collect cross-sectional data
simcs.tinf(
lambda,
n_samples,
age_range,
age_fixed = NA,
antigen_isos,
n_mcmc_samples = 0,
renew_params = FALSE,
...
)an array() with dimensions
n_samples, length(antigen_isos) + 1,
where rows are observations and columns are age and biomarkers y(t)
seroconversion rate (in events/person-day)
number of samples n_samples (= nr of simulated records)
age range to use for simulating data, in days
age_fixed for parameter sample (age_fixed = NA for age at infection)
character vector with one or more antibody names.
Values must match curve_params.
when n_mcmc_samples is in 1:4000,
a fixed posterior sample is used
when n_mcmc_samples = 0 a random sample is chosen
renew_params = TRUE
generates a new parameter set for each infection
renew_params = FALSE
keeps the one selected at birth, but updates baseline y0
Arguments passed on to simresp.tinf
predparan array() with dimensions named:
antigen_iso
parameter
obs