Hello, I’m working on spatial data by a GLMM. The model reads, y \sim binomial\left ( n,p \right ) p= logit(\eta) \eta = \alpha + X\beta + \phi \phi \sim MVN(0, \left [ \tau_{\mu}\left ( I-\lambda W \right )\left ( I-\lambda {W} \right )' \right ]^{-1} ) The stan code that I use data{ int<l…

hi, is that the Leroux spatial model? yes, it will be very slow - that’s a problem with the Leroux model. There’s a great paper by Riebler et al that goes over these models and motivates the BYM2 model proposed by Dan Simpson: https://arxiv.org/abs/1601.01180 Based on that, we’ve developed the …

Hi, It is not the usual CAR model. The variance-covariance matrix follows a SAR structure, rather than a CAR structure. Thanks

interesting - not sure what to do about SAR models - @anon75146577 might have input?

The cor_errorsar(W) in brm is not identical to the random intercept model (The SAR structure is imposed on the variance-covariance matrix.) mentioned here. You can see that in a cor_errorsar(W) model, the spatial structure is built on the error term, but there is not an error term in binomial regre…

With the caveat that I have not tried this, I would use the non-centred parameterization. Sadly, Stan does not have anything that speeds up the computations based on the Markov property (yet), so there’s a limit to how fast it can be. transformed data{ vector[N] zeros; zeros = rep_vector(0, N);…

How to accelerate spatial models on STAN

Modeling

mitzimorris September 10, 2020, 5:21pm 5

maybe use BRMS? cf Spatial simultaneous autoregressive (SAR) structures in multilevel models

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How to accelerate spatial models on STAN

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