Binomial regression with identity link in brms

Interfaces brms
,
1

As suggested here and elsewhere the model:

glm(y~x, family=binomial(link=identity))

gives estimates in risk difference.

I would like to know if the same model in brms, or specifically the model:

mod<-brm(r | trials(n) ~ treat, data=dt, family=binomial(link=identity))

for the data below would also output estimates in risk difference:

dt = read.table(header = TRUE, text = "
n r r/n group treat c2 c1 w
62 3 0.048387097 1 0 0.1438 1.941115288 1.941115288
96 1 0.010416667 1 0 0.237 1.186583128 1.186583128
17 0 0 0 0 0.2774 1.159882668 3.159882668
41 2 0.048780488 1 0 0.2774 1.159882668 3.159882668
212 170 0.801886792 0 0 0.2093 1.133397521 1.133397521
143 21 0.146853147 1 1 0.1206 1.128993008 1.128993008
143 0 0 1 1 0.1707 1.128993008 2.128993008
143 33 0.230769231 0 1 0.0699 1.128993008 1.128993008
73 62 1.260273973 0 1 0.1351 1.121927228 1.121927228
73 17 0.232876712 0 1 0.1206 1.121927228 1.121927228")

I read here that:

“In the following, we list all possible links for each family. (…) The (…) families binomial , bernoulli , Beta , zero_inflated_binomial , zero_inflated_beta , and zero_one_inflated_beta the links logit , probit , probit_approx , cloglog , cauchit , and identity”.

I hope what I am proposing is possible in bmrs. Thank you in advance for any help.

2

Yes, that should work with brms as well, but you may need to specify reasonable priors on the regression coefficients so that the predictions are between 0 and 1 and thus resemble a probability.

3

Thanks @paul.buerkner. That is very helpful.