breederInfo: R6 Class Representing a Breeder
In KosukeHamazaki/myBreedSimulatR: R Breeding Simulator for K.Hamazaki

breederInfo

R Documentation

R6 Class Representing a Breeder

Description

breederInfo object store specific information of one breeder.

Public fields

breederName: [character] name of this breeder
simInfo: [simInfo class] Simulation information (see:simInfo)
specie: [specie class] Specie of the SNPs (see:specie)
lociInfoFB: [list] information about the individuals haplotypes' SNPs available for breeder
traitInfoFB: [list] Specific information of traits available for breeder
popNameBase: [character] base of population's name.
populationsFB: [list] A list of populationFb objects available for breeders
crossInfoList: [list] A list of crossInfo objects
generation: [list] current generation No. in the breeder
calculateGRMBase: [logical] calculate genomic relationship matrix (GRM) for each population or not
methodsGRMBase: [character] default methods to calculate GRM
calcEpistasisBase: [logical] when additive / dominance GRM has already been calulated, whether or not calculate epistatic GRM
estimatedGVByGRMInfo: [list] A list of information on estimated GVs using GRM
estimatedMrkEffInfo: [list] A list of information on estimated marker effects
estimatedGVByMLRInfo: [list] A list of information on estimated GVs using MLR (multiple linear regression)
multiTraitsAsEnvs: [logical] Treat multiple traits as multiple environments or not
includeIntercept: [logical] Include intercept information when estimating genotypic values by replication
verbose: [boolean] display information
populationFB: [populationFB class] R6 class representing population for breeder

Methods

Method `new()`

Create a new breederInfo object.

Usage

breederInfo$new(
  breederName = "Undefined",
  bsInfo,
  mrkNames = NULL,
  initGenotyping = TRUE,
  initGenotypedIndNames = NULL,
  mafThres = 0.05,
  heteroThres = 1,
  calculateGRMBase = TRUE,
  methodsGRMBase = "addNOIA",
  calcEpistasisBase = FALSE,
  multiTraitsAsEnvs = FALSE,
  includeIntercept = TRUE,
  verbose = TRUE
)

Arguments

breederName: [character] name of this breeder
bsInfo: [bsInfo class] breeding scheme info (whichever generation is OK, but it will use only 1st population) (see:bsInfo)
mrkNames: [character] marker names
initGenotyping: [logical] obtain marker genotype for initial population or not
initGenotypedIndNames: [character] individual names that you want to genotype in initial population
mafThres: [numeric] threshold for removing markers with maf < mafThres
heteroThres: [numeric] threshold for removing markers with heteroRate >= heteroThres
calculateGRMBase: [logical] calculate genomic relationship matrix (GRM) for each population or not
methodsGRMBase: [character] default methods to calculate GRM
calcEpistasisBase: [logical] when additive / dominance GRM has already been calulated, whether or not calculate epistatic GRM
multiTraitsAsEnvs: [logical] Treat multiple traits as multiple environments or not
includeIntercept: [logical] Include intercept information when estimating genotypic values by replication
verbose: [logical] Display info (optional)

Returns

A new 'breederInfo' 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^-7,
                     recombRateOneVal = FALSE,
                     chrNames = c("C1", "C2", "C3"),
                     nLoci = 100,
                     effPopSize = 100,
                     simInfo = mySimInfo,
                     verbose = TRUE)
print(mySpec)

Method `getNewPopulation()`

get information on new population

Usage

breederInfo$getNewPopulation(
  bsInfo,
  generationNew = NULL,
  genotyping = TRUE,
  genotypedIndNames = NULL
)

Arguments

bsInfo: [bsInfo class] a bsInfo class object
generationNew: [numeric] a generation of new population
genotyping: [logical] Whether or not you want to genotype
genotypedIndNames: [character] individual names that you want to genotype

Method `phenotyper()`

get phenotypic values on current population

Usage

breederInfo$phenotyper(
  bsInfo,
  generationOfInterest = NULL,
  nRep = 1,
  phenotypedIndNames = NULL,
  estimateGV = TRUE,
  estimatedGVMethod = "lme4"
)

Arguments

bsInfo: [bsInfo class] a bsInfo class object
generationOfInterest: a generation where you want to obtain phenotypic values from population
nRep: [numeric] Replication of the field trial (common to all traits)
phenotypedIndNames: [character] individual names that you want to phenotype
estimateGV: [logical] estimate genotypic values by replication or not
estimatedGVMethod: [character] We offer 'lme4' and 'mean'. 'lme4' is recommended.
multiTraitsAsEnvs: [logical] Treat multiple traits as multiple environments or not

Method `estimateGVByGRM()`

estimate genotypic values based on GBLUP

Usage

breederInfo$estimateGVByGRM(
  trainingPop = NULL,
  testingPop = NULL,
  testingIndNames = NULL,
  methodsGRMFP = "addNOIA",
  bayesian = FALSE,
  multiTrait = FALSE,
  nIter = 10000,
  burnIn = 2000,
  thin = 5
)

Arguments

trainingPop: [character / numeric] training population names or No.s (not generations!!)
testingPop: [character / numeric] testing population names or No.s (not generations!!)
testingIndNames: [character] names of testing individuals
methodsGRMFP: [character] methods for calculating GRM for prediction
bayesian: [logical] use bayesian model (BGLR) or not (RAINBOWR) for solving mixed-effects model
multiTrait: [logical] use multiple-trait model for estimation of genotypic values
nIter: [numeric] the number of iterations
burnIn: [numeric] the number of burn-in
thin: [numeric] the number of thinning

Method `estimateMrkEff()`

estimate marker effects based on multiple linear regression (machine learning)

Usage

breederInfo$estimateMrkEff(
  trainingPop = NULL,
  trainingIndNames = NULL,
  methodMLR = "Ridge",
  multiTrait = FALSE,
  alpha = 0.5,
  nIter = 10000,
  burnIn = 2000,
  thin = 5,
  bayesian = FALSE,
  alphaMarker = 0.5,
  sizeMrkMin = 2,
  sizeMrkMax = 12
)

Arguments

trainingPop

[character / numeric] training population names or No.s (not generations!!)

trainingIndNames

[character] names of training individuals

methodMLR

[character] methods for estimating marker effects. The following methods are offered:

"Ridge", "LASSO", "ElasticNet", "RR-BLUP", "GBLUP", "BayesA" (uni-trait), "BayesB" (uni-trait), "BayesC" (uni-trait), "BRR", "BL" (uni-trait), "SpikeSlab" (multi-trait)

multiTrait

[logical] use multiple-trait model for estimation of marker effects or not

alpha

[numeric] the elastic net mixing parameter, with 0 \leq \alpha \leq 1. The penalty is defined as

\frac {1 - \alpha} { 2 } || \beta || _ 2 ^ 2 + \alpha || \beta || _ 1

'alpha = 1' is the lasso penalty, and 'alpha = 0' the ridge penalty.

nIter

[numeric] the number of iterations

burnIn

[numeric] the number of burn-in

thin

[numeric] the number of thinning

bayesian

[logical] use bayesian model (BGLR) or not (RAINBOWR) for solving mixed-effects model (only when ‘methodMLR = ’GBLUP'', this argument is valid.)

alphaMarker

[numeric] for plot: transparency for markers, see plot_ly

sizeMrkMin

[numeric] for plot: size for marker with minimum estimated effect

sizeMrkMax

[numeric] for plot: size for marker with maximum estimated effect

Method `estimateGVByMLR()`

estimate genotypic values based on GBLUP

Usage

breederInfo$estimateGVByMLR(
  trainingPop = NULL,
  trainingIndNames = NULL,
  testingPop = NULL,
  testingIndNames = NULL,
  methodMLR = "Ridge",
  multiTrait = FALSE,
  alpha = 0.5,
  nIter = 10000,
  burnIn = 2000,
  thin = 5,
  bayesian = FALSE
)

Arguments

trainingPop

[character / numeric] training population names or No.s (not generations!!)

trainingIndNames

[character] names of training individuals

testingPop

[character / numeric] testing population names or No.s (not generations!!)

testingIndNames

[character] names of testing individuals

methodMLR

[character] methods for estimating marker effects. The following methods are offered:

"Ridge", "LASSO", "ElasticNet", "RR-BLUP", "GBLUP", "BayesA" (uni-trait), "BayesB" (uni-trait), "BayesC" (uni-trait), "BRR", "BL" (uni-trait), "SpikeSlab" (multi-trait)

multiTrait

[logical] use multiple-trait model for estimation of marker effects or not

alpha

[numeric] the elastic net mixing parameter, with 0 \leq \alpha \leq 1. The penalty is defined as

\frac {1 - \alpha} { 2 } || \beta || _ 2 ^ 2 + \alpha || \beta || _ 1

'alpha = 1' is the lasso penalty, and 'alpha = 0' the ridge penalty.

nIter

[numeric] the number of iterations

burnIn

[numeric] the number of burn-in

thin

[numeric] the number of thinning

bayesian

[logical] use bayesian model (BGLR) or not (RAINBOWR) for solving mixed-effects model (only when ‘methodMLR = ’GBLUP'', this argument is valid.)

Method `overGeneration()`

Display informations about the object

assemble populations over generation

Usage

breederInfo$overGeneration(targetPop = NULL)

Arguments

targetPop: [character / numeric] population names or No.s you want to assemble. If NULL, assemble all the populations

Method `removeLatestPop()`

remove latest population

Usage

breederInfo$removeLatestPop()

Method `removeInitialPop()`

remove initial population

Usage

breederInfo$removeInitialPop()

Method `parentInd()`

search parents of an individual of interest

Usage

breederInfo$parentInd(indName)

Arguments

indName: [character] individual name of interest

Method `lociEffects()`

search parents of an individual of interest

Usage

breederInfo$lociEffects(bsInfo, trainingPop = NULL, methodMLR = NULL)

Arguments

bsInfo

[bsInfo class] breeding scheme info (whichever generation is OK, but it will use only 1st population) (see:bsInfo)#'

trainingPop

[character / numeric] training population names or No.s (not generations!!)

methodMLR

[character] methods for estimating marker effects. The following methods are offered:

"Ridge", "LASSO", "ElasticNet", "RR-BLUP", "GBLUP", "BayesA" (uni-trait), "BayesB" (uni-trait), "BayesC" (uni-trait), "BRR", "BL" (uni-trait), "SpikeSlab" (multi-trait)

Method `print()`

Display information about the object

Usage

breederInfo$print()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

breederInfo$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples


## ------------------------------------------------
## Method `breederInfo$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^-7,
                     recombRateOneVal = FALSE,
                     chrNames = c("C1", "C2", "C3"),
                     nLoci = 100,
                     effPopSize = 100,
                     simInfo = mySimInfo,
                     verbose = TRUE)
print(mySpec)

KosukeHamazaki/myBreedSimulatR documentation built on Aug. 31, 2024, 3:55 p.m.

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KosukeHamazaki/myBreedSimulatR R Breeding Simulator for K.Hamazaki

breederInfo: R6 Class Representing a Breeder In KosukeHamazaki/myBreedSimulatR: R Breeding Simulator for K.Hamazaki

R6 Class Representing a Breeder

Description

Public fields

Methods

Public methods

Method new()

Usage

Arguments

Returns

Examples

Method getNewPopulation()

Usage

Arguments

Method phenotyper()

Usage

Arguments

Method estimateGVByGRM()

Usage

Arguments

Method estimateMrkEff()

Usage

Arguments

Method estimateGVByMLR()

Usage

Arguments

Method overGeneration()

Usage

Arguments

Method removeLatestPop()

Usage

Method removeInitialPop()

Usage

Method parentInd()

Usage

Arguments

Method lociEffects()

Usage

Arguments

Method print()

Usage

Method clone()

Usage

Arguments

Examples

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KosukeHamazaki/myBreedSimulatR
R Breeding Simulator for K.Hamazaki

breederInfo: R6 Class Representing a Breeder
In KosukeHamazaki/myBreedSimulatR: R Breeding Simulator for K.Hamazaki

Method `new()`

Method `getNewPopulation()`

Method `phenotyper()`

Method `estimateGVByGRM()`

Method `estimateMrkEff()`

Method `estimateGVByMLR()`

Method `overGeneration()`

Method `removeLatestPop()`

Method `removeInitialPop()`

Method `parentInd()`

Method `lociEffects()`

Method `print()`

Method `clone()`