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Predictions from ML-UMR model

Source: R/predict.R
predict.mlumr_fit.Rd

Generate population-average absolute-outcome predictions in the index and comparator populations.

Usage

# S3 method for class 'mlumr_fit'
predict(
  object,
  population = c("both", "index", "comparator"),
  type = c("response", "link"),
  summary = TRUE,
  probs = c(0.025, 0.5, 0.975),
  ...
)

Arguments

object

An mlumr_fit object

population

Which population: "both", "index", or "comparator"

type

Prediction type: "response" or "link". For "response": probabilities (binomial), means (normal), or rates (poisson). For "link": mean linear predictor on the fitted link scale (logit, probit, cloglog, log, or identity). The link-scale values are computed directly from parameter draws as E[eta], not as link(E[g^{-1}(eta)]), to avoid Jensen's inequality bias.

summary

Return summary statistics (TRUE) or full posterior draws (FALSE)

probs

Quantiles for summary (default c(0.025, 0.5, 0.975))

...

Additional arguments (unused)

Value

A data frame with predictions. When type = "link", values are mean linear predictors computed directly from parameter draws (avoiding Jensen's inequality bias).

Details

Marginalization on non-identity links. For type = "response" the reported values are E[g^{-1}(eta)] — the posterior expectation of the inverse-link-transformed linear predictor — not g^{-1}(E[eta]). The two differ whenever g is non-linear (logit, probit, cloglog, log) by Jensen's inequality. In the index population the expectation is taken over IPD individuals; in the comparator population it is taken over the integration points constructed by add_integration() from the AgD moments. This is the correct population-average prediction for an individual randomly drawn from that population, and it matches what the Stan generated quantities block computes. For the link scale (type = "link") the reported value is E[eta], a linear functional, and the two interpretations coincide.

See also

marginal_effects() for treatment-effect summaries; conditional_predict() and conditional_effects() for predictions at specific covariate profiles.