In conjunction with our reliable ABC model choice via random forest paper, about to be resubmitted to Bioinformatics, we have contributed an R package called abcrf that produces a most likely model and its posterior probability out of an ABC reference table. In conjunction with the realisation that we could devise an approximation to the (ABC) posterior probability using a secondary random forest. “We” meaning Jean-Michel Marin and Pierre Pudlo, as I only acted as a beta tester!

The package abcrf consists of three functions:

• abcrf, which constructs a random forest from a reference table and returns an object of class `abc-rf’;
• plot.abcrf, which gives both variable importance plot of a model choice abc-rf object and the projection of the reference table on the LDA axes;
• predict.abcrf, which predict the model for new data and evaluate the posterior probability of the MAP.

An illustration from the manual:

data(snp)
data(snp.obs)
mc.rf <- abcrf(snp[1:1e3, 1], snp[1:1e3, -1])
predict(mc.rf, snp[1:1e3, -1], snp.obs)