LogisticNormalMixture-class: `LogisticNormalMixture`

Description

LogisticNormalMixture is the class for standard logistic regression model with a mixture of two bivariate normal priors on the intercept and slope parameters.

Usage

LogisticNormalMixture(comp1, comp2, weightpar, ref_dose)
.DefaultLogisticNormalMixture()

Arguments

comp1: (ModelParamsNormal)
bivariate normal prior specification of the first component. See ModelParamsNormal for more details.
comp2: (ModelParamsNormal)
bivariate normal prior specification of the second component. See ModelParamsNormal for more details.
weightpar: (numeric)
the beta parameters for the weight of the first component. It must a be a named vector of length 2 with names a and b and with strictly positive values.
ref_dose: (number)
the reference dose $x*$ (strictly positive number).

Slots

comp1: (ModelParamsNormal)
bivariate normal prior specification of the first component.

comp2

(ModelParamsNormal)
bivariate normal prior specification of the second component.

weightpar

(numeric)
the beta parameters for the weight of the first component. It must a be a named vector of length 2 with names a and b and with strictly positive values.

ref_dose

(positive_number)
the reference dose.

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 $$(alpha0, alpha1) ~ w * Normal(mean1, cov1) + (1 - w) * Normal(mean2, cov2).$$ The weight w for the first component is assigned a beta prior B(a, b).

Examples

Run this code

my_model <- LogisticNormalMixture(
  comp1 = ModelParamsNormal(
    mean = c(-0.85, 1),
    cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2)
  ),
  comp2 = ModelParamsNormal(
    mean = c(1, 1.5),
    cov = matrix(c(1.2, -0.45, -0.45, 0.6), nrow = 2)
  ),
  weightpar = c(a = 1, b = 1),
  ref_dose = 50
)