dose {crmPack}

Compute the doses for a given probability, given model and samples

Description

Compute the doses for a given probability, given model and samples

Usage

dose(prob, model, samples, ...)

## S4 method for signature 'numeric,Model,Samples'
dose(prob, model, samples, ...)

## S4 method for signature 'numeric,ModelTox,Samples'
dose(prob, model, samples, ...)

## S4 method for signature 'numeric,ModelTox,missing'
dose(prob, model, samples, ...)

Arguments

prob

the probability

model

the Model

samples

the Samples

...

unused

Functions

• dose(prob = numeric, model = ModelTox, samples = Samples): Compute the doses for a given probability, given Pseudo DLE model with samples

• dose(prob = numeric, model = ModelTox, samples = missing): Compute the dose for a given probability and a given Pseudo DLE model without samples

Examples

# create some data
data <- Data(x =c (0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y = c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid = c(0.1, 0.5, 1.5, 3, 6,
                          seq(from=10, to=80, by=2)))

# Initialize a  model
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=matrix(c(1, -0.5, -0.5, 1),
                                      nrow=2),
                           refDose=56)

# Get samples from posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Posterior for the dose achieving Prob(DLE) = 0.45
TD45 <- dose(prob=0.45,model=model,samples=samples)


# create data from the 'Data" (or DataDual') class
data <- Data(x = c(25,50,25,50,75,300,250,150),
             y = c(0,0,0,0,0,1,1,0),
             doseGrid = seq(25,300,25))

## Initialize a model from 'ModelTox' class e.g using 'LogisticIndepBeta' model
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

options <- McmcOptions(burnin=100, step=2, samples=200)
DLEsamples <- mcmc(data=data,model=DLEmodel,options=options)

TD45 <- dose(prob=0.45, model = DLEmodel,samples = DLEsamples)


# create data from the 'Data' (or 'DataDual') class
data <- Data(x = c(25,50,25,50,75,300,250,150),
                 y = c(0,0,0,0,0,1,1,0),
                 doseGrid = seq(25,300,25))

## Initialize a model from 'ModelTox' class e.g using 'LogisticIndepBeta' model
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

TD45 <- dose(prob=0.45, model = DLEmodel)