predict.modelFits: predict.modelFits

In BayesianMCPMod: Simulate, Evaluate, and Analyze Dose Finding Trials with Bayesian MCPMod

predict.modelFits

Description

This function performs model predictions based on the provided model and dose specifications

Usage

## S3 method for class 'modelFits'
predict(
  object,
  doses = NULL,
  probability_scale = attr(object, "probability_scale"),
  ...
)

Arguments

object	A modelFits object containing information about the fitted model coefficients
doses	A vector specifying the doses for which a prediction should be done
probability_scale	A boolean variable to specify if the trial has a continuous or a binary outcome. Setting to TRUE will transform predictions from the logit scale to the probability scale, which can be desirable for a binary outcome. Default FALSE.
...	Currently without function

Value

a list with the model predictions for the specified models and doses

Examples

posterior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 1), sigma = 2),
                       DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 3, s = 1.2), sigma = 2),
                       DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) ,
                       DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 6, s = 1.2), sigma = 2) ,
                       DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 6.5, s = 1.1), sigma = 2))
models         <- c("emax", "exponential", "sigEmax", "linear", "betaMod")
dose_levels    <- c(0, 1, 2, 4, 8)
fit            <- getModelFits(models      = models,
                               posterior   = posterior_list,
                               dose_levels = dose_levels)

predict(fit, doses = c(0, 1, 3, 4, 6, 8))

BayesianMCPMod documentation built on Feb. 23, 2026, 5:06 p.m.