plot stddev vs mean to asses stability of variance

plot stddev vs mean to asses stability of variance

Usage

plot_stdv_vs_mean(pdata, factor_keys_depth, size = 2000)

Arguments

pdata

data.frame with statistics

factor_keys_depth

character vector — factor columns for faceting

size

how many points to sample (since scatter plot to slow for all)

See also

Other stats: INTERNAL_FUNCTIONS_BY_FAMILY, lfq_power_t_test_proteins(), lfq_power_t_test_quantiles(), lfq_power_t_test_quantiles_V2(), plot_stat_density(), plot_stat_density_median(), plot_stat_violin(), plot_stat_violin_median(), pooled_V2(), summarize_stats()

Examples

bb1 <- prolfqua::sim_lfq_data_peptide_config()
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done
lfq <- LFQData$new(bb1$data, bb1$config)
res <- lfq$get_Stats()$stats()
plot_stdv_vs_mean(res, lfq$relevant_factor_keys())
#> `geom_smooth()` using formula = 'y ~ x'
#> Warning: Removed 2 rows containing non-finite outside the scale range (`stat_smooth()`).
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_point()`).