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Predict at new covariate values (population level)

Source: R/methods_generic.R
predict.tulpa_fit.Rd

Prediction of the linear predictor at newdata, on the link or response scale. The fixed-effect part is X beta with credible bounds from the fixed-effect covariance (vcov()). For a fit carrying a continuous spatial field, the posterior-mean field is interpolated (kriged) to the newdata coordinates and added to the linear predictor by default, so predict() gives the conditional (location-specific) prediction. Three continuous field families are supported: an SPDE Matern field (spatial_spde()), projected through the mesh; a Hilbert-space GP field (spatial_gp(approx = "hsgp")), where the Laplacian basis is re-evaluated at the new coordinates (with the training centring / boundary); and a GP / NNGP field (spatial_gp()), interpolated by the NNGP conditional mean at each new location's nearest training locations. The HSGP and GP/NNGP fields are marginalised over the hyperparameter grid (not plugged in at the posterior mean). Ordinary random effects are held at zero (population level); add group effects from ranef() when needed.

Usage

# S3 method for class 'tulpa_fit'
predict(
  object,
  newdata = NULL,
  type = c("link", "response"),
  se.fit = FALSE,
  level = 0.95,
  include_field = TRUE,
  ...
)

Arguments

object

A tulpa_fit object.

newdata

Data frame of covariates (and, for an SPDE fit, the coordinate columns named in the spec's coordinate formula). If NULL, predicts at the training design (requires $model_matrix).

type

"link" (linear predictor) or "response" (mean scale). For a binomial fit the "response" scale here is the per-trial success probability g^{-1}(eta) (there is no n_trials at newdata); this differs from fitted(), which returns the trial-scaled expected count at the training design.

se.fit

If TRUE, also return the link-scale standard error and credible bounds. With an included SPDE field the SE propagates the joint (fixed-effect, field) posterior precision at the fitted hyperparameters – including the cross term – conditional on (range, sigma) (a nested fit's hyperparameter-grid spread is not propagated, so the bound is mildly optimistic when that posterior is wide). Integer-nu, no-RE SPDE fits only; other layouts decline with an explanation.

level

Credible-interval level (default 0.95).

include_field

For a continuous-spatial fit (SPDE, HSGP, or GP/NNGP), add the kriged field to the prediction (default TRUE). FALSE gives the fixed-effect (population) prediction. Ignored for fits with no continuous field. For an HSGP or GP/NNGP fit the field is added to the point prediction but its uncertainty is not yet propagated into se.fit (the interval reflects the fixed-effect covariance only).

...

Ignored.

Value

If se.fit = FALSE, a numeric vector. If se.fit = TRUE, a data frame with fit, se.fit (link scale), lower, upper on the requested scale.