Level 0 function that calculates the selection criterion as the sum of workers (direct) and queen (maternal) effects of workers, as defined by Du et al. (2021). This can be seen as the expected value of virgin queens from the queen (as well as workers, but we would not be selecting workers).
calcSelectionCriterion(
x,
queenTrait = 1,
queenTraitFUN = sum,
workersTrait = 2,
workersTraitFUN = sum,
use = "gv"
)numeric (column position) or character (column name), trait
that represents queen's effect on the colony value; if NULL then this contribution is 0
function, that will be applied to the queen effect values of workers, default is sum (see examples), but note that the correct function will depend on how you will setup simulation!
numeric (column position) or character (column name), trait
that represents workers' effect on the colony value; if NULL then this contribution is 0
function, that will be applied to the workers effect values of workers, default is sum (see examples), but note that the correct function will depend on how you will setup simulation!
character, the measure to use for the calculation, being either "gv" (genetic value), "ebv" (estimated breeding value), or "pheno" (phenotypic value)
integer when x is
Colony-class and a named list when x is
MultiColony-class, where names are colony IDs
Du, M., et al. (2021) Short-term effects of controlled mating and selection on the genetic variance of honeybee populations. Heredity 126, 733–747. doi:/10.1038/s41437-021-00411-2
calcInheritanceCriterion and
calcPerformanceCriterion and as well as
founderGenomes <- quickHaplo(nInd = 8, nChr = 1, segSites = 100)
SP <- SimParamBee$new(founderGenomes)
meanA <- c(10, 10 / SP$nWorkers)
varA <- c(1, 1 / SP$nWorkers)
corA <- matrix(data = c( 1.0, -0.5,
-0.5, 1.0), nrow = 2, byrow = TRUE)
SP$addTraitA(nQtlPerChr = 100, mean = meanA, var = varA, corA = corA,
name = c("queenTrait", "workersTrait"))
varE <- c(3, 3 / SP$nWorkers)
corE <- matrix(data = c(1.0, 0.3,
0.3, 1.0), nrow = 2, byrow = TRUE)
SP$setVarE(varE = varE, corE = corE)
basePop <- createVirginQueens(founderGenomes)
#> Error in get(x = "SP", envir = .GlobalEnv): object 'SP' not found
drones <- createDrones(x = basePop[1], nInd = 1000)
#> Error in get(x = "SP", envir = .GlobalEnv): object 'SP' not found
droneGroups <- pullDroneGroupsFromDCA(drones, n = 10, nDrones = nFathersPoisson)
#> Error in get(x = "SP", envir = .GlobalEnv): object 'SP' not found
# Create a Colony and a MultiColony class
colony <- createColony(x = basePop[2])
#> Error in get(x = "SP", envir = .GlobalEnv): object 'SP' not found
colony <- cross(colony, drones = droneGroups[[1]])
#> Error in get(x = "SP", envir = .GlobalEnv): object 'SP' not found
colony <- buildUp(colony)
#> Error in get(x = "SP", envir = .GlobalEnv): object 'SP' not found
apiary <- createMultiColony(basePop[3:4], n = 2)
#> Error in createMultiColony(basePop[3:4], n = 2): object 'basePop' not found
apiary <- cross(apiary, drones = droneGroups[c(2, 3)])
#> Error in get(x = "SP", envir = .GlobalEnv): object 'SP' not found
apiary <- buildUp(apiary)
#> Error in get(x = "SP", envir = .GlobalEnv): object 'SP' not found
calcSelectionCriterion(colony,
queenTrait = 1, queenTraitFUN = sum,
workersTrait = 2, workersTraitFUN = sum)
#> Error in is(x, class2 = "Colony"): object 'colony' not found
calcSelectionCriterion(apiary,
queenTrait = 1, queenTraitFUN = sum,
workersTrait = 2, workersTraitFUN = sum)
#> Error in is(x, class2 = "Colony"): object 'apiary' not found
apiary[[2]] <- removeQueen(apiary[[2]])
#> Error in get(x = "SP", envir = .GlobalEnv): object 'SP' not found
calcSelectionCriterion(apiary, queenTrait = 1,
workersTrait = 2, workersTraitFUN = sum)
#> Error in is(x, class2 = "Colony"): object 'apiary' not found