Predict Grid Response from Posterior Draws (Bayesian) — predict_grid

For each grid point, evaluates the forward model at every posterior draw and back-calculates concentration to produce a precision profile. Whether a fresh observation-noise draw is injected before back-calculation is governed by include_measurement_error (see file header).

Usage

predict_grid_bayes(
  grid,
  bayes_fit,
  curve_idx = 1L,
  n_draws = NULL,
  cv_x_max = 150,
  pcov_threshold = 20,
  is_log_x = TRUE,
  is_log_response = TRUE,
  include_measurement_error = TRUE
)

Arguments

grid: Data frame from curveRcore::generate_prediction_grid().
bayes_fit: Output of fit_bayes_single().
curve_idx: Integer. Which curve (1-based Stan index).
n_draws: Integer or NULL. Subsample this many draws.
cv_x_max: Numeric. Cap for pcov/pcov_rmse. Default 150.
pcov_threshold: Numeric. Percent CV threshold for pcov_pass. Default 20.
is_log_x: Logical. Default TRUE.
is_log_response: Logical. Whether the response is log10-transformed. Passed to curveRcore::enrich_grid_with_d2y(). Default TRUE.
include_measurement_error: Logical. If TRUE (default) inject observation noise before back-calculation (measurement/CDAN precision). If FALSE, invert the fixed posterior-mean reference response across draws (curve/parameter precision only). See file header.

Value

grid with added columns: predicted_response, ci_lower, ci_upper, predicted_concentration, se_concentration, pcov, pcov_rmse, pcov_pass, noise_mode.