petersen, K. B. and M. S. Pedersen. 2008. The Matrix Cookbook . Tehcnical Report, Technical University of Denmark. It contains 70+ pages of useful relations and derivations involving matrices. What grabbed my eye was the computation of gradients for matrix operations ranging from eigenvalues and determinants to multivariate normal density functions. I had no idea the multivariate normal had such a clean gradient (see section 8). We’ve been playing around with Hamiltonian (aka Hybrid) Monte Carlo for sampling from the posterior of hierarchical generalized linear models with lots of interactions. HMC speeds up Metropolis sampling by using the gradient of the log probability to drive samples in the direction of higher probability density, which is particularly useful for correlated parameters that mix slowly with standard Gibbs sampling. Matt “III” Hoffman ‘s already got it workin
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Introduction: Somebody emailed me: I am a researcher at ** University and I have recently read your article on average predictive comparisons for statistical models published 2007 in the journal “Sociological Methodology”. Gelman, Andrew/Iain Pardoe. 2007. “Average Predictive Comparisons for Models with Nonlinearity, Interactions, and Variance Components”. Sociological Methodology 37: 23-51. Currently I am working with multilevel models and find your approach very interesting and useful. May I ask you whether replication materials (e.g. R Code) for this article are available? I had to reply: Hi—I’m embarrassed to say that our R files are a mess! I had ideas of programming the approach more generally as an R package but this has not yet happened yet.
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Introduction: Greg Campbell writes: I am a Canadian archaeologist (BSc in Chemistry) researching the past human use of European Atlantic shellfish. After two decades of practice I am finally getting a MA in archaeology at Reading. I am seeing if the habitat or size of harvested mussels (Mytilus edulis) can be reconstructed from measurements of the umbo (the pointy end, and the only bit that survives well in archaeological deposits) using log-transformed measurements (or allometry; relationships between dimensions are more likely exponential than linear). Of course multivariate regressions in most statistics packages (Minitab, SPSS, SAS) assume you are trying to predict one variable from all the others (a Model I regression), and use ordinary least squares to fit the regression line. For organismal dimensions this makes little sense, since all the dimensions are (at least in theory) free to change their mutual proportions during growth. So there is no predictor and predicted, mutual variation of
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