LogisticNormal-class: 'LogisticNormal'

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

LogisticNormal

Description

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

Usage

LogisticNormal(mean, cov, ref_dose = 1)

.DefaultLogisticNormal()

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, alpha1) ~ Normal(mean, cov).

Note

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

See Also

ModelLogNormal, LogisticLogNormal, LogisticLogNormalSub, ProbitLogNormal, ProbitLogNormalRel, LogisticNormalMixture.

Examples

# Define the dose-grid.
empty_data <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

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

my_options <- McmcOptions(burnin = 10, step = 2, samples = 100)

samples <- mcmc(empty_data, my_model, my_options)
samples

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