 # Comparing marginal effects between different models using brms

Hello, I am using brms package to estimate multivariate models, each using different samples (i.e., the data observations used in the two are different).

I found that I could compare two or more coefficient from different models by using suest (seemingly unrelated estimation) and gsem (generalized structural equation modeling) functions in STATA.

I wonder if there is a similar options in brms.

Thanks.

Can you use an additional factor which indicates which sample the data come from and interact that with the other predictors in the model? Then everything’s in the same model and it should be easy to compare coefficients from the two data sets.

hi there. I don’t think that would work. It’s mainly because that different samples include same individuals; for example, my dv1 counts for free-service behavior while dv2 counts for paid-service behavior, among the same data panels.
Is there anybody who can help me?

Some more info about exactly what data you have would be helpful. But from what you’ve described so far it sounds like you could run a multivariate regression on your two DVs which includes random effects varying by participant. Within that model you will be able to compare the two sets of coefficients for the two DVs. You can correlate the random effects between the two DVs using the |ID| syntax as detailed here Estimating Multivariate Models with brms.

@andymilne Thank you so much for your kindness.
To be specific, my model is as follows :
model =
bf(Y1 ~ IV+(1|rand|individual)+factor(time), family=bernoulli(logit)) +
bf(Y2 ~ IV+(1|rand|individual)+factor(time), family=bernoulli(logit)) +
bf(DV1 | subset(Y1) ~ IV+(1|rand|individual)+factor(time), family=gaussian()) +
bf(DV2 | subset(Y1) ~ IV+(1|rand|individual)+factor(time), family=gaussian())
Prior_int = c(
set_prior(“normal(0,5)”, resp = "Y1, class = “Intercept”),
set_prior(“normal(0,5)”, resp = “Y2”, class = “Intercept”),
set_prior(“normal(0,5)”, resp = “DV1”, class = “Intercept”),
set_prior(“normal(0,5)”, resp = “DV2”, class = “Intercept”) )
brm (model + set_rescor(FALSE), data=data, […] , prior = c(Prior_int), inits=0)

, where Y1 and Y2 are dichotomous variables while DV1 and DV2 are continuous ones.

My concern is to compare the effect size of IV between Y1 and Y2, as well as D1 and D2.
I found ‘suest’ function in STATA do the similar things, but it does not exactly match with my object.

The formula looks reasonable (although I’ve not previously used the subset argument, so cannot comment on that).

This model presumably results in coefficients called something like `Y1_IV` and `Y2_IV`, and you want to compare those. Same with the coefficients `DV1_IV` and `DV2_IV`. The easiest way to compare coefficients is to use the hypothesis function:

``````hypothesis(model, c("Y1_IV - Y2_IV > 0", "DV1_IV - DV2_IV > 0"))
``````

(or flipping the `>` to `<` depending on your hypothesized direction). This will give their estimated difference and CIs for that difference, along with an evidence ratio (posterior odds the difference is in the direction specified) and associated posterior probability. Hopefully I have understood what you are trying to do.

Thank you so much. Problem solved.

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