Friday, December 5, 2014
Null hypothesis significance testing (NHST) is increasingly falling out of style with methodologically-minded behavioral and social scientists. Many diverse critiques have been leveled against significance testing; the debate is increasingly what should replace it. Building on work with my colleagues (see here and here), I discuss and critique one replacement option that has been persistently suggested over the years: confidence procedures. We begin with a parable.
Wednesday, October 22, 2014
Today I submitted a new release of BayesFactor, version 0.9.9, to CRAN. Among the new features are support for contingency table analyses, via the function contingencyTableBF, and analysis of a single proportion, via the function proportionBF. Other features and fixes include:
- Added "simple" argument to ttest.tstat, oneWayAOV.Fstat, and linearReg.R2stat; when TRUE, return only the Bayes factor (not the log BF and error)
- When sampling Bayes factors, recompute() now increases the precision of BayesFactor objects, rather than simply recomputing them. Precision from new samples is added
- Added Hraba and Grant (1970) data set; see ?raceDolls
- Added model.matrix method for BayesFactor objects; allows for extracting the design matrix used for an analysis
- recompute() now has multicore and callback support, as intended
- Moved many backend functions to Rcpp from R C API
- t test samplers now sample from interval null hypotheses and point null hypotheses where appropriate
- fixed bug in in meta t test sampler which wouldn't allow sampling small numbers of MCMC samples
Friday, September 12, 2014
For those of us interested in reproducible analysis, Rmarkdown is a great way of communicating our code to other researchers. Rstudio, in particular, makes it very easy to create attractive HTML document containing text, code, and figures, which can then be sent to colleagues or put on the internet for anyone to see. If you aren't using Rmarkdown for your statistical analyses, I recommend you start; you'll never go back to simple script files again (and your colleagues won't want you to).
Sunday, August 31, 2014
BayesFactor version 0.9.8 has been released on CRAN! This is a both a bug fix and feature update. From the NEWS:
- Fixed bugs in model enumeration code
- Fixed bug leading to wrong computation of number of covariate when interactions between continuous variables were included
- Corrected typos/old information in the documentation
- Fixed a memory allocation bug that affected computing Bayes factors with lots of data
- Added meta-analytic Bayes factor for t tests (see meta.ttestBF)
- Fixed bug in ttestBF that yielded Bayes factor of NaN for very extreme posterior interval probabilities
- Fixed several bugs causing infinite integrals; generally improved integration
- Added check to ensure no missing data before analyses
- Added callbacks for access by third-party interfaces
Monday, February 24, 2014
Sunday, February 23, 2014
In the previous post, I introduced the logic of Bayes factors for one-sample designs by means of a simple example. In this post, I will give more detail about the models and assumptions used by the BayesFactor package, and also how to do simple analyses of two- sample designs.
See the previous posts for background:
Wednesday, February 12, 2014
In my first post, I described the general logic of Bayes factors. I will continue discussing the general logic of Bayes factor, while introducing some of the basic functionality of the BayesFactor package.
Sunday, February 9, 2014
The BayesFactor packageThis blog is a companion to the
BayesFactorpackage in R (website), which supports inference by Bayes factors in common research designs. Bayes factors have been proposed as more principled replacements for common classical statistical procedures such as \(p\) values; this blog will offer tutorials in using the package for data analysis.
In this first post, I describe the general logic of Bayes factors using a very simple research example. In the coming posts, I will show how to do a more complete Bayesian data analysis using the R package.