ModelLogNormal

ModelLogNormal is the class for a model with a reference dose and bivariate normal prior on the model parameters alpha0 and natural logarithm of alpha1, i.e.: $$(alpha0, log(alpha1)) ~ Normal(mean, cov),$$. Transformations other than log, e.g. identity, can be specified too in priormodel slot. The parameter alpha1 has a log-normal distribution by default to ensure positivity of alpha1 which further guarantees exp(alpha1) > 1. The slots of this class contain the mean vector, the covariance and precision matrices of the bivariate normal distribution, as well as the reference dose. Note that the precision matrix is an inverse of the covariance matrix in the JAGS. All ("normal") model specific classes inherit from this class.

Usage

ModelLogNormal(mean, cov, ref_dose = 1)

.DefaultModelLogNormal()

Arguments

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

params

(ModelParamsNormal)
bivariate normal prior parameters.

ref_dose

(positive_number)
the reference dose.

Note

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

See also

ModelParamsNormal, LogisticNormal, LogisticLogNormal, LogisticLogNormalSub, ProbitLogNormal, ProbitLogNormalRel.