Predict from a fitted mixeff LMM

Predictions follow the lme4 generic shape. In-sample predictions reuse the cached fitted/fixed values; new-data predictions are dispatched through the Rust predict_new contract.

Usage

# S3 method for class 'mm_lmm'
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,
  ...
)

# S3 method for class 'mm_lmm'
fitted(object, ...)

# S3 method for class 'mm_lmm'
residuals(
  object,
  type = c("response", "pearson", "deviance", "working"),
  scaled = FALSE,
  ...
)

# S3 method for class 'mm_glmm'
fitted(object, ...)

# S3 method for class 'mm_glmm'
residuals(object, type = c("response"), ...)

Arguments

object

A fitted mm_lmm 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.

scaled

Logical; when TRUE, residuals are divided by the residual scale.

Value

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