Bootstrap method for consumption uncertainty propagation

Propagates p-value uncertainty to consumption predictions using parametric bootstrap. Generates multiple samples from the p-value distribution and runs FB4 simulations for each sample. Provides full uncertainty distribution without linearity assumptions. Supports parallel processing for improved performance.

Usage

predict_consumption_bootstrap(
  p_mean,
  p_sd,
  bio_obj,
  n_sims = 1000,
  first_day = 1,
  last_day = 365,
  parallel = FALSE,
  n_cores = NULL,
  confidence_level = 0.95,
  verbose = FALSE
)

Arguments

p_mean

Mean of p-value distribution

p_sd

Standard deviation of p-value distribution

bio_obj

Bioenergetic object with simulation settings and environmental data

n_sims

Number of bootstrap simulations, default 1000

first_day

First simulation day, default 1

last_day

Last simulation day, default 365

parallel

Use parallel processing, default FALSE

n_cores

Number of cores for parallel processing (NULL = auto-detect), default NULL

confidence_level

Confidence level for intervals, default 0.95

verbose

Show progress messages, default FALSE

Details

The bootstrap method: 1. Samples p-values from Normal(p_mean, p_sd) 2. Constrains samples to valid range [0.01, 5.0] 3. Runs FB4 simulation for each p-value sample 4. Summarizes consumption distribution

Parallel processing can significantly reduce computation time for large n_sims. The method handles simulation failures gracefully and reports success rates.

Examples

if (FALSE) { # \dontrun{
# Bootstrap uncertainty propagation
uncertainty_result <- predict_consumption_bootstrap(
  p_mean = 0.8,
  p_sd = 0.15,
  bio_obj = bio_obj,
  n_sims = 1000,
  parallel = TRUE
)

# Compare with delta method
delta_result <- predict_consumption_delta(0.8, 0.15, bio_obj)
} # }