spatial_range: Extract spatial range and variance from a fitted spatial...

In tulpa: Templated Unified Library for Posterior Approximation in Bayesian Hierarchical Models

Extract spatial range and variance from a fitted spatial model

Description

Summarises the posterior of spatial hyperparameters. For sampler-tier fits this reads the raw hyperparameter draws; for a nested-Laplace spatial fit it summarises the outer hyperparameter grid. Works with ICAR, BYM2, GP (NNGP), CAR, SPDE, and SVC spatial types.

Usage

spatial_range(object, probs = c(0.025, 0.975))

Arguments

object	A tulpa_fit object fitted with a spatial component.
probs	Quantile probabilities for the summary (default 0.025, 0.975).

Details

A range is reported in the units of the coordinates as supplied, also when the field was fitted on standardized coordinates (scale_coords = TRUE, the default of spatial_gp() and spatial_svc()). A mode = "laplace" fit conditions on the field's hyperparameters rather than inferring them, so it carries no posterior for this function to summarise.

Value

A data.frame with rows for each spatial hyperparameter and columns mean, sd, and one quantile column per entry of probs (named from the probability, e.g. q2.5, q97.5 for the defaults).

Examples

set.seed(1)
S <- 12L
adj <- matrix(0, S, S)
for (i in 1:(S - 1)) adj[i, i + 1] <- adj[i + 1, i] <- 1
d <- data.frame(region = factor(rep(1:S, each = 5)), x = rnorm(S * 5))
field <- as.numeric(scale(cumsum(rnorm(S, 0, 0.5))))
d$y <- rpois(nrow(d), exp(0.3 + 0.4 * d$x + field[d$region]))
fit <- tulpa(y ~ x + spatial(region), data = d, family = "poisson",
             spatial = spatial_car(adj, level = "group", group_var = "region"))
spatial_range(fit)

tulpa documentation built on Oct. 19, 2026, 5:09 p.m.