Predict from a fitted mixeff GLMM — predict.mm

GLMM predictions are computed on the R side from the stored fixed effects (population, re.form = NA) or fixed effects plus conditional modes (re.form = NULL), then mapped through the family link. This mirrors lme4::predict.merMod for generalized models: type = "link" returns the linear predictor and type = "response" the mean.

Usage

# S3 method for class 'mm_glmm'
predict(
  object,
  newdata = NULL,
  re.form = NULL,
  allow.new.levels = FALSE,
  type = c("response", "link"),
  se.fit = FALSE,
  interval = c("none", "confidence", "prediction"),
  level = 0.95,
  ...
)

Arguments

object: A fitted mm_glmm object.
newdata: Optional new data. Must be a data.frame containing every variable referenced by the model's formula. Categorical levels must either match the training factor levels or trigger the allow.new.levels policy.
re.form: Random-effects conditioning, following lme4's basic convention. NULL returns conditional predictions; NA (or ~0) returns population-level (fixed-effect) predictions. Conditioning on a subset of grouping factors via a one-sided formula is not supported by the current Rust contract and raises mm_inference_unavailable.
allow.new.levels: When FALSE (default), unseen grouping levels in newdata raise mm_inference_unavailable through the Rust NewReLevels::Error policy. When TRUE, unseen levels are replaced by the population mean (zero random effect), matching lme4::predict(allow.new.levels = TRUE).
type: Prediction scale. Gaussian LMMs use the same values for "response" and "link".
se.fit: Logical; when TRUE, returns a list with fit and se.fit. For population predictions (re.form = NA) the standard error is the Wald SE of the fixed-effect linear predictor, sqrt(diag(X V X')). For conditional predictions (re.form = NULL) the SE comes from the engine prediction-variance payload, which adds the random-effect contribution (BLUP variance and the fixed/random covariance). Rows the engine cannot certify — e.g. unseen grouping levels under allow.new.levels = TRUE — return NA with the engine's reason in the mm_reason attribute. (lme4::predict.merMod offers no conditional SE at all.)
interval: Interval type: "confidence" for the fitted mean or "prediction" for a new observation (adds the residual variance). Population (re.form = NA) intervals are fit +/- z*se computed R-side; conditional (re.form = NULL) bounds come from the engine prediction-variance payload. Returns a matrix with fit/lwr/upr.
level: Confidence level for interval / se.fit intervals.
...: Reserved for generic compatibility.

Value

A numeric vector, or a list with fit and se.fit when se.fit = TRUE.

Details

In-sample response predictions reuse the engine's certified fitted values.

Standard errors and confidence intervals: population (re.form = NA) SEs are the fixed-effect Wald SE mapped through the link by the delta method; conditional (re.form = NULL) SEs and confidence bounds come from the engine prediction-variance payload. The engine certifies these rows for method = "joint_laplace" fits and for default pirls_profiled fits whose post-fit profiled-optimum certificate is issued (per-row status "available"). Uncertified fits (e.g. singular fits, or fits whose certificate fails) keep status "degraded", and their conditional SEs and bounds are withheld as NA with the engine's reason in the mm_reason attribute — consistent with the package's "no fake certainty" contract.

Prediction (future-observation) intervals (interval = "prediction") are available for conditional, response-scale predictions: the engine returns quantiles of the plug-in predictive distribution (the family conditional distribution mixed over link-scale fitted-mean uncertainty), so bounds are integers for count families and support points for Bernoulli. They are refused with a typed condition on the link scale (future observations are response-scale objects), for population-level requests, and for grouped binomial fits (the future trial count is not representable in newdata).