Power analysis for per-peptide mode using fitted distributions

Usage

# S3 method for class 'peppwr_fits'
power_analysis(
  distribution,
  effect_size = NULL,
  n_per_group = NULL,
  target_power = NULL,
  alpha = 0.05,
  test = "wilcoxon",
  find = "power",
  n_sim = 1000,
  on_fit_failure = "exclude",
  proportion_threshold = 0.5,
  include_missingness = FALSE,
  apply_fdr = FALSE,
  prop_null = 0.9,
  fdr_threshold = 0.05,
  ...
)

Arguments

distribution: A peppwr_fits object from fit_distributions()
effect_size: Fold change to detect
n_per_group: Sample size per group (required for find="power")
target_power: Target power (required for find="sample_size")
alpha: Significance level (default 0.05)
test: Statistical test to use (default "wilcoxon")
find: What to solve for: "power" or "sample_size"
n_sim: Number of simulations per peptide (default 1000)
on_fit_failure: How to handle failed fits: "exclude", "empirical", or "lognormal"
proportion_threshold: Proportion of peptides that must reach target_power (default 0.5)
include_missingness: If TRUE, incorporate peptide-specific NA rates into simulations
apply_fdr: If TRUE, use FDR-aware simulation with Benjamini-Hochberg correction. Note: not compatible with test = "bayes_t" (Bayes factors cannot be converted to p-values)
prop_null: Proportion of true null peptides (default 0.9 = 90% unchanged)
fdr_threshold: FDR threshold for calling discoveries (default 0.05)
...: Additional arguments (ignored)

Value

A peppwr_power object