LogisticNormalMixture-class | R Documentation |
LogisticNormalMixture
LogisticNormalMixture
is the class for standard logistic regression model
with a mixture of two bivariate normal priors on the intercept and slope parameters.
LogisticNormalMixture(comp1, comp2, weightpar, ref_dose)
.DefaultLogisticNormalMixture()
comp1 |
( |
comp2 |
( |
weightpar |
( |
ref_dose |
( |
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)
.
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.
The weight of the two normal priors is a model parameter, hence it is a flexible mixture. This type of prior is often used with a mixture of a minimal informative and an informative component, in order to make the CRM more robust to data deviations from the informative component.
Typically, end-users will not use the .DefaultLogisticNormalMixture()
function.
ModelParamsNormal
, ModelLogNormal
,
LogisticNormalFixedMixture
, LogisticLogNormalMixture
.
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
)
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