Predict from MAIHDA Model — predict

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. A non-longitudinal fit whose stratum random effects include random slopes (a hand-written (1 + x | stratum)) is an error here, as in summary(): the single value this would return is the intercept alone – the stratum effect only where the slope variables are zero.

"link" or "response" is also accepted and gives 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. FALSE (default): a stratum in newdata that the model never saw is an error, as in lme4. TRUE: an unseen stratum is predicted at a zero random effect for that stratum; any other random effect the row has (a context intercept, a longitudinal growth term) is kept. That is the prediction for a stratum whose effect is zero, not a response-scale population average: the two agree on the link scale and under an identity link, but under a log link with stratum variance $\tau^2$ the population mean is larger by a factor $\exp(\tau^2/2)$, and under a logit link the population probability lies closer to 0.5. The response-scale value returned is the median, not the mean, of the stratum-specific means. Applies to type = "individual" only; with type = "strata" an unseen stratum is always an error.

...

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, one per row of newdata – or, when newdata is NULL, one per ANALYTIC row, matching nrow(object$data). A model fitted with na.action = na.exclude is not padded back out to the original input rows the way predict() on the underlying fit is, so the result can always be bound to object$data.
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")
# }