SIMplyBee (version 0.4.1)

calcPerformanceCriterion: Calculate the performance criterion

Description

Level 0 function that calculates the performance criterion as the sum of the queen (maternal) effect from the queen and the workers (direct) effect from her workers, as defined by Du et al. (2021). This can be seen as the expected value of the colony.

Usage

calcPerformanceCriterion(
  x,
  queenTrait = 1,
  workersTrait = 2,
  workersTraitFUN = sum,
  use = "gv",
  simParamBee = NULL
)

Value

integer when x is

Colony-class and a named list when x is

MultiColony-class, where names are colony IDs

Arguments

x

Colony-class or MultiColony-class

queenTrait

numeric (column position) or character (column name), trait that represents queen's effect on the colony value; if NULL then this effect is 0

workersTrait

numeric (column position) or character (column name), trait that represents workers' effect on the colony value; if NULL then this effect is 0

workersTraitFUN

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!

use

character, the measure to use for the calculation, being either "gv" (genetic value),"ebv" (estimated breeding value), or "pheno" (phenotypic value)

simParamBee

SimParamBee, global simulation parameters

References

Du, M., et al. (2021) Short-term effects of controlled mating and selection on the genetic variance of honeybee populations. Heredity 126, 733–747. tools:::Rd_expr_doi("10.1038/s41437-021-00411-2")

See Also

calcSelectionCriterion and calcInheritanceCriterion and as well as vignette(topic = "QuantitativeGenetics", package = "SIMplyBee")

Examples

founderGenomes <- quickHaplo(nInd = 8, nChr = 1, segSites = 100)
SP <- SimParamBee$new(founderGenomes)
SP$nThreads = 1L
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)

drones <- createDrones(x = basePop[1], nInd = 1000)
droneGroups <- pullDroneGroupsFromDCA(drones, n = 10, nDrones = nFathersPoisson)

# Create a Colony and a MultiColony class
colony <- createColony(x = basePop[2])
colony <- cross(colony, drones = droneGroups[[1]])
colony <- buildUp(colony)

apiary <- createMultiColony(basePop[3:4], n = 2)
apiary <- cross(apiary, drones = droneGroups[c(2, 3)])
apiary <- buildUp(apiary)

calcPerformanceCriterion(colony, queenTrait = 1, workersTrait = 2, workersTraitFUN = sum)
calcPerformanceCriterion(apiary, queenTrait = 1, workersTrait = 2, workersTraitFUN = sum)

apiary[[2]] <- removeQueen(apiary[[2]])
calcPerformanceCriterion(apiary, queenTrait = 1,
                         workersTrait = 2, workersTraitFUN = sum)