coala (version 0.7.2)

sumstat_mcmf: Summary Statistic: Most Common Mutation's Frequency

Description

This summary statistic calculates the observed frequency of the mutational pattern that is observed most often in the data.

Usage

sumstat_mcmf(
  name = "mcmf",
  population = 1,
  transformation = identity,
  expand_mcmf = FALSE,
  type_expand = 1
)

Value

A numeric vector or matrix containing MCMF for each locus.

mcmf

The observed frequency of the mutational pattern that is observed most often in the data.

bal

The frequency of derived alleles in the most frequently observed mutational pattern.

perc_poly

The percentage of positions that are polymorpic.

Arguments

name

The name of the summary statistic. When simulating a model, the value of the statistics are written to an entry of the returned list with this name. Summary statistic names must be unique in a model.

population

The population for which the statistic is calculated. Can also be "all" to calculate it from all populations.

transformation

An optional function for transforming the results of the statistic. If specified, the results of the transformation are returned instead of the original values.

expand_mcmf

Whether to use or not the expanded MCMF. See Details

type_expand

Specifies the type of expanded MCMF to be used. See Details

Details

The expand_mcmf = FALSE calculates the mcmf per locus and returns a vector. The expand_mcmf = TRUE and type_expand = 1 returns the same results as the first column of a Matrix. The expand_mcmf = TRUE and type_expand = 2 adds the frequency of derived alleles in the most frequently observed mutational pattern as a second column. The expand_mcmf = TRUE and type_expand = 3 adds the percentage of positions that are polymorpic. When expanded_mcmf = TRUE results are returned as a matrix.

See Also

To create a demographic model: coal_model

To calculate this statistic from data: calc_sumstats_from_data

Other summary statistics: sumstat_dna(), sumstat_file(), sumstat_four_gamete(), sumstat_ihh(), sumstat_jsfs(), sumstat_nucleotide_div(), sumstat_omega(), sumstat_seg_sites(), sumstat_sfs(), sumstat_tajimas_d(), sumstat_trees()

Examples

# Calculate MCMF for a panmictic population
model <- coal_model(10, 2) +
  feat_mutation(50) +
  sumstat_mcmf()
simulate(model)