How to pass multiple columns of weights to brms

[realkrantz]

Thanks, @mjskay, for this very helpful insight. I am doing this to account for Uncertainty in the Design Stage.

@realkrantz

My objective is to obtain the posterior distribution of effects after marginalizing over the distribution of weights as discussed here.

I need to do this in brms or stanarm rather than stan directly because I want to use functions of https://github.com/mjskay/tidybayes that are currently not supported by a stanfit object.

@mjskay

you could perhaps flatten your dataframe into something with n*w rows and a single weights column.

If I am understanding correctly your explanation, the solution to my problem would be to change the structure of the weights from wide to long format with this:

library(tidyr)

#convert to long format
dt_bind <- tibble::rowid_to_column(dt_bind, "id")
dt_bind$id <- factor(dt_bind$id)
dt_long <- gather(dt_bind, draw, weight, weights.w_1:weights.w_10, factor_key=TRUE)

head(dt_long, 12)
id  N   n   r          p group treat       c1     c2        draw   weight
1   10  62  3 0.04838710     1     0 1.941115 0.1438 weights.w_1 1.941115
2   10  96  1 0.01041667     1     0 1.186583 0.2370 weights.w_1 1.186583
3   10  17  0 0.00000000     0     0 1.159883 0.2774 weights.w_1 3.159883
4   10  41  2 0.04878049     1     0 1.159883 0.2774 weights.w_1 3.159883
5   10  212 170 0.80188679   0     0 1.133398 0.2093 weights.w_1 1.133398
6   10  143 21  0.14685315   1     1 1.128993 0.1206 weights.w_1 1.128993
7   10  143 0 0.00000000     1     1 1.128993 0.1707 weights.w_1 2.128993
8   10  143 33 0.23076923    0     1 1.128993 0.0699 weights.w_1 1.128993
9   10  73  62 0.84931507    0     1 1.121927 0.1351 weights.w_1 1.121927
10  10  73  17 0.23287671    0     1 1.121927 0.1206 weights.w_1 1.121927
1   10  62  3 0.04838710     1     0 1.941115 0.1438 weights.w_2 1.931115
2   10  96  1 0.01041667     1     0 1.186583 0.2370 weights.w_2 1.176583

Is that right?

Assuming I am getting it right, how would I then account in my brms or rstanarm model for the fact that each id has a distribution of weights represented by the variable draw, rather than a single weight?

Thanks in advance.