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

This function uses generalized simulated annealing to optimize 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)