Predict from a Bayesian spatial GP model

Applies the same newdata preparation pipeline as predict.gwr_fit(): non-point geometries are coerced to points, the data is projected to the CRS used during fitting (via ensure_projected()), and rows with missing or non-finite values are dropped. Coordinate scaling and predictor standardisation stored at fit time are then applied before delegating to brms::posterior_epred() or brms::posterior_predict().

Usage

# S3 method for class 'bayesian_fit'
predict(
  object,
  newdata = NULL,
  summary = c("mean", "median"),
  type = c("epred", "predict"),
  draws = FALSE,
  ...
)

Arguments

object: A bayesian_fit object.
newdata: An sf object with the same predictors. The response variable need not be present (true out-of-sample prediction is supported). NULL = fitted values.
summary: "mean" (default) or "median" over posterior draws.
type: "epred" (default) for expected predictions (no obs noise), or "predict" for full posterior predictive draws (includes obs noise).
draws: If TRUE, return the full posterior draw matrix instead of a point summary. Default FALSE.
...: Ignored.

Value

Numeric vector of length nrow(newdata), or a n_draws x nrow(newdata) matrix when draws = TRUE. If the posterior draw fails the result is all NA (a 1-row matrix for draws = TRUE) and the cause is logged. An ordinal or categorical family is not a failed draw and is an error under type = "epred": its expected value is a probability per response category, not one number per row. Use type = "predict", draws = TRUE for posterior draws of the predicted category, as category indices; the share of draws in each category estimates its probability. Without draws = TRUE, type = "predict" returns the mean (or median) category index, an expected rank for an ordinal family and an error for brms::categorical(), whose categories have no order. With newdata = NULL the cached fitted() values are returned only for the default summary = "mean", type = "epred", draws = FALSE combination; any other combination is recomputed against the training data, because the cache holds epred column means and nothing else.

The GP boundary is held at its fitted value

brms 2.17 to 2.22 do not store the Hilbert-space boundary \(L\) in a fitted GP basis, so brms:::.data_gp() recomputes it from whatever rows predict() is handed, which moved every eigenfunction of the approximation with the newdata bounding box while the fitted basis coefficients stayed put. Two synthetic rows at the training coordinate extrema are therefore appended before the posterior draw and dropped from the result, and when newdata reaches past the training range the c of the gp() term is scaled down by as much as the range grew, so brms rebuilds exactly the boundary the model was fitted with. A prediction therefore does not depend on which other rows share the call: chunked, fold-wise and single-call predictions agree, and predict_surface() does not depend on chunk_size. brms 2.23.0 and later store \(L\) and reuse it, so there c is left alone and the two extra rows change nothing.

Predictions outside the training coordinate envelope are extrapolation (a notice is written to the log). A row further than \(L\) from the centre of the training coordinates on either axis is past the edge of the basis, where the approximate GP is an odd reflection of the fitted surface rather than an estimate of anything, so it is returned as NA (a column of NA with draws = TRUE) with a warning. The default boundary factor puts that edge well outside the training data, so only newdata reaching far past it is affected.

Usage

Arguments

Value

The GP boundary is held at its fitted value

See also