demo_Cutoffscreening.GP {BayesianPlatformDesignTimeTrend}

R Documentation

A demo for cutoff screening using Bayesian optimisation

Description

This function does a cutoff screening for trial simulation using Bayesian optimisation.

Usage

demo_Cutoffscreening.GP(
  ntrials = 1000,
  trial.fun = simulatetrial,
  grid.inf = list(start.length = 10, grid.min = NULL, grid.max = NULL, confidence.level =
    0.95, grid.length = 5000, change.scale = FALSE, noise = TRUE, errorrate = 0.1,
    simulationerror = 0.01, iter.max = 15, plotornot = FALSE),
  input.info = list(response.probs = c(0.15, 0.15, 0.15, 0.15), ns = c(120, 240, 360,
    480, 600), max.ar = 0.85, rand.algo = "Urn", max.deviation = 3, model.inf =
    list(model = "tlr", ibb.inf = list(pi.star = 0.5, pess = 2, betabinomialmodel =
    ibetabinomial.post), tlr.inf = list(beta0_prior_mu = 0, beta1_prior_mu = 0,
    beta0_prior_sigma = 2.5, beta1_prior_sigma = 2.5, beta0_df = 7, beta1_df = 7, reg.inf
    = "main", variable.inf = "Fixeffect")), Stop.type = "Early-OBF", Boundary.type =
    "Symmetric", Random.inf = list(Fixratio = FALSE, 
     Fixratiocontrol = NA,
    BARmethod = "Thall", Thall.tuning.inf = list(tuningparameter = "Unfixed", fixvalue =
    1)), trend.inf = list(trend.type = "step", trend.effect = c(0, 0, 0, 0),
    trend_add_or_multip = "mult")),
  cl = 2
)

Arguments

ntrials	A numeric variable indicating how many trial replicates you want to run
trial.fun	The function of trial simulation, related to MainFunction.R
grid.inf	A list of grid information to create start grid and extend grid for cutoff screening. 'start.length' is the size of start grid. Default is 10. 'grid.min' is the lower bound for screening grid. 'grid.max' is the upper bound for screening grid. 'errorrate' refers to the target of type I error rate or family-wise error rate. 'confidence.level' is a numeric value indicating the confidence level of estimate. Default is 0.95. 'grid.length' is the accuracy of grid. Default is 5000. 'change.scale' is a logic value indicating whether we want to change scale when doing Gaussian process. Default is FALSE. 'noise' is a logic value indicating whether the input x is noisy. Default is TRUE. 'simulationerror' is a numeric value indicating the tolerable error for simulated type I error rate. Default is 0.01, 'iter.max' is a numeric value indicating the maximum number of evaluations. Default is 15. 'plotornot' is a logic value indicating whether the errorrate vs grid plot needed to be generated at each iteration. Default is FALSE.
input.info	A list of input information including all information required for trial simulation.
cl	A numeric variable indicating how many cores you want to use in parallel programming.

Value

A vector of recommended cutoff. The final value is the latest recommended value. A plot for all tested cutoff and error rate

Author(s)

Ziyan Wang

Examples

demo_Cutoffscreening.GP(ntrials = 2, cl = 2,
    grid.inf = list(
    start.length = 10,
    confidence.level = 0.95,
    grid.length = 5000,
    change.scale = FALSE,
    noise = TRUE,
    errorrate = 0.1,
    simulationerror = 0.01,
    iter.max = 15,
    plotornot = FALSE))

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