crmPack (version 1.0.6)

prob: 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 numeric,Model,Samples
prob(dose, model, samples, ...)
# S4 method for numeric,ModelTox,Samples
prob(dose, model, samples, ...)
# S4 method for numeric,ModelTox,missing
prob(dose, model, samples, ...)

Value

the vector (for Model objects) of probability samples.

Arguments

dose

the dose

model

the Model object

samples

the Samples

...

unused

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)