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Asked: 2026-05-23T04:00:31+00:00

Assume this easy example: treatment <- factor(rep(c(1, 2), c(43, 41)), levels = c(1, 2),labels

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Assume this easy example:

treatment <- factor(rep(c(1, 2), c(43, 41)), levels = c(1, 2),labels = c("placebo", "treated"))
improved <- factor(rep(c(1, 2, 3, 1, 2, 3), c(29, 7, 7, 13, 7, 21)),levels = c(1, 2, 3),labels = >c("none", "some", "marked"))
numberofdrugs<-rpois(84, 50)+1
healthvalue<-rpois(84,5)
y<-data.frame(healthvalue,numberofdrugs, treatment, improved)
test<-lm(healthvalue~numberofdrugs+treatment+improved, y)

What am I supossed to do when I’d like to estimate a beta-binomial regression with R? Is anybody familiar with it? Any thought is appreciated!

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1 Answer

  1. Editorial Team

    I don’t see how this example relates to beta-binomial regression (i.e., you have generated count data, rather than (number out of total possible)). To simulate beta-binomial data, see rbetabinom in either the emdbook or the rmutil packages …

    library(sos); findFn("beta-binomial") finds a number of useful starting points, including

    • aod (analysis of overdispersed data), betabin function
    • betabinomial family in VGAM
    • hglm package
    • emdbook package (for dbetabinom) plus mle2 package
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