Hi,

I think using `weights(1/sei)`

is somewhat different to having `se(sei)`

(although I am not 100% sure about this). Weights will multiply the likelihood contribution of each point and keep `sigma`

(the standard deviation) as a free parameter. OTOH `se`

will fix the standard deviation of the response distribution to the one given in the data (you can always use `make_stancode`

to see what the model does under the hood).

You can workaround this limitation by writing a custom family that would use `von_mises`

as a single parameter distribution while getting the `kappa`

parameter from data via the `vreal`

construct. (note that `kappa`

however is not the standard deviation of the distribution, so some additional math will be needed). If the instructions seem mysterious even after reading the vignette on custom families (linked earlier), please ask for further clarification.

The best for metanalysis is obviously when you have access to raw data from the individual studies and build a big model with all the data, rather then relying on summaries, but that’s unfortunately not always possible.

I should also note that `von_mises`

can be tricky to fit in general, because the Stan implementation has to (for technical reasons) introduce an artificial boundary somewhere around the circle and you need to ensure that boundary is far from “where the action is happening”.

Best of luck with the model!