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.