predict.RprobitB_fit: Predict choices

In RprobitB: Bayesian Probit Choice Modeling

Predict choices

Description

Computes occasion-level choice probabilities over the retained posterior draws and predicts the alternative with the largest mean probability.

Usage

## S3 method for class 'RprobitB_fit'
predict(
  object,
  newdata = NULL,
  type = c("population", "conditional"),
  uncertainty = FALSE,
  level = 0.95,
  ghk_draws = 500L,
  progress = interactive(),
  ...
)

Arguments

object	[RprobitB_fit] Fitted choice model.
newdata	[data.frame | NULL] Data for prediction. NULL uses the fitted data. A response column is optional. In wide format, missing decider and occasion identifiers make every row a choice occasion of its own decider.
type	[character(1)] Which coefficients to predict with: "population" integrates over the estimated population distribution of the random coefficients and applies to any decider. "conditional" uses the posterior random coefficients and class allocations of the deciders that were observed when fitting the model, which newdata must then name.
uncertainty	[logical(1)] Add posterior standard deviations and credible intervals?
level	[numeric(1)] Probability of the credible intervals.
ghk_draws	[integer(1)] Number of draws of the GHK simulator for multivariate normal probabilities of more than three dimensions, see oeli::pmvnorm().
progress	[logical(1)] Show progress?
...	Currently not used.

Details

Conditional prediction is based on the individual-level parameters (Train, 2009, Chapters 11 and 12): the posterior distribution of a decider's coefficients given their observed choices, which the Gibbs sampler provides as draws when the model is fitted with save_individual_draws = TRUE.

Value

A data.frame with one row per choice occasion, identifier columns, .prediction, and one ⁠probability_*⁠ column per alternative. If uncertainty = TRUE, ⁠sd_*⁠, ⁠lower_*⁠, and ⁠upper_*⁠ columns are added.

References

\insertRef

Train2009RprobitB

Examples

set.seed(1)
model <- fit(
  choice ~ x | 0,
  random_effects = "x",
  dgp_parameters = list(beta = c(x = 1), Omega = matrix(0.5)),
  n_deciders = 20,
  iterations = 300,
  warmup = 150,
  chains = 1,
  save_individual_draws = TRUE
)
head(predict(model))
head(predict(model, type = "conditional"))
head(predict(model, uncertainty = TRUE))

### new choice occasions
new_data <- data.frame(deciderID = 21:22, x_A = c(1, -1), x_B = c(0, 0))
predict(model, newdata = new_data)

RprobitB documentation built on Sept. 20, 2026, 9:07 a.m.