Predict from a fitted mixeff LMM — predict.mm

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,
  na.action = stats::na.pass,
  offset = NULL,
  ...
)

# 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("deviance", "pearson", "working", "response"),
  scaled = FALSE,
  ...
)

Arguments

object: A fitted mm_lmm object.
newdata: Optional new data. Must be a data.frame containing every variable the requested prediction needs (the fixed-effect variables, plus the grouping and slope variables of the random terms used); other columns are ignored. Grouping columns may be factor, character, or integer (coerced the way they were at fit time); fixed-effect factor and character columns are matched to the training levels, and a level not seen when fitting is refused. Stateful terms (poly(), scale(), ns(), ...) are evaluated with the training basis, as in stats::lm().
re.form: Random-effects conditioning, following lme4. NULL returns conditional predictions; NA (or ~0) returns population-level (fixed-effect) predictions; a one-sided formula naming some of the model's random terms (e.g. ~ (1 | subject)) conditions on those terms' conditional modes only. Standard errors and intervals are not available for such partial conditioning.
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". For residuals(), the residual type, as in lme4: LMMs default to "response" (y - mu, also "working"), with "pearson"/"deviance" the weighted residuals sqrt(w) * (y - mu); GLMMs default to "deviance" and also offer "pearson" ((y - mu) * sqrt(w / V(mu))), "working" and "response".
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.
na.action: Missing-value handling for newdata, as in lme4::predict.merMod(): the default stats::na.pass returns NA for rows with a missing value in a variable the prediction needs; stats::na.omit drops them and stats::na.exclude pads them back as NA. In-sample predictions of a fit made with na.action = na.exclude are padded to the original rows, like fitted() and residuals().
offset: Offset for newdata rows when the model was fitted with the offset = argument: a numeric vector (one value per newdata row) or an expression evaluated in newdata. Offsets written in the formula (offset(log(t))) are evaluated from newdata automatically. (lme4 silently drops an offset = argument offset for new data; mixeff refuses instead of guessing.)
...: Reserved for generic compatibility.
scaled: Logical; when TRUE, residuals are divided by sigma(object) (once), as in lme4.

Value

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