LogisticLogNormal-class: 'LogisticLogNormal'

In Roche/crmPack: Object-Oriented Implementation of CRM Designs

LogisticLogNormal

Description

LogisticLogNormal is the class for the usual logistic regression model with a bivariate normal prior on the intercept and log slope.

Usage

LogisticLogNormal(mean, cov, ref_dose = 1)

.DefaultLogisticLogNormal()

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 natural logarithm of the dose x divided by the reference dose x*, i.e.:

logit[p(x)] = alpha0 + alpha1 * log(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

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

See Also

ModelLogNormal, LogisticNormal, LogisticLogNormalSub, ProbitLogNormal, ProbitLogNormalRel, LogisticLogNormalMixture, DALogisticLogNormal.

Examples

my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 50
)
my_model

Roche/crmPack documentation built on April 30, 2024, 3:19 p.m.