prob {crmPack}

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

Description

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

Usage

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

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

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

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

Arguments

dose

the dose

model

the Model object

samples

the Samples

...

unused

Value

the vector (for Model objects) of probability samples.

Functions

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

• prob(dose = numeric, model = ModelTox, samples = missing): Compute the probability for a given dose, 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 Prob(DLT | dose=50)
tox.prob <- prob(dose=50, model=model, samples=samples)




# create data from the 'DataDual' class
data <- DataDual(x = c(25,50,25,50,75,300,250,150),
                 y = c(0,0,0,0,0,1,1,0),
                 w = c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                 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)

tox.prob <- prob(dose=100, model = DLEmodel, samples = DLEsamples)



# create data from the 'DataDual' class
data <- DataDual(x = c(25,50,25,50,75,300,250,150),
                 y = c(0,0,0,0,0,1,1,0),
                 w = c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                 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)

tox.prob <- prob(dose=100, model = DLEmodel)