bsInfo: R6 Class Representing a Breeding Scheme
In KosukeHamazaki/myBreedSimulatR: R Breeding Simulator for K.Hamazaki
bsInfo R Documentation
R6 Class Representing a Breeding Scheme
Description
bsInfo object store specific information of one breeding scheme.
Public fields
bsName[character] name of this breeding scheme
simInfo[simInfo class] Simulation information
(see:simInfo)
specie[specie class] Specie of the SNPs
(see:specie)
lociInfo[lociInfo object] information about the individuals haplotypes'
SNPs (see:lociInfo)
traitInfo[traitInfo class] Specific information of traits
(see:traitInfo)
founderIsInitPop[logical] Founder haplotype will be regarded as first population or not.
seedSimHaplo[numeric] Random seed for selecting haplotype from founder haplotype
seedSimRM[numeric] Random seed for mate pairs
seedSimMC[numeric] Random seed for make crosses
popNameBase[character] base of population's name.
populations[list] A list of population objects
crossInfoList[list] A list of crossInfo objects
generation[list] current generation No. in the breeding scheme
herit[numeric] Heritability for each trait (plot-based/line-based)
envSpecificEffects[numeric] Effects specific to each environments / treatments.
If 'multiTraitsAsEnvs = FALSE', envSpecificEffects will be 0 for all traits.
residCor[matrix] Residual correlation between traits
Active bindings
lociEffects[matrix] marker and QTL effects used for crossInfo object
Methods
Public methods
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Method new()
Create a new bsInfo object.
Usage
bsInfo$new(
bsName = "Undefined",
simInfo,
specie,
lociInfo,
traitInfo,
geno = NULL,
haplo = NULL,
founderIsInitPop = TRUE,
seedSimHaplo = NA,
seedSimRM = NA,
seedSimMC = NA,
popNameBase = "Population",
initIndNames = NULL,
herit = NULL,
envSpecificEffects = NULL,
residCor = NULL,
verbose = TRUE
)
Arguments
bsName[character] Name of this breeding scheme
simInfo[simInfo class] Simulation information
(see:simInfo)
specie[specie class] Specie of the SNPs
(see:specie)
lociInfo[lociInfo object] information about the individuals haplotypes'
SNPs (see:lociInfo)
traitInfo[traitInfo class] Specific information of traits
(see:traitInfo)
geno[data.frame / matrix] genotype of the individuals encoded in allele dose.
If you use real data, you must specify 'geno' or 'haplo' argument.
haplo[array] haplotype of the individuals scored with 0 and 1 (3-dimensional array).
founderIsInitPop[logical] Founder haplotype will be regarded as first population or not.
seedSimHaplo[numeric] Random seed for selecting haplotype from founder haplotype
seedSimRM[numeric] Random seed for mate pairs
seedSimMC[numeric] Random seed for make crosses
popNameBase[character] base of population's name.
initIndNames[character] NULL or character string vector specifying the individuals
names for initial population. If NULL, rownames(geno) will be used.
herit[numeric] Heritability for each trait (plot-based/line-based)
envSpecificEffects[numeric] Effects specific to each environments / treatments.
If 'multiTraitsAsEnvs = FALSE', envSpecificEffects will be 0 for all traits.
residCor[matrix] Residual correlation between traits
verbose[logical] Display info (optional)
Returns
A new 'bsInfo' object.
Examples
### create simulation information
mySimInfo <- simInfo$new(simName = "Simulation Example",
simGeno = TRUE,
simPheno = TRUE,
#' nSimGeno = 1,
#' nSimPheno = 3,
#' nCoreMax = 4,
#' nCorePerGeno = 1,
#' nCorePerPheno = 3,
saveDataFileName = NULL)
### create specie information
mySpec <- specie$new(nChr = 3,
lChr = c(100, 150, 200),
specName = "Example 1",
ploidy = 2,
mutRate = 10^-8,
recombRate = 10^-6,
chrNames = c("C1", "C2", "C3"),
nLoci = 100,
recombRateOneVal = FALSE,
effPopSize = 100,
simInfo = mySimInfo,
verbose = TRUE)
### create lociInfo object
myLoci <- lociInfo$new(genoMap = NULL, specie = mySpec)
plot(myLoci, alpha = 0.1)
### create traitInfo object
myTrait <- traitInfo$new(lociInfo = myLoci,
nMarkers = 80,
nTraits = 3,
nQTLs = c(4, 8, 3),
actionTypeEpiSimple = TRUE,
qtlOverlap = TRUE,
nOverlap = c(2, 3, 0),
effCor = 0.1,
propDomi = 0.2,
interactionMean = c(4, 1, 2))
myTrait$plot(alphaMarker = 0.1)
### create bsInfo object
myBsInfo <- bsInfo$new(simInfo = mySimInfo,
specie = mySpec,
lociInfo = myLoci,
traitInfo = myTrait,
geno = NULL,
haplo = NULL,
founderIsInitPop = TRUE,
popNameBase = "Population",
initIndNames = NULL,
verbose = TRUE)
### create cross information object
for (i in 1:10) {
myCrossInfo <- crossInfo$new(parentPopulation = myBsInfo$populations[[myBsInfo$generation]],
method = "randomMate",
nNextPop = 100)
myBsInfo$nextGeneration(crossInfo = myCrossInfo)
}
geno <- myBsInfo$overGeneration()$genoMat
myBsInfo$print()
myBsInfo$plot(plotTarget = "trueAGV",
targetTrait = 1:3,
targetPopulation = 1:11,
plotType = "jitter")
Method nextGeneration()
create next population
Usage
bsInfo$nextGeneration(crossInfo)
Arguments
crossInfo[crossInfo class] Information of crossing (including selection scheme)
(see:crossInfo)
Method removeLatestPop()
remove latest population
Usage
bsInfo$removeLatestPop()
Method removeInitialPop()
remove initial population
Usage
bsInfo$removeInitialPop()
Method overGeneration()
assemble populations over generation
Usage
bsInfo$overGeneration(targetPop = NULL)
Arguments
targetPop[character / numeric] population names or No.s you want to assemble.
If NULL, assemble all the populations
Method parentInd()
search parents of an individual of interest
Usage
bsInfo$parentInd(indName)
Arguments
indName[character] individual name of interest
Method print()
Display information about the object
Usage
bsInfo$print()
Method plot()
Draw figures for visualization of each data (GVs)
Usage
bsInfo$plot(
plotTarget = "trueGV",
targetTrait = 1:self$traitInfo$nTraits,
targetPopulation = 1:self$generation,
plotType = "box",
facet_grid = FALSE,
unitSd = 1,
colorVecBase = c("black", "red", "green", "blue", "lightblue", "purple", "yellow",
"gray", "pink", "orange"),
widthVecBase = c(1.5, 0.8, 0.8),
dashVecBase = c("solid", "dash", "dash")
)
Arguments
plotTarget[character] Target of figure, select either one of
"trueAGV", "trueDGV", "trueEGV", "trueGV", "trueAGVET", "trueDGVET", "trueEGVET",
"trueGVET", "trueAGVCT", "trueDGVCT", "trueEGVCT", "trueGVCT".
targetTrait[numeric] Target traits. character is OK, but numeric vector
corresponding to target traits is preferred.
targetPopulation[numeric] Target populations. character is OK, but numeric vector
corresponding to target traits is preferred.
plotType[character] We offer "box", "violin", "jitter", "lines"
to draw figures for genotypic values.
facet_grid[logical] When you choose "jitter" for plotType,
you can select whether you display the plot for wach trait with grid.
unitSd[numeric] When you choose "lines", we will show the mean, mean + unitSd * sd,
mean - unitSd * sd.
colorVecBase[character] vector representing color of the line for traits in "lines" option
widthVecBase[numeric] vector representing width of the line in "lines" option
dashVecBase[character]vector representing solid / dash in "lines" option
Method clone()
The objects of this class are cloneable with this method.
Usage
bsInfo$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## ------------------------------------------------
## Method `bsInfo$new`
## ------------------------------------------------
### create simulation information
mySimInfo <- simInfo$new(simName = "Simulation Example",
simGeno = TRUE,
simPheno = TRUE,
#' nSimGeno = 1,
#' nSimPheno = 3,
#' nCoreMax = 4,
#' nCorePerGeno = 1,
#' nCorePerPheno = 3,
saveDataFileName = NULL)
### create specie information
mySpec <- specie$new(nChr = 3,
lChr = c(100, 150, 200),
specName = "Example 1",
ploidy = 2,
mutRate = 10^-8,
recombRate = 10^-6,
chrNames = c("C1", "C2", "C3"),
nLoci = 100,
recombRateOneVal = FALSE,
effPopSize = 100,
simInfo = mySimInfo,
verbose = TRUE)
### create lociInfo object
myLoci <- lociInfo$new(genoMap = NULL, specie = mySpec)
plot(myLoci, alpha = 0.1)
### create traitInfo object
myTrait <- traitInfo$new(lociInfo = myLoci,
nMarkers = 80,
nTraits = 3,
nQTLs = c(4, 8, 3),
actionTypeEpiSimple = TRUE,
qtlOverlap = TRUE,
nOverlap = c(2, 3, 0),
effCor = 0.1,
propDomi = 0.2,
interactionMean = c(4, 1, 2))
myTrait$plot(alphaMarker = 0.1)
### create bsInfo object
myBsInfo <- bsInfo$new(simInfo = mySimInfo,
specie = mySpec,
lociInfo = myLoci,
traitInfo = myTrait,
geno = NULL,
haplo = NULL,
founderIsInitPop = TRUE,
popNameBase = "Population",
initIndNames = NULL,
verbose = TRUE)
### create cross information object
for (i in 1:10) {
myCrossInfo <- crossInfo$new(parentPopulation = myBsInfo$populations[[myBsInfo$generation]],
method = "randomMate",
nNextPop = 100)
myBsInfo$nextGeneration(crossInfo = myCrossInfo)
}
geno <- myBsInfo$overGeneration()$genoMat
myBsInfo$print()
myBsInfo$plot(plotTarget = "trueAGV",
targetTrait = 1:3,
targetPopulation = 1:11,
plotType = "jitter")
KosukeHamazaki/myBreedSimulatR documentation built on Aug. 31, 2024, 3:55 p.m.
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| bsInfo | R Documentation |
R6 Class Representing a Breeding Scheme
Description
bsInfo object store specific information of one breeding scheme.
Public fields
bsName[character] name of this breeding scheme
simInfo[simInfo class] Simulation information (see:simInfo)
specie[specie class] Specie of the SNPs (see:specie)
lociInfo[lociInfo object] information about the individuals haplotypes' SNPs (see:lociInfo)
traitInfo[traitInfo class] Specific information of traits (see:traitInfo)
founderIsInitPop[logical] Founder haplotype will be regarded as first population or not.
seedSimHaplo[numeric] Random seed for selecting haplotype from founder haplotype
seedSimRM[numeric] Random seed for mate pairs
seedSimMC[numeric] Random seed for make crosses
popNameBase[character] base of population's name.
populations[list] A list of population objects
crossInfoList[list] A list of crossInfo objects
generation[list] current generation No. in the breeding scheme
herit[numeric] Heritability for each trait (plot-based/line-based)
envSpecificEffects[numeric] Effects specific to each environments / treatments. If 'multiTraitsAsEnvs = FALSE', envSpecificEffects will be 0 for all traits.
residCor[matrix] Residual correlation between traits
Active bindings
lociEffects[matrix] marker and QTL effects used for crossInfo object
Methods
Public methods
Method new()
Create a new bsInfo object.
Usage
bsInfo$new( bsName = "Undefined", simInfo, specie, lociInfo, traitInfo, geno = NULL, haplo = NULL, founderIsInitPop = TRUE, seedSimHaplo = NA, seedSimRM = NA, seedSimMC = NA, popNameBase = "Population", initIndNames = NULL, herit = NULL, envSpecificEffects = NULL, residCor = NULL, verbose = TRUE )
Arguments
bsName[character] Name of this breeding scheme
simInfo[simInfo class] Simulation information (see:simInfo)
specie[specie class] Specie of the SNPs (see:specie)
lociInfo[lociInfo object] information about the individuals haplotypes' SNPs (see:lociInfo)
traitInfo[traitInfo class] Specific information of traits (see:traitInfo)
geno[data.frame / matrix] genotype of the individuals encoded in allele dose. If you use real data, you must specify 'geno' or 'haplo' argument.
haplo[array] haplotype of the individuals scored with 0 and 1 (3-dimensional array).
founderIsInitPop[logical] Founder haplotype will be regarded as first population or not.
seedSimHaplo[numeric] Random seed for selecting haplotype from founder haplotype
seedSimRM[numeric] Random seed for mate pairs
seedSimMC[numeric] Random seed for make crosses
popNameBase[character] base of population's name.
initIndNames[character] NULL or character string vector specifying the individuals names for initial population. If NULL,
rownames(geno)will be used.herit[numeric] Heritability for each trait (plot-based/line-based)
envSpecificEffects[numeric] Effects specific to each environments / treatments. If 'multiTraitsAsEnvs = FALSE', envSpecificEffects will be 0 for all traits.
residCor[matrix] Residual correlation between traits
verbose[logical] Display info (optional)
Returns
A new 'bsInfo' object.
Examples
### create simulation information
mySimInfo <- simInfo$new(simName = "Simulation Example",
simGeno = TRUE,
simPheno = TRUE,
#' nSimGeno = 1,
#' nSimPheno = 3,
#' nCoreMax = 4,
#' nCorePerGeno = 1,
#' nCorePerPheno = 3,
saveDataFileName = NULL)
### create specie information
mySpec <- specie$new(nChr = 3,
lChr = c(100, 150, 200),
specName = "Example 1",
ploidy = 2,
mutRate = 10^-8,
recombRate = 10^-6,
chrNames = c("C1", "C2", "C3"),
nLoci = 100,
recombRateOneVal = FALSE,
effPopSize = 100,
simInfo = mySimInfo,
verbose = TRUE)
### create lociInfo object
myLoci <- lociInfo$new(genoMap = NULL, specie = mySpec)
plot(myLoci, alpha = 0.1)
### create traitInfo object
myTrait <- traitInfo$new(lociInfo = myLoci,
nMarkers = 80,
nTraits = 3,
nQTLs = c(4, 8, 3),
actionTypeEpiSimple = TRUE,
qtlOverlap = TRUE,
nOverlap = c(2, 3, 0),
effCor = 0.1,
propDomi = 0.2,
interactionMean = c(4, 1, 2))
myTrait$plot(alphaMarker = 0.1)
### create bsInfo object
myBsInfo <- bsInfo$new(simInfo = mySimInfo,
specie = mySpec,
lociInfo = myLoci,
traitInfo = myTrait,
geno = NULL,
haplo = NULL,
founderIsInitPop = TRUE,
popNameBase = "Population",
initIndNames = NULL,
verbose = TRUE)
### create cross information object
for (i in 1:10) {
myCrossInfo <- crossInfo$new(parentPopulation = myBsInfo$populations[[myBsInfo$generation]],
method = "randomMate",
nNextPop = 100)
myBsInfo$nextGeneration(crossInfo = myCrossInfo)
}
geno <- myBsInfo$overGeneration()$genoMat
myBsInfo$print()
myBsInfo$plot(plotTarget = "trueAGV",
targetTrait = 1:3,
targetPopulation = 1:11,
plotType = "jitter")
Method nextGeneration()
create next population
Usage
bsInfo$nextGeneration(crossInfo)
Arguments
crossInfo[crossInfo class] Information of crossing (including selection scheme) (see:crossInfo)
Method removeLatestPop()
remove latest population
Usage
bsInfo$removeLatestPop()
Method removeInitialPop()
remove initial population
Usage
bsInfo$removeInitialPop()
Method overGeneration()
assemble populations over generation
Usage
bsInfo$overGeneration(targetPop = NULL)
Arguments
targetPop[character / numeric] population names or No.s you want to assemble. If NULL, assemble all the populations
Method parentInd()
search parents of an individual of interest
Usage
bsInfo$parentInd(indName)
Arguments
indName[character] individual name of interest
Method print()
Display information about the object
Usage
bsInfo$print()
Method plot()
Draw figures for visualization of each data (GVs)
Usage
bsInfo$plot(
plotTarget = "trueGV",
targetTrait = 1:self$traitInfo$nTraits,
targetPopulation = 1:self$generation,
plotType = "box",
facet_grid = FALSE,
unitSd = 1,
colorVecBase = c("black", "red", "green", "blue", "lightblue", "purple", "yellow",
"gray", "pink", "orange"),
widthVecBase = c(1.5, 0.8, 0.8),
dashVecBase = c("solid", "dash", "dash")
)Arguments
plotTarget[character] Target of figure, select either one of "trueAGV", "trueDGV", "trueEGV", "trueGV", "trueAGVET", "trueDGVET", "trueEGVET", "trueGVET", "trueAGVCT", "trueDGVCT", "trueEGVCT", "trueGVCT".
targetTrait[numeric] Target traits. character is OK, but numeric vector corresponding to target traits is preferred.
targetPopulation[numeric] Target populations. character is OK, but numeric vector corresponding to target traits is preferred.
plotType[character] We offer "box", "violin", "jitter", "lines" to draw figures for genotypic values.
facet_grid[logical] When you choose "jitter" for plotType, you can select whether you display the plot for wach trait with grid.
unitSd[numeric] When you choose "lines", we will show the mean, mean + unitSd * sd, mean - unitSd * sd.
colorVecBase[character] vector representing color of the line for traits in "lines" option
widthVecBase[numeric] vector representing width of the line in "lines" option
dashVecBase[character]vector representing solid / dash in "lines" option
Method clone()
The objects of this class are cloneable with this method.
Usage
bsInfo$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## ------------------------------------------------
## Method `bsInfo$new`
## ------------------------------------------------
### create simulation information
mySimInfo <- simInfo$new(simName = "Simulation Example",
simGeno = TRUE,
simPheno = TRUE,
#' nSimGeno = 1,
#' nSimPheno = 3,
#' nCoreMax = 4,
#' nCorePerGeno = 1,
#' nCorePerPheno = 3,
saveDataFileName = NULL)
### create specie information
mySpec <- specie$new(nChr = 3,
lChr = c(100, 150, 200),
specName = "Example 1",
ploidy = 2,
mutRate = 10^-8,
recombRate = 10^-6,
chrNames = c("C1", "C2", "C3"),
nLoci = 100,
recombRateOneVal = FALSE,
effPopSize = 100,
simInfo = mySimInfo,
verbose = TRUE)
### create lociInfo object
myLoci <- lociInfo$new(genoMap = NULL, specie = mySpec)
plot(myLoci, alpha = 0.1)
### create traitInfo object
myTrait <- traitInfo$new(lociInfo = myLoci,
nMarkers = 80,
nTraits = 3,
nQTLs = c(4, 8, 3),
actionTypeEpiSimple = TRUE,
qtlOverlap = TRUE,
nOverlap = c(2, 3, 0),
effCor = 0.1,
propDomi = 0.2,
interactionMean = c(4, 1, 2))
myTrait$plot(alphaMarker = 0.1)
### create bsInfo object
myBsInfo <- bsInfo$new(simInfo = mySimInfo,
specie = mySpec,
lociInfo = myLoci,
traitInfo = myTrait,
geno = NULL,
haplo = NULL,
founderIsInitPop = TRUE,
popNameBase = "Population",
initIndNames = NULL,
verbose = TRUE)
### create cross information object
for (i in 1:10) {
myCrossInfo <- crossInfo$new(parentPopulation = myBsInfo$populations[[myBsInfo$generation]],
method = "randomMate",
nNextPop = 100)
myBsInfo$nextGeneration(crossInfo = myCrossInfo)
}
geno <- myBsInfo$overGeneration()$genoMat
myBsInfo$print()
myBsInfo$plot(plotTarget = "trueAGV",
targetTrait = 1:3,
targetPopulation = 1:11,
plotType = "jitter")
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