Bayesian spatial models in INLA
Typically, the linear predictor contains both:
- (independent) linear covariate effects: \(X_i \cdot \beta\)
These will typically correspond to the fixed effects of frequentist GLMMs. - other covariate effects: \(Z_i\)
E.g. non-linear effects, spatial, time & seasonal patterns, random intercepts & slope.