Emacs-lisp is a pretty functional language for managing Emacs and automating complex tasks within it, particularly to do with text processing. It’s probably not wise to use it for more general programming or analytical tasks, but every now and then (when I need to procrastinate, mostly) I get carried away.

A few years ago I was reading Peter Hedstrom’s book, Dissecting the Social, and realised his Desires-Believes-Acts model (a kind of cellular automaton) would be easy enough to implement. More recently, I noticed that Emacs’ tools for displaying simple games like Tetris (do “M-x tetris”) would permit a clean display.

In Hedstrom’s model, every cell in a grid may desire an outcome, and may believe they are able to achieve it. If they do both, they act. Belief and desire depend on the beliefs and desires of your neighbours. Generally, even starting from random and low distributions of belief and desire, within a number of iterations stable configurations emerge, with systematic segregation; often everyone acts in the end but sometime stable oscillating systems emerge.

Continue reading Hedstrom’s Desires-Believes-Acts model in Emacs lisp →

I have updated my Stata add-ons for sequence analysis (including Optimal Matching), and have now released it as a  package that you can install with the following commands:

 net from http://teaching.sociology.ul.ie/sadi

 net install sadi

Continue reading New sequence analysis add-ons: sadi →

In a previous note, I used Stata to simulate 2*2 tables of a one-off outcome. The simulation shows that odds ratios (ORs) are a much better estimate of the underlying causal effect or statistical association than relative rates are, given certain assumptions. One key assumption is that it is a one-off outcome, where it is reasonable to model the propensity for the event with a normal or logistic distribution. Where the outcome is the result of potentially repeated exposure to a risk (such as being ever married or ever infected with a particular pathogen) the resulting propensity is not likely to be normal. That is, if you are exposed to many opportunities to marry, saying yes once means you become ever-married for ever after, and even if the propensity to marry at a specific opportunity is normally distributed, the combined distribution of propensity to be ever-married after an unknown number of opportunities is likely not to be well-described as normal.

Continue reading Relative rates, odds ratios and the complementary log-log model →

A frequent theme in the medical statistics and epidemiological literature is that odds ratios (ORs) as effect measures for binary outcomes are counter intuitive and an impediment to understanding. Barros and Hirakata (2003), for instance, refer to the relative rate as the “measure of choice” and complain that the OR will “overestimate” the RR as the baseline probability rises. Clearly, ORs are less intuitive than relative rates (RRs), but in this note I take issue with the conclusion sometimes made, that models with relative-rate interpretations should be used instead of logistic regression and other OR models. This is because RRs are not measures of the size of the statistical association between a variable and an outcome since they also vary inversely with the baseline probability), and because, under certain assumptions, ORs and related measures are. That is, RRs may feel more real but they are likely to be misleading.

Continue reading Relative rates and odds ratios →