observationalModel.RdThis data format represents information about the observation model
A list with the following columns:
Unique identifier for each biomarker.
Description of the model using the makeModel and addObservationalModel functions.
Description of the prior distribution using the addPrior function.
Add more data about this model here.
makeModel, addObservationalModel, addPrior, for related functions.
# Example usage. This describes the observation model for a SARS-CoV-2 delta wave using IgG data. First define the log likelihood function, which is cauchy, with a LOD at a titre value of log10(40):
obsFunction = function(ll, titre_val, titre_est, pars) {
if (titre_val <= log10(40)) {
ll <- ll + pcauchy(log10(40), titre_est, pars[1], log.p = TRUE)
} else {
ll <- ll + dcauchy(titre_val, titre_est, pars[1], log = TRUE)
}
ll
}
# Now define the observation model in the format required for the rjmc package:
observationalModel <- list(
names = c("IgG"),
model = makeModel(addObservationalModel("IgG", c("sigma"), obsFunction)),
prior = addPrior("sigma", 0.0001, 4, "unif", 0.0001, 4) # observational model,
)