Standard logistic model with bivariate normal prior
This is the usual logistic regression model with a bivariate normal prior on the intercept and slope.
The covariate is the natural logarithm of the dose x divided by the reference dose x^{*}:
logit[p(x)] = α + β \cdot \log(x/x^{*})
where p(x) is the probability of observing a DLT for a given dose x.
The prior is
(α, β) \sim Normal(μ, Σ)
The slots of this class contain the mean vector, the covariance and precision matrices of the bivariate normal distribution, as well as the reference dose.
meanthe prior mean vector μ
covthe prior covariance matrix Σ
precthe prior precision matrix Σ^{-1}
refDosethe reference dose x^{*}
# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))
model <- LogisticNormal(mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
refDose = 50)
options <- McmcOptions(burnin=100,
step=2,
samples=1000)
options(error=recover)
mcmc(emptydata, model, options)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.