Hi, has anyone seen any Stan example of hierarchical logistic regression with time-varying coefficients, captured by a dynamic linear model (Kalman filter type) process? Thanks.
Does this help Example von Bertalanffy model (and hierarchical logistic regression and linear regression) as a starting point?
Check out Eric Ward’s course Applied Time Series Analysis (atsa-es.github.io)
He has moved much of the R MARSS package to Stan models that are part of the course he co-teaches (the above link goes to those packages as well).
Edit: I think this model example might be what you are looking for: atsar | Applied time series analysis in R with Stan. Allows fast Bayesian fitting of multivariate time-series models. (atsa-es.github.io)
Thanks for the replies thus far. Let me try to write down the model I have in mind:
Y[i, t]: binary outcome (1/0) for period i at time t
Y[i, t] = P( A[i, t] )
P(.) = inverse-logit
A[i, t+1] = A[i, t] + e[i, t+1]
e[i, t] ~ N ( 0, s ), for all t
A[i, 0] ~ N ( pop_A, pop_s )
I call this a hierarchical logistic time-varying random intercept model. Thanks.
I’m not sure what you are trying to do. But, if you want to do a latent, binary variable, your model seems similar to an occupancy model.
Checkout this package I have written as well: UMESC / quant-ecology / occStan · GitLab (usgs.gov)
As a tip, I suggest these steps:
- Writing out the math of your model
- Simulating your data
- Build up simple parts of your Stan model
Thanks. Could you tell me whether this is the stan code you are referring to? I use cmdStan so I cannot run your RStan-linked package. Thank you.
You found one model. There is also a two-level model in the same folder.