I’ve written about R squared a few times before. In a discussion I was involved with today the question was raised as to how/whether the R squared in a linear regression model with a single continuous predictor depends on the variance of the predictor variable. The answer to the question is of course yes.
My first foray with Stan
A number of people have mentioned Stan recently to me. Stan fits probability models to data using the Bayesian approach to statistical inference. WinBUGS was the first package to really allow users to fit complex, user defined models with Bayesian methods. As far as I understand, Stan’s strongest selling points are that it is fast, because it compiles your model into C++ code, and because of the clever sampling methods it implements (for more on this, see the sub-section in Stan’s manual).
Prof. Alan Agresti on modelling ordinal data, issues with Wald based inferences, and GEE for ordinal data
Yesterday we were very pleased to welcome Prof. Alan Agresti, from the University of Florida, to give a departmental seminar at the London School of Hygiene & Tropical Medicine. Prof. Agresti gave a very interesting seminar, covering a wide range of topics, which came out of writing his most recent book – ’Foundations of Linear and Generalized Linear Models’. Among these was some of the problems that can arise when ordinal outcomes are modelled using linear regression models. He then discussed a proposal for a new way of representing covariate effects in ordinal regression models, in a so called superiority measure. Next he discussed some of the problems that can arise with Wald based inferences, a topic I’ve touched upon before here. Prof. Agresti then discussed some issues with residuals in GLMs, and some recently new methods for modelling multivariate using generalized estimating equations.
An audio/slide recording of Prof. Agresti’s seminar is available here.