estimate_significance {transite} | R Documentation

## Permutation Test Based Significance of Observed Mean

### Description

`estimate_significance` returns an estimate of the significance of the observed mean, given a set of random permutations of the data.

### Usage

    estimate_significance(
      actual_mean,
      motif_kmers,
      random_permutations,
      alternative = c("two_sided", "less", "greater"),
      conf_level = 0.95,
      produce_plot = TRUE
    )

### Arguments

`actual_mean` | observed mean
`motif_kmers` | set of k-mers that were used to compute the `actual_mean`
`random_permutations` | a set of random permutations of the original data, used to generate an empirical null distribution.
`alternative` | side of the test, one of the following: `"two_sided"`, `"less"`, `"greater"`
`conf_level` | confidence level for the returned confidence interval
`produce_plot` | if distribution plot should be part of the returned list

### Value

A list with the following components:

`p_value_estimate` | the estimated p-value of the observed mean
`conf_int` | the confidence interval around that estimate
`plot` | plot of the empirical distribution of geometric means of the enrichment values

### See Also

Other k-mer functions: `calculate_kmer_enrichment()`, `check_kmers()`, `compute_kmer_enrichment()`, `count_homopolymer_corrected_kmers()`, `draw_volcano_plot()`, `estimate_significance_core()`, `generate_kmers()`, `generate_permuted_enrichments()`, `run_kmer_spma()`, `run_kmer_tsma()`

[Package transite version 1.10.0 Index]