coala (version 0.7.2)

feat_pop_merge: Feature: Population Merge

Description

Backwards in time, this feature merges one population into another. Forwards in time, this corresponds to a speciation event.

Usage

feat_pop_merge(time, pop_source, pop_target, locus_group = "all")

Value

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

Arguments

time

The time at which the merge occurs.

pop_source

The population from which all lines are moved. This is the newly created population in the speciation event.

pop_target

The population to which the lines are moved. This is the population in which the speciation event occurs.

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

In addition to the merge, the growth rate of and all migration rates from the source population will be set to 0 at the time of the merge to mimic a speciation event forwards in time. Technically, pop_source is still present in the model, but not used unless migration to the population is manually enabled.

See Also

For creating a model: coal_model

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

Examples

# Two population which merge after 0.5 time units:
model <- coal_model(c(25,25), 1) +
  feat_pop_merge(0.5, 2, 1) +
  feat_mutation(5) +
  sumstat_tajimas_d()
simulate(model)

# An standard isolation-with-migration model:
model_iwm <- model +
  feat_migration(.75, symmetric = TRUE)
simulate(model_iwm)