ProbitLogNormalRel

ProbitLogNormalRel is the class for probit regression model with a bivariate normal prior on the intercept and log slope.

Usage

ProbitLogNormalRel(mean, cov, ref_dose = 1)

.DefaultProbitLogNormalRel()

Arguments

mean

(numeric)
the prior mean vector.

cov

(matrix)
the prior covariance matrix. The precision matrix prec is internally calculated as an inverse of cov.

ref_dose

(number)
the reference dose \(x*\) (strictly positive number).

Details

The covariate is the dose \(x\) divided by a reference dose \(x*\), i.e.: $$probit[p(x)] = alpha0 + alpha1 * x/x*,$$ where \(p(x)\) is the probability of observing a DLT for a given dose \(x\). The prior $$(alpha0, log(alpha1)) ~ Normal(mean, cov).$$

Note

This model is also used in the DualEndpoint classes, so this class can be used to check the prior assumptions on the dose-toxicity model, even when sampling from the prior distribution of the dual endpoint model is not possible.

Typically, end users will not use the .DefaultProbitLogNormalRel() function.

See also

ModelLogNormal, LogisticNormal, LogisticLogNormal, LogisticLogNormalSub, ProbitLogNormal.

Examples

my_model <- ProbitLogNormalRel(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2)
)