LogisticLogNormalMixture-class | R Documentation |
LogisticLogNormalMixture
LogisticLogNormalMixture
is the class for standard logistic model with
online mixture of two bivariate log normal priors.
LogisticLogNormalMixture(mean, cov, ref_dose, share_weight)
.DefaultLogisticLogNormalMixture()
mean |
( |
cov |
( |
ref_dose |
( |
share_weight |
( |
This model can be used when data is arising online from the informative
component of the prior, at the same time with the data of the trial of
main interest. Formally, this is achieved by assuming that the probability
of a DLT at dose x
is given by
p(x) = \pi * p1(x) + (1 - \pi) * p2(x)
where \pi
is the probability for the model p(x)
being the same
as the model p1(x)
, which is the informative component of the prior.
From this model data arises in parallel: at doses xshare
, DLT information
yshare
is observed, in total nObsshare
data points (see DataMixture
).
On the other hand, 1 - \pi
, is the probability of a separate model
p2(x)
. Both components have the same log normal prior distribution,
which can be specified by the user, and which is inherited from the
LogisticLogNormal
class.
share_weight
(proportion
)
the prior weight for the share component
p_{1}(x)
.
Typically, end users will not use the .DefaultLogNormalMixture()
function.
ModelLogNormal
, LogisticNormalMixture
,
LogisticNormalFixedMixture
.
# Decide on the dose grid and MCMC options.
dose_grid <- 1:80
my_options <- McmcOptions()
# Classic model.
my_model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 50
)
empty_data <- Data(doseGrid = dose_grid)
prior_samples <- mcmc(empty_data, my_model, my_options)
plot(prior_samples, my_model, empty_data)
# Set up the mixture model and data share object.
model_share <- LogisticLogNormalMixture(
share_weight = 0.1,
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 50
)
empty_data_share <- DataMixture(
doseGrid = dose_grid,
xshare = rep(c(10, 20, 40), each = 4),
yshare = rep(0L, 12),
)
# Compare with the resulting prior model.
prior_samples_share <- mcmc(empty_data_share, model_share, my_options)
plot(prior_samples_share, model_share, empty_data_share)
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