R/predictive_interval.R

In OncoBayes2: Bayesian Logistic Regression for Oncology Dose-Escalation Trials

#' Posterior predictive intervals
#'
#' Posterior predictive intervals of the model.
#'
#' @template args-methods
#' @template args-prob
#' @template args-dots-ignored
#'
#' @details
#'
#' Reports for each row of the input data set the predictive interval
#' according to the fitted model.
#'
#' @return Matrix with as many rows as the input data set and two
#'     columns which contain the lower and upper quantile
#'     corresponding to the central probability mass \code{prob} for
#'     the number of responses of the predictive distribution.
#'
#' @template start-example
#' @examples
#' example_model("single_agent", silent=TRUE)
#'
#' predictive_interval(blrmfit)
#'
#' @template stop-example
#'
#' @method predictive_interval blrmfit
#' @aliases predictive_interval
#' @export
predictive_interval.blrmfit <- function(object, prob=0.95, newdata, ...) {
    ##yrep <- posterior_predict(object, newdata=newdata)
    ##rstantools::predictive_interval(yrep, prob=prob)
    assert_number(prob, lower=0, upper=1, finite=TRUE)
    s <- summary(object, newdata=newdata, prob=prob, predictive=TRUE, transform=FALSE)
    cp  <- c(0.5- prob/2, 0.5+ prob/2)
    as.matrix(s[,c(3,5)])
}

#' @method predictive_interval blrm_trial
#' @export
predictive_interval.blrm_trial <- function(object, prob=0.95, newdata, ...) {
    .assert_is_blrm_trial_and_prior_is_set(object)
    if(missing(newdata)) {
        return(predictive_interval.blrmfit(object$blrmfit, prob=prob, newdata=object$data, ...))
    } else {
        return(predictive_interval.blrmfit(object$blrmfit, prob=prob, newdata=newdata, ...))
    }
}