Description Usage Arguments Methods (by class) Examples
Compute the doses for a given probability, given model and samples
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prob 
the probability 
model 
the 
samples 
the 
... 
unused 
prob = numeric,model = ModelTox,samples = Samples
: Compute the doses for a given probability, given
Pseudo DLE model with samples
prob = numeric,model = ModelTox,samples = missing
: Compute the dose for a given probability and a given
Pseudo DLE model without samples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53  # 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)

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