DALogisticLogNormal-class | R Documentation |
DALogisticLogNormal
DALogisticLogNormal
is the class for the logistic model with bivariate
(log) normal prior and data augmentation. This class inherits from the
LogisticLogNormal
class.
DALogisticLogNormal(npiece = 3, l, c_par = 2, cond_pem = TRUE, ...)
.DefaultDALogisticLogNormal()
npiece |
( |
l |
( |
c_par |
( |
cond_pem |
( |
... |
Arguments passed on to
|
npiece
(number
)
the number of pieces in the PEM
.
l
(numeric
)
a vector used in the lambda prior.
c_par
(numeric
)
a parameter used in the lambda prior; according to
Liu's paper, c_par = 2
is recommended.
cond_pem
(flag
)
is a conditional piecewise-exponential model used?
(default). Otherwise an unconditional model is used.
We still need to include here formula for the lambda prior.
Typically, end users will not use the .DefaultDALogisticLogNormal()
function.
ModelLogNormal
, LogisticNormal
, LogisticLogNormal
.
npiece <- 10
Tmax <- 60 # nolintr
lambda_prior <- function(k) {
npiece / (Tmax * (npiece - k + 0.5))
}
model <- DALogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 56,
npiece = npiece,
l = as.numeric(t(apply(as.matrix(c(1:npiece), 1, npiece), 2, lambda_prior))),
c_par = 2
)
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