Multivariate Model

[inti]

Hi,
Is it possible with bambi to fit multivariate models. I am looking into fitting a model with (at leas) one covariance matrix and two outcomes. I want to estimate the variance components associated with the known covariance (kernel) matrix on each outcome and also the covariance between the outcomes associated with the kernel.

Is that possible to do with bambi

Many thanks in advance

[jake-westfall]

It's not currently possible to fit models like that. If I understood correctly, you seem to be talking about two separate features: (a) fitting models with multiple response variables where you estimate the covariance between responses (/their errors), and (b) fitting models where the realizations of each response are not independent but rather have a certain covariance matrix with parameters to be estimated, as in e.g. serial autocorrelation. (That's my best guess at what you mean by "the variance components associated with the known covariance (kernel) matrix on each outcome.")

Both of these features would be nice to eventually have, at least for Gaussian responses. For non-Gaussian responses I think it would potentially be quite tricky, as you're basically in GEE territory at that point.

[inti]

thanks for the response. your interpretation was correct, sorry if the explanation was a bit confusing. regards, Inti

…

On 17 November 2017 at 02:05, Jake Westfall ***@***.***> wrote: It's not currently possible to fit models like that. If I understood correctly, you seem to be talking about two separate features: (a) fitting models with multiple response variables where you estimate the covariance between responses (/their errors), and (b) fitting models where the realizations of each response are not independent but rather have a certain covariance matrix with parameters to be estimated, as in for example GLS <https://en.wikipedia.org/wiki/Generalized_least_squares>. (That's my best guess at what you mean by "the variance components associated with the known covariance (kernel) matrix on each outcome.") Both of these features would be nice to eventually have, at least for Gaussian responses. For non-Gaussian responses I think it would potentially be quite tricky, as you're basically in GEE <https://en.wikipedia.org/wiki/Generalized_estimating_equation> territory at that point. — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#108 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AAXApdyUW19vzHbsWD6e4p5Ihtrc1GQ4ks5s3RQmgaJpZM4QhEHP> .

[jake-westfall]

The title of this issue corresponds to case (a) from above, so I just created #110 to handle case (b).