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Plot prior predictive check trajectories

Source: R/plot_prior_predictive.R
plot_prior_predictive.Rd

Visualizes antibody trajectories simulated from priors to assess whether prior distributions generate realistic curves for the study context.

Usage

plot_prior_predictive(
  sim_data,
  original_data = NULL,
  log_scale = TRUE,
  max_traj = 100,
  show_points = TRUE,
  alpha = 0.3
)

Arguments

sim_data

A simulated prepped_jags_data object from simulate_prior_predictive(), or a list of such objects

original_data

Optional original prepped_jags_data object from prep_data() to overlay observed data

log_scale

logical Whether to plot on log scale (default = TRUE)

max_traj

integer Maximum number of trajectories to plot per subject (default = 100). Useful when sim_data contains many simulations.

show_points

logical Whether to show individual observation points (default = TRUE)

alpha

numeric Transparency for trajectory lines (default = 0.3)

Value

A ggplot2::ggplot() object

Details

Creates plots showing:

  • Simulated antibody trajectories over time

  • Separate panels for each biomarker (faceted)

  • Optional overlay of observed data for comparison

  • Multiple trajectories (if multiple simulations provided)

The plot uses log-scale antibody values by default (matching the model), but can optionally show natural scale.

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 and plot
sim_data <- simulate_prior_predictive(
  prepped_data, prepped_priors, n_sims = 20
)
plot_prior_predictive(sim_data, original_data = prepped_data)