Standard logistic model with bivariate normal prior

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Description

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

Details

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.

Slots

mean

the prior mean vector μ

cov

the prior covariance matrix Σ

prec

the prior precision matrix Σ^{-1}

refDose

the reference dose x^{*}

Examples

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model <- LogisticNormal(mean = c(-0.85, 1),
                        cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                        refDose = 50)

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