Package 'Ease'

Description

Generates a matrix that allows to go from genotypic frequencies to allelic frequencies.

Usage

alleleFreqMatGeneration(genomeObj)

Arguments

Details

An allele frequency matrix is a matrix with rows equal to the number of genotypes and columns equal to the number of alleles. By multiplying a row matrix of genotype frequencies we obtain a row matrix of associated allele frequencies.

Value

A matrix for calculating allelic frequencies from genotypes frequencies.

Author(s)

Ehouarn Le Faou

Description

Test if there are homozygotes in the specified allelic combinations of a selection formula

Usage

areThereHomoz(formula)

Arguments

Value

logical indicating if there are homozygotes

Author(s)

Ehouarn Le Faou

Description

Object output in the same way as the function cat but adding a line break at the end.

Usage

catn(..., file = "", sep = " ", fill = FALSE, labels = NULL, append = FALSE)

Arguments

Details

See cat.

Value

None (invisible NULL).

Author(s)

Ehouarn Le Faou

Description

The validity check for the Genome class

Usage

check.genome(object)

Arguments

Value

A logical corresponding to whether the object is a correct Genome object.

Author(s)

Ehouarn Le Faou

Description

The validity check for the Metapopulation class

Usage

check.metapopulation(object)

Arguments

Value

a boolean corresponding to whether the object is a correct Metapopulation object.

Author(s)

Ehouarn Le Faou

Description

The validity check associated with the MutationMatrix class

Usage

check.mutationMatrix(object)

Arguments

Value

A logical corresponding to whether x is a correct MutationMatrix object.

Author(s)

Ehouarn Le Faou

Description

The validity check for the Population class

Usage

check.population(object)

Arguments

Value

a boolean corresponding to whether the object is a correct Population object.

Author(s)

Ehouarn Le Faou

Description

The validity check for the Selection class

Usage

check.selection(object)

Arguments

Value

A logical corresponding to whether the object is a correct Selection object.

Author(s)

Ehouarn Le Faou

Description

Conversion of an allelic combination defined in a selection formula into the vector listing the alleles present (alleles that must be in the homozygous state appear 2 times, 1 time for heterozygous).

Usage

extractAlleleComb(xVect)

Arguments

Value

the list of alleles that must be present in the genotype to match the input allelic combination

Author(s)

Ehouarn Le Faou

Description

The Genome class allows to define all the characteristics of the genome which will be used as a basis for the construction of transition matrices from one generation to another in simulations of the model.

Details

A genome includes the list of all possible haplotypes and genotypes resulting from the combination of the alleles defined in input. As the Ease package was originally built for population genetics simulations including both diploid and haploid loci, it is necessary that both types of loci are defined. Despite this, the user can define only diploid or only haploid loci if they wish. If no diploid locus is defined, one is automatically generated with only one allele, thus not influencing the simulation. The same applies if no haploid locus is defined.

Each locus is described by a vector of factors which are the names of the possible alleles at that locus. All diploid (resp. haploid) loci thus defined are grouped in a list, called listDipLoci (resp. listHapLoci). Therefore, a Genome class object has two lists of loci defined in this way, one for diploid loci, one for haploid loci. The alleles and loci (diploid and haploid) must all have different names so that no ambiguity can persist.

If several are defined, the order of the diploid loci in the list is not trivial. The rates of two-to-one combinations between them must indeed be defined by the vector recRate. For example, if three diploid loci are defined, recRate must be of length 2, the first of its values defining the recombination rate between the first and second loci, the second of its values the recombination rate between the second and third loci. For example, if we want to define two groups of two loci that are linked to each other but are on two different chromosomes, we can define a recRate = c(0.1, 0.5, 0.1). The first two loci are thus relatively linked (recombination rate of 0.1), as are the last two loci. On the other hand, the recombination rate of 0.5 between the second and third loci ensures that the two groups are independent.

Slots

listHapLoci

a list of haploid loci

listDipLoci

a list of diploid loci

recRate

a two-by-two recombination rate vector

nbHL

the number of haploid loci

nbDL

the number of diploid loci

listLoci

the list of all loci

haplotypesHL

haplotypes of haploid loci only

haplotypesDL

haplotypes of diploid loci only

haplotypes

haplotypes of all loci

alleles

the vector of all the alleles

nbAlleles

the number of alleles

nbHaplo

the number of haplotypes

IDhaplotypes

IDs of haplotypes

genotypes

the list of genotypes

nbGeno

the number of genotypes

IDgenotypes

IDs of genotypes

IDgenome

ID of the genome

Author(s)

Ehouarn Le Faou

Description

Generation of genotypes associated with a Genome object.

Usage

genotyping(genomeObj)

Arguments

Details

The output genotypes are described as a list of three matrices. A genotype consists of two diploid haplotypes (first two matrices) and one haploid haplotype (third matrix), which are read at the same row number on all three matrices.

Value

A list of matrices describing genotypes in rows.

Author(s)

Ehouarn Le Faou

Description

Getting the custom output

Usage

getCustomOutput(metapop)

Arguments

Value

The list generated through the custom result function, if at least it was specified during the simulation of the Metapopulation.

Author(s)

Ehouarn Le Faou

Description

Getting the simulation results

Usage

getRecords(metapop)

Arguments

Value

A list where each item is associated with a simulation. Each of these elements consists of a list of data.frames, one per population. These data.frames consist of the same columns as the results (see getResults documentation), except that they do not include the stop conditions.

Author(s)

Ehouarn Le Faou

Description

Getting the simulation results

Usage

getResults(metapop)

Arguments

Value

A data.frame where each line corresponds to a simulation. The results include : - the last generation reached (the threshold or the generation that first verified at least one of the stopping conditions) - the final population size - the genotype frequencies - allelic frequencies - the reason(s) for the stop (either the threshold was reached, i.e. unstopped or the stop condition(s) that was (were) reached, in the form of boolean values - Average fitness (individual, gamete production and gametic)

Author(s)

Ehouarn Le Faou

Description

Generation of the haplotype crossing matrix associated to a Genome object.

Usage

haploCrossMatrix(genomeObj)

Arguments

Details

A crossover matrix is a square matrix of size equal to the number of haplotypes. It describes for each combination of two gametic haplotypes the genotype index resulting from their syngamy. In the general case it is not a symmetrical matrix (it is if a single haploid locus with a single allele is defined), because the transmission of haploid loci is only maternal, therefore non-symmetrical as is the transmission of diploid loci. It is therefore necessary to enter the haplotype frequencies of male gametes in the columns and the haplotype frequencies of female gametes in the rows during the calculations (this is done in the simulations).

Value

An haplotype crossing matrix.

Author(s)

Ehouarn Le Faou

Description

Generation of haplotypes associated with a Genome object.

Usage

haplotyping(genomeObj)

Arguments

Details

The generated haplotypes are output as a list of three enumeration in the form of matrices of alleles (each row corresponding to an haplotype, each column to a locus). The first enumeration corresponds to haplotypes considering only haploid loci, the second only diploid loci and the third all loci (with two matrices, 1 for haploid loci, 1 for diploid loci).

Value

A list of matrices describing haplotypes in rows.

Author(s)

Ehouarn Le Faou

Description

Generation of the input genome ID, i.e. the concatenation in string form of the names of the loci and alleles constituting this genome.

Usage

IDgenomeGeneration(listLoci, alleles)

Arguments

Value

The genome ID as a character string.

Author(s)

Ehouarn Le Faou

Description

Generation of the input genotype ID, i.e. the concatenation in string form of the names of the alleles constituting these haplotypes (the two from the diploid genome and the one from the haploid genome).

Usage

IDgenotypeGeneration(dl1, dl2, hl = NULL)

Arguments

Value

The genotype ID as a character string.

Author(s)

Ehouarn Le Faou

Description

Generation of the input haplotype ID, i.e. the concatenation in string form of the names of the alleles constituting this haplotype.

Usage

IDhaplotypeGeneration(dl, hl)

Arguments

Value

The haplotype ID as a character string.

Author(s)

Ehouarn Le Faou

Description

Initialize method for the Genome class

Usage

## S4 method for signature 'Genome'
initialize(.Object, listHapLoci, listDipLoci, recRate)

Arguments

Value

A Genome object

Author(s)

Ehouarn Le Faou

Description

Initialize method for the Metapopulation class

Usage

## S4 method for signature 'Metapopulation'
initialize(.Object, populations, migMat)

Arguments

Value

a Metapopulation object

Author(s)

Ehouarn Le Faou

Description

Initialize method for the MutationMatrix class

Usage

## S4 method for signature 'MutationMatrix'
initialize(.Object, genomeObj, mutHapLoci, mutDipLoci)

Arguments

Value

A MutationMatrix object

Author(s)

Ehouarn Le Faou

Description

Initialize method for the Population class

Usage

## S4 method for signature 'Population'
initialize(
  .Object,
  name,
  size,
  dioecy,
  selfRate,
  demography,
  growthRate,
  initGenoFreq,
  genomeObj,
  initPopSize,
  selectionObj,
  mutMatrixObj
)

Arguments

Value

a Population object

Author(s)

Ehouarn Le Faou

Description

Initialize method for the Selection class

Usage

## S4 method for signature 'Selection'
initialize(.Object, genomeObj)

Arguments

Value

A Selection object

Author(s)

Ehouarn Le Faou

Description

Test if a matrix is a correct transition matrix

Usage

is.correct.transition.matrix(x, type, name)

Arguments

Value

A logical corresponding to whether x is a correct transition matrix, i.e. a square matrix with dimensions greater than 0 and whose rows sum to 1.

Author(s)

Ehouarn Le Faou

Description

Test if a matrix is a default matrix

Usage

is.default.matrix(x)

Arguments

Value

A logical corresponding to whether x is a default matrix (matrix of dimension 0x0).

Author(s)

Ehouarn Le Faou

Description

Test if a matrix is of probability

Usage

is.probability.matrix(x)

Arguments

Value

A logical corresponding to whether x is a probability matrix (sum of rows equal to 1).

Author(s)

Ehouarn Le Faou

Description

Determination for a given genotype or haplotype whether it contains the allelic combination under selection

Usage

isAffected(refDNAtype, selDNAtype)

Arguments

Value

a logic indicating whether the reference genotype or haplotype is affected by the allelic combination under selection

Author(s)

Ehouarn Le Faou

Description

Test if there are homozygotes in the specified allelic combinations of a list of selection formulas

Usage

isHaploSelectFormula(selectFormula)

Arguments

Value

logical indicating if there are homozygotes

Description

Listing from the elements of a vector by producing a string, with comma separation between each element and the word "and" between the last two elements.

Usage

listing(vect)

Arguments

Value

A listing of the elements of the input vector as a string.

Author(s)

Ehouarn Le Faou

Description

Generation of the meiosis matrix associated to a Genome object.

Usage

meiosisMatrix(genomeObj)

Arguments

Details

A meiosis matrix is a matrix where the number of rows is equal to the number of genotypes and the number of columns to the number of haplotypes. It is a matrix that allows to pass from parental genotypes to gametic haplotypes by meiosis. It is a probability matrix in that the sum of the values in each row is equal to 1. For a given genotype, the row associated with it describes the probabilistic proportions that lead by meiosis to the production of the other genotypes (and of itself if there are no mutations).

Value

A meiosis matrix (probability matrix that associates to each genotype in a row the probability of producing each of the possible haplotypes).

Author(s)

Ehouarn Le Faou

Description

Simulation of a metapopulation

Usage

METAPOP_SIMULATION(
  nbPop,
  ids,
  migMat,
  nsim,
  verbose,
  recording,
  recordGenGap,
  drift,
  nbHaplo,
  nbGeno,
  idGeno,
  nbAlleles,
  idAlleles,
  nbLoci,
  initGenoFreq,
  meiosisMat,
  gametogenesisMat,
  popSize,
  threshold,
  dioecy,
  selfRate,
  stopCondition,
  IDstopCondition,
  haploCrossMat,
  alleleFreqMat,
  gamFit,
  indFit,
  gamProdFit,
  demography,
  growthRate,
  initPopSize,
  nameOutFunct
)

Arguments

Author(s)

Ehouarn Le Faou

Description

The class Metapopulation is used to centralise the information relating to the populations that we want to simulate, as well as to define the migration conditions between them if there are several. This class is thus defined by a list of objects Population and a migration matrix.

Slots

populations

list of objects Population

nbPop

number of populations

names

names of the populations

migMat

migration matrix between population (if there is more than one)

sizes

sizes of the populations

dioecies

sexual systems of the populations (they must all be the same)

selfRates

selfing rates of the populations

demographies

demography parameter of the populations

growthRates

growth rates of the populations

initPopSizes

initial population sizes of the populations

initGenoFreqs

initial genotypic frequencies of the populations

genome

a Genome object

genomeIDs

ID of the Genome object

mutMat

a MutationMatrix object

selection

a Selection object

recMat

recombination matrix

meiosisMat

a meiosis matrix

haploCrossMat

an haplotype crossing matrix

haploCrossMatNamed

an haplotype crossing matrix with names of genotypes instead of their indices

gametogenesisMat

a gametogenesis matrix

alleleFreqMat

a matrix for calculating allelic frequencies

rawOutputSimul

raw output of the simulation function, its refinement is done directly afterwards in the simulate method

stopCondition

list of stop conditions for the simulation (if required)

IDstopCondition

names of stop conditions. They are given an arbitrary name if none is given by the user.

results

data.frame.

records

list.

customOutput

list.

Author(s)

Ehouarn Le Faou

Description

Utility function to easily generate a mutation matrix (see setMutationMatrix).

Usage

mutation(from, to, rate)

Arguments

Details

Mutation occurs from one allele to another at a specific rate. Please take care to define alleles as traits, that these alleles are present in the genome you are using and that the alleles are associated with the same locus.

Value

A standardised list of input parameters that will be used by the function setMutationMatrix to generate the mutation matrix.

Examples

### Example with two loci, each with two alleles ###

# Definition of the genome
DL <- list(dl = c("A", "a"))
HL <- list(hl = c("B", "b"))
genomeObj <- setGenome(listHapLoci = HL, listDipLoci = DL)

# The mutation function allows each transition from one allele to
# another to be defined individually, to produce the mutation matrix
# as follows:
mutMatrixObj <- setMutationMatrix(genomeObj,
  mutations = list(
    mutation(from = "A", to = "a", rate = 0.1),
    mutation(from = "B", to = "b", rate = 0.1)
  )
)

Description

A mutation matrix is used to simulate mutations that affect loci. An object of the class MutationMatrix does not only contain a (haplotypic) mutation matrix. It also contains the attributes necessary for the construction and easy-to-read display of this matrix.

Details

The mutation matrix itself is a square matrix of size equal to the number of haplotypes. It is a probability matrix in that the sum of the values in each row is equal to 1. For a given haplotype, the row associated with it describes the probabilistic proportions that lead by mutation of this haplotype to the production of the other haplotypes (and of itself if there are no mutations).

Slots

mutHapLoci

a list of haploid locus by locus allelic mulation matrices.

mutDipLoci

a list of diploid locus by locus allelic mulation matrices.

mutLoci

a list concatenating mutHapLoci and mutDipLoci

nbAlDL

a vector of the number(s) of alleles at each haploid locus

nbAlHL

a vector of the number(s) of alleles at each diploid locus

mutationMatrix

the haplotypic mutation matrix

nbHaplo

the number of haplotypes

nbDL

the number of diploid loci

nbHL

the number of haploid loci

haplotypes

the enumeration of haplotypes

IDgenome

ID of the associated genome

Author(s)

Ehouarn Le Faou

Description

Translation of the list of individually defined mutations into allelic mutation matrices which are then used to generate the genotypic mutation matrix.

Usage

mutMatFriendly(genomeObj, mutations)

Arguments

Value

A list of the two list of allelic mutation matrices, for haploid and diploid loci respectively.

Author(s)

Ehouarn Le Faou

Description

Generation of a mutation matrix from the allele enumeration vector of a loci and the forward and backward mutation rates.

Usage

mutMatRates(alleles, forwardMut, backwardMut)

Arguments

Details

See MutationMatrix for more details on mutation matrices.

Value

An allelic mutation matrix (probability matrix which associates to each allele in a row the probability of mutating or not to the other alleles of the locus in question).

Author(s)

Ehouarn Le Faou

Description

Allow to produce a custom output for a simulation.

Usage

outFunct(pop)

Arguments

Details

This function is called each generation in each population of a simulation and systematically returns a list with the first element being a logic that indicates whether something should be saved. If so, the second element of this list will be saved.

By default the save nothing function, but it can be changed by the user as an argument in the simulate method of the Metapopulation class.

Author(s)

Ehouarn Le Faou

Description

The Population class allows for the collection of the parameters necessary to characterise a biological population. It is an essentially useful class in that no method associated with the population class can simulate its dynamics. To do this, it is necessary to use the Metapopulation class, which takes as input a list of populations (from one). The Population class is also used to check that each of these parameters is compatible with each other.

Details

Thus to build an object of class Ease, it is necessary to have defined an object Genome, as well as an object MutationMatrix and an object Selection (even if it is neutral, see setSelectNeutral).

Slots

name

the name of the population.

size

the size of the population.

dioecy

logical indicating whether the population is dioecious or not (hermaphrodite).

selfRate

the selfing rate of the population

demography

logical indicating whether the population has stochastic demography (this does not include migration), i.e. non-constant size and potentially population growth or decay, depending on the situation it is in.

growthRate

growth rate of the population.

initGenoFreq

A row matrix of the size of the genotype number describing the initial allele frequencies common to all simulations

genome

a Genome object

initPopSize

initial population size, knowing that if the demography is extinct, the initial population size will automatically be set equal to the population size.

selection

a Selection object

mutMat

a MutationMatrix object

Author(s)

Ehouarn Le Faou

Description

Print method for the Genome class

Usage

## S4 method for signature 'Genome'
print(x, ...)

Arguments

Value

No return value, only a display.

Author(s)

Ehouarn Le Faou

Description

Print method for the Metapopulation class

Usage

## S4 method for signature 'Metapopulation'
print(x, ...)

Arguments

Author(s)

Ehouarn Le Faou

Description

Print method for the MutationMatrix class

Usage

## S4 method for signature 'MutationMatrix'
print(x, ...)

Arguments

Value

No return value, only a display.

Author(s)

Ehouarn Le Faou

Description

Print method for the Population class

Usage

## S4 method for signature 'Population'
print(x, ...)

Arguments

Author(s)

Ehouarn Le Faou

Description

Print method for the Selection class

Usage

## S4 method for signature 'Selection'
print(x, ...)

Arguments

Value

No return value, only a display.

Author(s)

Ehouarn Le Faou

Description

Generation of the recombination matrix associated to a Genome object.

Usage

recombinationMatrix(genomeObj)

Arguments

Details

A recombination matrix is a square matrix of size equal to the number of genotypes. It is a probability matrix in that the sum of the values in each row is equal to 1. For a given genotype, the row associated with it describes the probabilistic proportions that lead by recombination between diploid loci to the production of the other genotypes (and of itself if there are no mutations).

Value

A recombination matrix (probability matrix which associates to each genotype in a row the probability of recombining or not and of becoming another genotype or remaining the same).

Author(s)

Ehouarn Le Faou

Description

Processing a result (or record) list

Usage

rowResultGen(x)

Arguments

Value

Merges the column names of the matrices making up the list with the names of the matrices, then merges the matrices together.

Author(s)

Ehouarn Le Faou

Description

Conversion of a list of selection formulas into a genotypic (or haplotypic) fitness vector associated with a Genome object.

Usage

selectFormIntoVect(selectFormula, genomeObj, haplo = FALSE)

Arguments

Value

a vector of fitness values

Author(s)

Ehouarn Le Faou

Description

Determines whether an entry for the selection is a list of selection formulas or just a vector. If it is a list of formulas, turns them into a vector. If it is a vector, does nothing.

Usage

selectInputTreatment(selectInput, genomeObj, haplo = FALSE)

Arguments

Value

a vector of fitness values

Author(s)

Ehouarn Le Faou

Description

Class used to generate objects that manage the selection in the simulations.

Details

An object of type Selection is an object which describes the set of fitnesses which will be taken into account in the simulations. The selection according to these fitnesses can be applied at three levels: at the level of the individual, at the level of the production of gametes and at the level of the gametes themselves. Selection is therefore genotypic in the first two cases (each genotype is associated with a fitness value) and haplotypic in the third (each haplotype is associated with a fitness value).

Slots

genome

a Genome object

IDhaplotypes

IDs of haplotypes

IDgenotypes

IDs of genotypes

IDgenome

ID of the associated genome

nbHaplo

the number of haplotypes

nbGeno

the number of genotypes

gamFit

the list of gametes' fitness

indFit

the list of individuals' fitness

gamProdFit

the list of gamete production fitness

sOnInds

a logical indicating whether a selection on individuals has been configured by the user

sOnGams

a logical indicating whether a selection on gametes has been configured by the user

sOnGamsProd

a logical indicating whether a selection on gamete production has been configured by the user

Author(s)

Ehouarn Le Faou

Description

Conversion of the factors of a selection formula into the list of corresponding allelic combinations

Usage

selection.form.treatment(factors, genomeObj = NULL, checking = FALSE)

Arguments

Value

A list of vectors enumerating the allelic combinations that correspond to the factors

Author(s)

Ehouarn Le Faou

Description

Generation of a genome class object from the list of haploid loci and diploid loci. Each loci is defined by a factor vector that enumerates its alleles.

Usage

setGenome(listHapLoci = list(), listDipLoci = list(), recRate = numeric())

Arguments

Details

A genome includes the list of all possible haplotypes and genotypes resulting from the combination of the alleles defined in input. As the Ease package was originally built for population genetics simulations including both diploid and haploid loci, it is necessary that both types of loci are defined. Despite this, the user can define only diploid or only haploid loci if they wish. If no diploid locus is defined, one is automatically generated with only one allele, thus not influencing the simulation. The same applies if no haploid locus is defined.

Each locus is described by a vector of factors which are the names of the possible alleles at that locus. All diploid (resp. haploid) loci thus defined are grouped in a list, called listDipLoci (resp. listHapLoci). Therefore, a Genome class object has two lists of loci defined in this way, one for diploid loci, one for haploid loci. The alleles and loci (diploid and haploid) must all have different names so that no ambiguity can persist.

If several are defined, the order of the diploid loci in the list is not trivial. The rates of two-to-one combinations between them must indeed be defined by the vector recRate. For example, if three diploid loci are defined, recRate must be of length 2, the first of its values defining the recombination rate between the first and second loci, the second of its values the recombination rate between the second and third loci. For example, if we want to define two groups of two loci that are linked to each other but are on two different chromosomes, we can define a recRate = c(0.1, 0.5, 0.1). The first two loci are thus relatively linked (recombination rate of 0.1), as are the last two loci. On the other hand, the recombination rate of 0.5 between the second and third loci ensures that the two groups are independent.

Value

a Genome object

Author(s)

Ehouarn Le Faou

Examples

DL <- list(dl = c("A", "a"))
HL <- list(hl = c("B", "b"))
genomeObj <- setGenome(listHapLoci = HL, listDipLoci = DL)

Description

A metapopulation is a set of population(s) (from 1) that are simulated with potential migration between them. Only genotypes can migrate, i.e. adult individuals.

Usage

setMetapopulation(populations, migMat = matrix(1))

Arguments

Details

The construction of a Metapopulation object requires only two arguments (one optional). The first is a population(s) list, defined from the population class. The second is a migration matrix, which connects the populations together. This matrix is a probability matrix (square with the sum of the rows equal to 1, whose size is equal to the number of populations) where each value corresponds to the proportion of individuals (genotypes) that disperse from their source population (row) to their target population (column).

Value

a Metapopulation object

Author(s)

Ehouarn Le Faou

Examples

# Definition of a population in its simplest form:
DL <- list(dl = c("A", "a"))
HL <- list(hl = c("B", "b"))
mutations <- list(
  mutation(from = "A", to = "a", rate = 1e-3),
  mutation(from = "B", to = "b", rate = 1e-3)
)
genomeObj <- setGenome(listHapLoci = HL, listDipLoci = DL)
pop <- setPopulation(
  name = "A",
  size = 1000,
  dioecy = TRUE,
  genomeObj = genomeObj,
  selectionObj = setSelectNeutral(genomeObj),
  mutMatrixObj = setMutationMatrix(genomeObj, mutations = mutations)
)
metapop <- setMetapopulation(populations = list(pop))
metapop <- simulate(metapop, nsim = 10, seed = 123)
# Other examples available in the documentation of the package

Description

Generation of the mutation matrix associated with the genome given as input. A mutation matrix is used to simulate mutations that affect loci. An object of the class MutationMatrix does not only contain a (genotypic) mutation matrix. It also contains the attributes necessary for the construction and easy-to-read display of this matrix. The mutation matrix itself is a square matrix of size equal to the number of genotypes. It is a probability matrix in that the sum of the values in each row is equal to 1. For a given genotype, the row associated with it describes the probabilistic proportions that lead by mutation of this genotype to the production of the other genotypes (and of itself if there are no mutations).

Usage

setMutationMatrix(genomeObj, ...)

Arguments

Details

There are three ways to define the mutation matrix associated with a Genome class object.

1) By giving two lists of allelic mutation matrices mutHapLoci and mutDipLoci, for haploid and diploid loci respectively. Each of these lists contains as many matrices as there are loci. These matrices are transition matrices (squares, with the sum of the rows equal to 1) of size equal to the number of alleles at the locus concerned.

2) By giving a forward and a backward allelic mutation rate (forwardMut and backwardMut respectively). The generated mutation matrices will thus be defined with the same rates for all loci. A forward mutation rate means that the transition from one allele to another is done in the order in which they were defined when the Genome class object was created, and in the other direction for the backward rate.

3) By giving a list of mutations generated through the mutation function.

Value

a MutationMatrix object

Author(s)

Ehouarn Le Faou

Examples

### Example with two loci, each with two alleles ###

# Definition of the genome
DL <- list(dl = c("A", "a"))
HL <- list(hl = c("B", "b"))
genomeObj <- setGenome(listHapLoci = HL, listDipLoci = DL)

# Three ways to define the same mutation matrix associated with the
# genome defined above:

# 1) Mutation matrix from matrices
mutHapLoci <- list(matrix(c(0.99, 0.01, 0.01, 0.99), 2))
mutDipLoci <- list(matrix(c(0.99, 0.01, 0.01, 0.99), 2))
# One can then define the MutationMatrix class object:
setMutationMatrix(genomeObj,
  mutHapLoci = mutHapLoci,
  mutDipLoci = mutDipLoci
)

# 2) Mutation matrix from mutation rates
mutMatrixObj <- setMutationMatrix(genomeObj, forwardMut = 0.1)
# or by adding a backward mutation rate:
mutMatrixObj <- setMutationMatrix(genomeObj,
  forwardMut = 1e-3,
  backwardMut = 1e-4
)

# 3) Mutation matrix from single mutation definition
mutMatrixObj <- setMutationMatrix(genomeObj,
  mutations = list(
    mutation(from = "A", to = "a", rate = 0.1),
    mutation(from = "B", to = "b", rate = 0.1)
  )
)

Description

Generation of a population by providing all the necessary ingredients for its definition, including a genome, a mutation matrix and a selection regime.

Usage

setPopulation(
  name,
  size,
  dioecy,
  genomeObj,
  mutMatrixObj,
  selectionObj,
  selfRate = 0,
  demography = F,
  growthRate = 0,
  initPopSize = NULL,
  initGenoFreq = NULL
)

Arguments

Details

A population is defined strictly by a name, a size, a sexual system (dioecy or hermaphodite), and the three objects defined previously: genome, mutation matrix and selection. In addition to that, it is possible to define - a selfing rate (by default equal to 0) - a vector of initial genotypic frequencies - a demography

Two demographic regimes are possible: no demography, i.e. a fixed population size, or demography, i.e. a population where the size fluctuates stochastically. The boolean argument 'demography' is used to define whether there should be stochasticity. For a fixed population size, it is therefore sufficient to define that 'demography = FALSE' (default) and to set the desired population size with the 'popSize' parameter.

For a fluctuating demography, 'demography' must be 'TRUE' and three other parameters are then needed: the initial population size ('initPopSize'), the population growth rate ('growthRate') and the carrying capacity of the population (the population size, 'popSize').

It is also possible to avoid defining a population size altogether, by setting off the genetic drift ('drift' parameter). This will allow the model to be simulated deterministically.

Value

a Population object

Author(s)

Ehouarn Le Faou

Examples

# Definition of a population in its simplest form:
DL <- list(dl = c("A", "a"))
HL <- list(hl = c("B", "b"))
mutations <- list(
  mutation(from = "A", to = "a", rate = 1e-3),
  mutation(from = "B", to = "b", rate = 1e-3)
)
genomeObj <- setGenome(listHapLoci = HL, listDipLoci = DL)
pop <- setPopulation(
  name = "A",
  size = 1000,
  dioecy = TRUE,
  genomeObj = genomeObj,
  selectionObj = setSelectNeutral(genomeObj),
  mutMatrixObj = setMutationMatrix(genomeObj, mutations = mutations)
)

Description

Generation of a neutral class Selection object. It can be used as a basis for adding selection layers with the setSelectOnInds, setSelectOnGametes or setSelectOnGametesProd functions, or if the model is neutral.

Usage

setSelectNeutral(genomeObj)

Arguments

Details

An object of type Selection is an object which describes the set of fitnesses which will be taken into account in the simulations. The selection according to these fitnesses can be applied at three levels: at the level of the individual, at the level of the production of gametes and at the level of the gametes themselves. Selection is therefore genotypic in the first two cases (each genotype is associated with a fitness value) and haplotypic in the third (each haplotype is associated with a fitness value).

Value

a Selection object

Author(s)

Ehouarn Le Faou

Examples

### Example with two loci, each with two alleles ###
# Definition of the diploid locus
DL <- list(dl = c("A", "a"))
# Definition of the haploid locus
HL <- list(hl = c("B", "b"))
# Definition of the object of Genome class
genomeObj <- setGenome(listHapLoci = HL, listDipLoci = DL)
genomeObj

### Exemple with more diploid loci ###
# Definition of the diploid loci
DL <- list(
  dl1 = c("A", "a"),
  dl2 = c("B", "b"),
  dl3 = c("C", "c")
)
# Definition of the haploid locus
HL <- list(hl = c("D", "d"))
# Definition of the object of Genome class, with in addition the necessary
# definition of recombination rates between loci:
genomeObj <- setGenome(
  listHapLoci = HL, listDipLoci = DL,
  recRate = c(0.1, 0.5)
)
# Here we have a 0.1 recombination rate between dl1 and dl2 and a 0.5
# recombination rate between dl2 and dl3. It is as if dl1 and dl2 were linked,
# for example on the same chromosome, and that dl2 (and dl1 by consequence)
# and dl3 were independent, for example on different chromosomes.

genomeObj

Description

Generation of an object of the Selection class which defines a selection among the individuals either by adding this type of selection to an already existing SelectionObj object (parameter selectionObj) or by creating one.

Usage

setSelectOnGametes(
  genomeObj = NULL,
  gamFit = c(),
  femaleFit = c(),
  maleFit = c(),
  selectionObj = NULL
)

Arguments

Value

a Selection object

Author(s)

Ehouarn Le Faou

Examples

DL <- list(dl = c("A", "a"))
HL <- list(hl = c("B", "b"))
genomeObj <- setGenome(listHapLoci = HL, listDipLoci = DL)
selectionObj <- setSelectOnGametes(
  genomeObj = genomeObj,
  gamFit = c(1, 1, 0.5, 0)
)

Description

Generation of an object of the Selection class which defines a selection on the gamete production either by adding this type of selection to an already existing SelectionObj object (parameter selectionObj) or by creating one.

Usage

setSelectOnGametesProd(
  genomeObj = NULL,
  indProdFit = c(),
  femProdFit = c(),
  maleProdFit = c(),
  selectionObj = NULL
)

Arguments

Value

a Selection object

Author(s)

Ehouarn Le Faou

Examples

DL <- list(dl = c("A", "a"))
HL <- list(hl = c("B", "b"))
genomeObj <- setGenome(listHapLoci = HL, listDipLoci = DL)
selectionObj <- setSelectOnGametesProd(
  genomeObj = genomeObj,
  indProdFit = c(1, 1, 1, 1, 0.5, 0)
)

Description

Generation of an object of the Selection class which defines a selection among the individuals either by adding this type of selection to an already existing SelectionObj object (parameter selectionObj) or by creating one.

Usage

setSelectOnInds(
  genomeObj = NULL,
  indFit = c(),
  femaleFit = c(),
  maleFit = c(),
  selectionObj = NULL
)

Arguments

Value

a Selection object

Author(s)

Ehouarn Le Faou

Examples

DL <- list(dl = c("A", "a"))
HL <- list(hl = c("B", "b"))
genomeObj <- setGenome(listHapLoci = HL, listDipLoci = DL)
selectionObj <- setSelectOnInds(
  genomeObj = genomeObj,
  indFit = c(1, 1, 1, 1, 0.5, 0)
)

Description

Show method for the Genome class

Usage

## S4 method for signature 'Genome'
show(object)

Arguments

Value

No return value, only a display.

Author(s)

Ehouarn Le Faou

Description

Show method for the Metapopulation class

Usage

## S4 method for signature 'Metapopulation'
show(object)

Arguments

Author(s)

Ehouarn Le Faou

Description

Show method for the MutationMatrix class

Usage

## S4 method for signature 'MutationMatrix'
show(object)

Arguments

Value

No return value, only a display.

Author(s)

Ehouarn Le Faou

Description

Show method for the Population class

Usage

## S4 method for signature 'Population'
show(object)

Arguments

Author(s)

Ehouarn Le Faou

Description

Show method for the Selection class

Usage

## S4 method for signature 'Selection'
show(object)

Arguments

Value

No return value, only a display.

Author(s)

Ehouarn Le Faou

Description

Performing simulations of an Metapopulation object. The returned object is the same Metapopulation object completed with the results and records if they have been activated.

Usage

## S4 method for signature 'Metapopulation'
simulate(
  object,
  nsim = 1,
  seed = NULL,
  threshold = 500,
  includefreqGeno = TRUE,
  recording = FALSE,
  recordGenGap = 1,
  drift = TRUE,
  includeParams = TRUE,
  includeFitness = TRUE,
  verbose = FALSE,
  stopCondition = list(),
  nameOutFunct = "outFunct"
)

Arguments

Value

An Metapopulation object from which we can now extract the results (or the records if recording = TRUE) with the getResults and getRecords functions.

Author(s)

Ehouarn Le Faou

Description

Determine which alleles are at least once input as homozygous in the formula.

Usage

whichHomoz(formula)

Arguments

Value

the enumeration of alleles that appear at least once homozygous

Author(s)

Ehouarn Le Faou