Simulate antibody trajectories from priors only

Performs a prior predictive check by simulating antibody trajectories and measurements using only the prior distributions, before fitting the model to data. This is useful for assessing whether priors generate realistic antibody values for a given pathogen and assay.

Usage

simulate_prior_predictive(
  prepped_data,
  prepped_priors,
  n_sims = 1,
  seed = NULL
)

Arguments

prepped_data

A prepped_jags_data object from prep_data()

prepped_priors

A curve_params_priors object from prep_priors()

n_sims

integer Number of prior predictive simulations to generate (default = 1). If > 1, returns a list of simulated datasets.

seed

integer Optional random seed for reproducibility

Value

If n_sims = 1, a prepped_jags_data object with simulated antibody values replacing the observed values. If n_sims > 1, a list of such objects.

Details

This function:

1. Draws kinetic parameters from the prior distributions specified by prep_priors()

2. Generates latent antibody trajectories using the same within-host antibody model used in the JAGS model

3. Applies measurement noise to simulate observed antibody values

4. Preserves the original dataset structure (IDs, biomarkers, timepoints)

The simulation follows the hierarchical model structure:

• Population-level parameters are drawn from hyperpriors

• Individual-level parameters are drawn from population distributions

• Observations are generated with log-normal measurement error

Examples

# Prepare data and priors
set.seed(1)
raw_data <- serocalculator::typhoid_curves_nostrat_100 |>
  sim_case_data(n = 5)
prepped_data <- prep_data(raw_data)
prepped_priors <- prep_priors(max_antigens = prepped_data$n_antigen_isos)

# Simulate from priors
sim_data <- simulate_prior_predictive(prepped_data, prepped_priors)

# Generate multiple simulations
sim_list <- simulate_prior_predictive(
  prepped_data, prepped_priors, n_sims = 10
)