crmPack (version 1.0.6)

LogisticNormal-class: Standard logistic model with bivariate normal prior

Description

This is the usual logistic regression model with a bivariate normal prior on the intercept and slope.

Slots

mean

the prior mean vector \(\mu\)

cov

the prior covariance matrix \(\Sigma\)

prec

the prior precision matrix \(\Sigma^{-1}\)

refDose

the reference dose \(x^{*}\)

Details

The covariate is the natural logarithm of the dose \(x\) divided by the reference dose \(x^{*}\):

$$logit[p(x)] = \alpha + \beta \cdot \log(x/x^{*})$$ where \(p(x)\) is the probability of observing a DLT for a given dose \(x\).

The prior is $$(\alpha, \beta) \sim Normal(\mu, \Sigma)$$

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.

Examples

# 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)