prob | R Documentation |
Compute the probability for a given dose, given model and samples
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, ...)
dose |
the dose |
model |
the |
samples |
the |
... |
unused |
the vector (for Model
objects) of probability
samples.
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
# 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)
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