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Extract spatially-varying coefficients from a fitted model

Source: R/spatial_svc.R
svc.Rd

Extract posterior distributions of spatially-varying coefficients (SVCs) from a fitted tulpa model with SVC specification.

Usage

svc(object, terms = NULL, summary = FALSE, probs = c(0.025, 0.5, 0.975), ...)

# S3 method for class 'tulpa_fit'
svc(object, terms = NULL, summary = FALSE, probs = c(0.025, 0.5, 0.975), ...)

Arguments

object

A tulpa_fit object fitted with svc argument

terms

Which SVC terms to extract. If NULL (default), extracts all.

summary

Logical; if TRUE, return summary statistics instead of full posterior draws.

probs

Quantiles to compute if summary = TRUE.

...

Ignored

Value

A tulpa_svc_posterior object containing:

  • draws: Array of posterior draws (draws x locations x terms)

  • coords: Coordinate matrix

  • term_names: Names of SVC terms

See also

spatial_svc(), plot.tulpa_svc_posterior()

Examples

# \donttest{
set.seed(303)
n <- 25L
df <- data.frame(lon = runif(n), lat = runif(n), x = rnorm(n))
bsurf <- 0.9 * sin(2.8 * df$lon) + 0.7 * cos(2.2 * df$lat)
df$count <- rpois(n, exp(0.2 + (0.8 + bsurf) * df$x))

# The varying slope on `x` is a spatial field; SVC is exact-mode only.
fit <- tulpa(
  count ~ x,
  data = df,
  family = "poisson",
  spatial = spatial_svc(~ lon + lat, terms = ~ x - 1, nn = 5L),
  mode = "exact",
  control = list(n_iter = 80L, n_warmup = 40L, seed = 1L)
)

svc_post <- svc(fit)
summary(svc_post)
# }