On July 25th, I’ll be presenting at the Seattle R Meetup about implementing Bayesian nonparametrics in R. If you’re not sure what Bayesian nonparametric methods are, they’re a family of methods that allow you to fit traditional statistical models, such as mixture models or latent factor models, without having to fully specify the number of
Many readers of this blog will know that I’m a big fan of Bayesian methods, in large part because automated inference tools like JAGS allow modelers to focus on the types of structure they want to extract from data rather than worry about the algorithmic details of how they will fit their models to data.
In this fourth part of my series of criticisms of NHST, I’m going to focus on broad
Introduction Continuing on with my series on the weaknesses of NHST, I’d like to focus on an issue that’s not specific to NHST, but rather one that’s relevant to all quantitative analysis: the destruction caused by an inappropriate reduction of dimensionality. In our case, we’ll be concerned with the loss of essential information caused by
Introduction This is my second post in a series describing the weaknesses of the NHST paradigm. In the first post, I argued that NHST is a dangerous tool for a community of researchers because p-values cannot be interpreted properly without perfect knowledge of the research practices of other scientists — knowledge that we cannot hope
Introduction Over my years as a graduate student, I have built up a long list of complaints about the use of Null Hypothesis Significance Testing (NHST) in the empirical sciences. In the next few weeks, I’m planning to publish a series of blog posts, each of which will articulate one specific weakness of NHST. The
Introduction For me, Hadley Wickham‘s reshape and plyr packages are invaluable because they encapsulate omnipresent design patterns in statistical computing: reshape handles switching between the different possible representations of the same underlying data, while plyr automates what Hadley calls the Split-Apply-Combine strategy, in which you split up your data into several subsets, perform some computation
One major benefit of spending my time recently adding statistical functionality to Julia is that I’ve learned a lot about the inner guts of algorithmic null hypothesis significance testing. Implementing Welch’s two-sample t-test last week was a trivial task because of the symmetry of the null hypothesis, but implementing the exact binomial test has proven
While I should confess upfront that I’ve always had a weaker command of the details of floating point arithmetic than I feel I ought to have, this sort of thing still blows my mind when I stumble upon it. These moments invariably make me realize that floating point math will simply never satisfy my naive
While exploring the Julia manual recently, I realized that it might be helpful to put the basic vocabularies of Julia and R side-by-side for easy comparison. So I took Hadley Wickham’s R Vocabulary section from the book he’s putting together on the devtools wiki, put all of the functions Hadley listed into a CSV file,
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
