Skip to contents

Predict from a fitted gllvmTMB model

Source: R/methods-gllvmTMB.R
predict.gllvmTMB_multi.Rd

Returns the linear predictor or inverse-link response at each observation in the training data, or at user-supplied newdata. For mixed-family fits, type = "response" uses the row's own trait/family inverse link rather than the first trait's link.

Usage

# S3 method for class 'gllvmTMB_multi'
predict(
  object,
  newdata = NULL,
  type = c("link", "response"),
  re_form = ~.,
  se.fit = FALSE,
  ...
)

Arguments

object

A fit returned by gllvmTMB().

newdata

Optional new data frame. If NULL, predictions are produced for the training rows.

type

One of "link" (default) or "response".

"response" includes the row's offset, so on a fit with an effort or support offset it returns an expected count at that effort, or a detection probability at that support – not a relative intensity. For a map or any effort-free comparison, set the offset variable to zero in newdata (e.g. newdata$log_support <- 0) and predict from that; the offset is re-evaluated against newdata, so this is exact rather than an approximation.

On a mixed-family fit the returned est column mixes scales by design (expected counts beside probabilities). The fit's family/source column is returned alongside it so each row's scale is identifiable.

re_form

Random-effect formula controlling which random effects are included in the predicted linear predictor. The default ~ . includes them; ~ 0, NA, and numeric 0 all request the fixed-effects-only / population-mean prediction. Any other value is not a supported form and warns rather than silently including the random effects. Honoured on both the training-row and the newdata path (before 0.7.1 it was read only on the newdata path, and there only as the literal ~ 0). For newdata with sites/species not present in the training data the random effects cannot be drawn, so those rows are fixed-effects-only regardless of re_form.

On newdata, predict() rebuilds the linear predictor in R and can re-add only some of the model's random-effect tiers (the unit-level rr/diag terms, propto, and the spatial SPDE field). Structured intercepts also include their shared and folded Psi effects at known source levels; ancestral prediction is not added. A fit carrying any other active tier gets a warning naming exactly what was omitted; newdata = NULL always returns the full conditional predictor.

se.fit

Logical, default FALSE. If TRUE, add an se.fit column: a conditional, fixed-effect-only, delta-method (Wald) standard error of est. "Conditional" means the random-effect contributions to the linear predictor are held fixed at their predicted (conditional-mode) values – their own uncertainty is not propagated, only the fixed-effect coefficients' sdreport() covariance is. This is a smaller quantity than a full marginal SE would be; it is also not a claim about coverage, which has not been measured for this quantity. type = "link" returns the SE of the linear predictor directly; type = "response" multiplies it by the local derivative of the per-row inverse link (the standard delta-method transform), so it approximates the SE of the inverse-link fitted value, not an exact one. Currently only supported for newdata = NULL (training rows), non-multinomial() fits, fits without an active mi() missing-covariate model, and fits with unit likelihood weights. A non-unit weighted objective has no certified prediction-standard-error route.

...

Unused.

Value

A data frame with the original row identifiers plus an est column on the requested link or response scale, and (if se.fit = TRUE) an se.fit column carrying the conditional delta-method standard error described above.