DALogisticLogNormal

DALogisticLogNormal is the class for the logistic model with bivariate (log) normal prior and data augmentation. This class inherits from the LogisticLogNormal class.

Usage

DALogisticLogNormal(npiece = 3, l, c_par = 2, cond_pem = TRUE, ...)

.DefaultDALogisticLogNormal()

Arguments

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.

...

Arguments passed on to LogisticLogNormal

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).

Slots

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.

Note

We still need to include here formula for the lambda prior.

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

See also

ModelLogNormal, LogisticNormal, LogisticLogNormal.

Examples

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
)