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 add_par_df function.
Add more data about this model here.
makeModel, addObservationalModel, add_par_df, 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 = add_par_df("sigma", 0.0001, 4, "unif", 0.0001, 4) # observational model,
)