Make predictions from a fitted splinemixmeta model — predict.splinemixmeta • splinemixmeta

Make predictions from a fitted splinemixmeta model

Usage

# S3 method for class 'splinemixmeta'
predict(
  object,
  include_smooths = TRUE,
  include_REs = FALSE,
  include_residuals = FALSE,
  type = "outcome",
  ...
)

Arguments

object: A fitted mixmeta object returned from splinemixmeta()
include_smooths: TRUE to include the smooth (spline) terms in predictions. Typically one wants these.
include_REs: TRUE to include any additional random effects (beyond the smooths) that were provided in the random argument to splinemixmeta. Omit these if you want to see just the spline predictions.
include_residuals: TRUE to include the random effects (one for each datum) for residual variation not accounted for in the measurement variation (S). Only matters if residual_re = TRUE when calling splinemixmeta, which should typically be the case. Typically one does not want these in predictions.
type: Type of predictions. This can be "outcome" or "residual" and will be passed to the type argument of blup.splinemixmeta().
...: Additional arguments (currently unused)

Value

A matrix with columns "blup" for the predicted values, "se" for the standard errors of the predictions, and "vcov" for the variance of the predictions. These are returned from mixmeta::blup() with vcov=TRUE and se=TRUE.

Details

This is a convenience function that calls blup (i.e. blup.splinemixmeta) without requiring one to know which random-effects "levels" of the fitted mixmeta object correspond to which parts of the model. Instead one can simply choose whether to include smooths, random effects, and/or residuals. For more fine-grained control (such as including one spline term but not another), one can use blup() directly.