Conditional predictions — conditional_predict • mlumr

Generate absolute predictions at specific covariate values for both treatments.

Usage

conditional_predict(
  object,
  newdata = NULL,
  type = c("response", "link"),
  summary = TRUE,
  probs = c(0.025, 0.5, 0.975)
)

Arguments

object: An mlumr_fit object
newdata: Data frame of covariate values. If NULL, uses IPD covariate means.
type: "response" for probabilities, means, or rates; "link" for the fitted linear-predictor scale.
summary: Return summary (TRUE) or full draws (FALSE)
probs: Quantiles for summary

Value

A data frame with predictions for each treatment at each profile

See also

conditional_effects() for covariate-conditional treatment effects; predict.mlumr_fit() for population-level predictions.

Examples

if (FALSE) { # \dontrun{
conditional_predict(fit)
conditional_predict(fit, newdata = data.frame(age = 60, sex = 1))
} # }