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JenniNiku / gllvm

Generalized Linear Latent Variable Models
https://jenniniku.github.io/gllvm/
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How to identify variables that did not converge? #60

Closed Leitemfa closed 2 years ago

Leitemfa commented 2 years ago

Hello,

I'm using the gllvm, but got a warning on the lack of convergence. I would like to look at the MCMC values to see which variable(s) did not converge. How do I do that?

Is there any option to store the MCMC chains in the R object?

BertvanderVeen commented 2 years ago

Thanks for your question! gllvm approximates the integration in the likelihood, so no MCMC occurs and thus no chains are available (you can read e.g. https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13303 for a few details). A first check (up to local optima in the likelihood surface) for convergence can be done based on the gradient, which is available in model$TMBfn$gr(model$TMBfn$par). The values of this vector should all be close to zero for a converged model. There is an automated way of doing this in the package, which is done by setting gradient.check = TRUE in the main gllvm(.) function.

I hope that helps!

Leitemfa commented 2 years ago

Hi @BertvanderVeen, Thank you very much for the explanation! I used the function as suggested, but how can I link the values with the variables names. In my case, the final output of model$TMBfn$gr(model$TMBfn$par) was a vector 1775 values. I have 260 dependent variables and samples grouped in four treatments.

BertvanderVeen commented 2 years ago

The names of the parameters are stored in the $par vector, so you can assign them to the gradient: grad <- model$TMBfn$gr(model$TMBfn$par); names(grad) <- names(model$TMBfn$par)

Leitemfa commented 2 years ago

Hi @BertvanderVeen , thanks again for the quick reply, but the names of the parameters are also not much informative to refer back to my dependent variables.

For my case I have repetition of the parameters (r0, b, lambda, lg_phi, u, and Au). I can see by the numbers that they will match with the combinations of treatments, variables and random effects. So, I could try to link them with the variable names, but I wonder if there is any other place where I can find this information already available.

BertvanderVeen commented 2 years ago

You're interested in the b's. They are ordered per dependent variable and predictor, so for K predictors you have p vectors of length 1 + K, where p is the number of dependent variables in the response and the first value is for the intercept for that dependent variable.

Leitemfa commented 2 years ago

Ok, I will check that. Thanks!