Case: housing prices in Boston
Fitting the INLA model:
res <- inla(formula, family = "gaussian", data = map,
control.predictor = list(compute = TRUE),
control.compute = list(return.marginals.predictor = TRUE))
Control computation of fitted values (at the scale of the linear predictor):
control.predictor = list(compute = TRUE)
: fitted values and their posterior summary:res$summary.fitted.values
control.compute = list(return.marginals.predictor = TRUE)
: compute posterior marginal distribution of the fitted values:res$marginals.fitted.values[[1]]
Model object evaluation: see book.