coala (version 0.7.2)

feat_growth: Feature: Exponential population size growth/decline

Description

This feature changes the growth factor of a population at given point in time. This factor applies to the time interval further into the past from this point.

Usage

feat_growth(rate, population = "all", time = "0", locus_group = "all")

Value

The feature, which can be added to a model created with coal_model using +.

Arguments

rate

The growth rate. Can be a numeric or a parameter. See Details for an explanation how the rate affects the population size.

population

The population which growths/declines. Can be "all" for all populations, or the number of one population.

time

The time at which the growth rate is changed. Can also be a parameter.

locus_group

The loci for which this features is used. Can either be "all" (default), in which case the feature is used for simulating all loci, or a numeric vector. In the latter case, the feature is only used for the loci added in locus_ commands with the corresponding index starting from 1 in order in which the commands where added to the model. For example, if a model has locus_single(10) + locus_averaged(10, 11) + locus_single(12) and this argument is c(2, 3), than the feature is used for all but the first locus (that is locus 2 - 12).

Details

The population size changes by a factor \(exp(-\alpha*t)\), where \(\alpha\) is the growth parameter and \(t\) is the time since the growth has started. For positive alpha, the population will decline backwards in time or grow forwards in time. For a negative value of \(\alpha\) it will decline (forward in time).

See Also

For instantaneous population size changes: feat_size_change

For creating a model: coal_model

Other features: feat_ignore_singletons(), feat_migration(), feat_mutation(), feat_outgroup(), feat_pop_merge(), feat_recombination(), feat_selection(), feat_size_change(), feat_unphased()

Examples

# Simulate a haploid population that has been expanding for
# the last 2*Ne generations
model <- coal_model(10, 1) +
  feat_growth(5, time = 0) +
  feat_growth(0, time = 1) +
  feat_mutation(10) +
  sumstat_sfs()
simulate(model)