LogisticNormal-class | R Documentation |

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.

`mean`

the prior mean vector

*μ*`cov`

the prior covariance matrix

*Σ*`prec`

the prior precision matrix

*Σ^{-1}*`refDose`

the 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)

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