Quantiles2LogisticNormal: Convert prior quantiles (lower, median, upper) to logistic...

In crmPack: Object-Oriented Implementation of CRM Designs

Convert prior quantiles (lower, median, upper) to logistic (log) normal model

Description

This function uses generalised simulated annealing to optimise a LogisticNormal model to be as close as possible to the given prior quantiles.

Usage

Quantiles2LogisticNormal(
  dosegrid,
  refDose,
  lower,
  median,
  upper,
  level = 0.95,
  logNormal = FALSE,
  parstart = NULL,
  parlower = c(-10, -10, 0, 0, -0.95),
  parupper = c(10, 10, 10, 10, 0.95),
  seed = 12345,
  verbose = TRUE,
  control = list(threshold.stop = 0.01, maxit = 50000, temperature = 50000, max.time =
    600)
)

Arguments

dosegrid	the dose grid
refDose	the reference dose
lower	the lower quantiles
median	the medians
upper	the upper quantiles
level	the credible level of the (lower, upper) intervals (default: 0.95)
logNormal	use the log-normal prior? (not default) otherwise, the normal prior for the logistic regression coefficients is used
parstart	starting values for the parameters. By default, these are determined from the medians supplied.
parlower	lower bounds on the parameters (intercept alpha and the slope beta, the corresponding standard deviations and the correlation.)
parupper	upper bounds on the parameters
seed	seed for random number generation
verbose	be verbose? (default)
control	additional options for the optimisation routine, see GenSA for more details

Value

a list with the best approximating model (LogisticNormal or LogisticLogNormal), the resulting quantiles, the required quantiles and the distance to the required quantiles, as well as the final parameters (which could be used for running the algorithm a second time)

crmPack documentation built on June 26, 2024, 5:07 p.m.