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Spatial field effects per unit

Source: R/field_effects.R
field_effects.Rd

The posterior of a fit's spatial field, one row per spatial unit: an areal region (a row of the adjacency matrix) or an SPDE mesh node. The value reported is the field's contribution to the linear predictor at that unit, the quantity a covariate is read against.

Usage

field_effects(object)

Arguments

object

A tulpa_fit carrying a spatial field.

Value

A data frame with one row per spatial unit: unit (the node index), label (the adjacency row name where it has one, else the index), estimate, sd, the 2.5% / 97.5% bounds conf.low / conf.high, and source: "sampled" for a posterior draw summary, "mixture" for a nested-Laplace Gaussian mixture, "mode" for a point at the mode (sd and the bounds NA). The field type is attached as attribute "field".

Details

Where the posterior comes from follows what the fit computes:

  • A ModelData sampler fit (mode = "hmc" and its siblings) with an ICAR, BYM2 or proper-CAR field: every draw's field contribution is evaluated through the engine's own linear-predictor assembly, so an intrinsic field is centred as its density centres it and a BYM2 field is the combined sigma * (sqrt(rho) * s * phi + sqrt(1 - rho) * theta) at that draw's sigma and rho (s the scaling factor). A unit that no observation falls in contributes nothing to the linear predictor and is reported as NA.

  • A Polya-Gamma Gibbs fit (mode = "gibbs") stores the field it samples (phi_spatial[k]); for BYM2 each draw's two unit-scale components are combined into the same sigma * (sqrt(rho) * s * phi + sqrt(1 - rho) * theta) the sampler path reports. The rows summarize those draws.

  • A nested-Laplace fit with an ICAR or proper-CAR field: each outer cell contributes its conditional mode, and its marginal variance where the fit retained one, weighted by the cell's posterior weight. With the variances the summary is the exact mean, SD and quantiles of that Gaussian mixture (source = "mixture"); without them the weighted mean of the cell modes, with no spread (source = "mode"). A BYM2 field is reported where the fit stores its grid-weighted combined field (fit_st_nested()); a tulpa() nested BYM2 fit carries its two components with cell-dependent mixing and errors.

  • A conditional-Laplace areal fit (mode = "laplace" / "eb") reports the field at its mode, with no spread. For BYM2 this is the combined field at the sigma = 1, rho = 0.5 the conditional kernel fixes.

  • An SPDE fit (fit_spde()) reports the mesh-node field: its draws on the sampler path, its mode (the posterior-mean hyperparameters' fit on the nested path) otherwise.

A fit whose field cannot be located this way (a continuous GP / NNGP / HSGP field, a restricted field, an inline spatial() varying-coefficient field) errors naming the field and the backend, as does a fit with no spatial field. A temporal field is read by temporal().

See also

smooth_effects() for an s(x) term, ranef() for random effects, temporal() for a temporal field.

Examples

# \donttest{
set.seed(1)
K <- 12
W <- matrix(0, K, K)
for (i in 1:K) { j <- if (i < K) i + 1 else 1; W[i, j] <- W[j, i] <- 1 }
region <- sample(1:K, 200, replace = TRUE)
d <- data.frame(region = region, x = rnorm(200))
d$y <- rpois(200, exp(0.3 + 0.5 * d$x + sin(2 * pi * region / K)))
fit <- tulpa(y ~ x + spatial(region), data = d, family = "poisson",
             spatial = list(type = "icar", adjacency = W), mode = "laplace")
fe <- field_effects(fit)
plot(fe$unit, fe$estimate, type = "b")
# }