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Predict from MAIHDA Model

Source: R/predict_maihda.R
predict_maihda.Rd

Makes predictions from a fitted MAIHDA model, either at the stratum level or individual level.

Usage

predict_maihda(
  object,
  newdata = NULL,
  type = c("individual", "strata", "response", "link"),
  scale = c("response", "link"),
  allow_new_levels = FALSE,
  ...
)

Arguments

object

A maihda_model object from fit_maihda().

newdata

Optional data frame for making predictions. If NULL, uses the original data from model fitting.

type

Character string specifying prediction type:

  • "individual": Individual-level predictions including random effects

  • "strata": Stratum-level predictions (random effects only). For a longitudinal (growth-curve) fit a stratum is a trajectory, so this returns the per-stratum trajectory parameters (baseline deviation, random intercept and random slope(s)) rather than a single random effect.

For backward compatibility, "link" or "response" may also be passed here and will be interpreted as individual-level predictions on that scale.

scale

Character string specifying the prediction scale for individual-level predictions: "response" (default) or "link". For a cumulative (ordinal) model the "link" scale is the latent location \(\eta\) and the "response" scale is the expected category score \(\sum_k k P(Y = k)\) (categories scored 1..K in their declared order). For an aggregated-binomial fit (an lme4 cbind(success, failure) or a brms success | trials(n)) the "response" scale is the per-trial probability on both engines (not the expected success count).

allow_new_levels

Logical. By default (FALSE) a stratum in newdata that the model never saw – whether supplied directly as a stratum column or rebuilt from the grouping variables – is an error, for every engine, matching lme4's default. Set TRUE to instead predict unseen strata with the stratum random effect dropped (treated as zero), while keeping any other random effect the row participates in (e.g. a contextual (1 | school) intercept from fit_maihda(context = ), or a longitudinal growth term) – the same behaviour as lme4's allow.new.levels, which zeroes only the unseen level's effect and keeps seen ones. For the usual single-stratum model the stratum is the only random effect, so this is the population-average (fixed-effects-only) prediction. This affects type = "individual" only: a stratum-level prediction (type = "strata") has no random effect to report for an unseen stratum, so unseen strata remain an error there regardless.

...

Additional arguments passed to predict method of underlying model.

Value

Depending on type:

  • For "individual": A numeric vector of predicted values on the requested scale

  • For "strata": A data frame with stratum ID and predicted random effect. When newdata is supplied, the result is restricted to the strata present in newdata (and a stratum the model never saw is an error, as for "individual"); when newdata is NULL, every training stratum is returned.

Examples

# \donttest{
strata_result <- make_strata(maihda_sim_data, vars = c("gender", "race"))
model <- fit_maihda(health_outcome ~ age + (1 | stratum), data = strata_result$data)

# Individual predictions
pred_ind <- predict_maihda(model, type = "individual")

# Stratum predictions
pred_strata <- predict_maihda(model, type = "strata")
# }