lfq_power_t_test_quantiles_V2: estimate sample sizes

In wolski/prolfqua: Proteomics Label Free Quantification

estimate sample sizes

Description

estimate sample sizes

Usage

lfq_power_t_test_quantiles_V2(
  quantile_sd,
  delta = c(0.59, 1, 2),
  power = 0.8,
  sig.level = 0.05,
  min.n = 1.5
)

Arguments

quantile_sd	output of 'summarize_stats_quantiles'
delta	effect size you are interested in
power	of test
min.n	smallest n to determine
sigma.level	P-Value

See Also

Other stats: INTERNAL_FUNCTIONS_BY_FAMILY, lfq_power_t_test_proteins(), lfq_power_t_test_quantiles(), plot_stat_density(), plot_stat_density_median(), plot_stat_violin(), plot_stat_violin_median(), plot_stdv_vs_mean(), pooled_V2(), summarize_stats()

Examples

bb1 <- prolfqua::sim_lfq_data_peptide_config()
config <- bb1$config
data2 <- bb1$data
stats_res <- summarize_stats(data2, config)
xx <- summarize_stats_quantiles(stats_res, config, probs = c(0.5,0.8))
bbb <- lfq_power_t_test_quantiles_V2(xx$long)
bbb <- dplyr::bind_rows(bbb)
summary <- bbb |>
 dplyr::select( -N_exact, -quantiles, -sdtrimmed ) |>
 tidyr::spread(delta, N, sep = "=")

wolski/prolfqua documentation built on Dec. 4, 2024, 11:18 p.m.