nVirginQueensFun: Sample a number of virgin queens

In SIMplyBee: 'AlphaSimR' Extension for Simulating Honeybee Populations and Breeding Programmes

Sample a number of virgin queens

Description

Sample a number of virgin queens - used when nFathers = NULL (see SimParamBee$nVirginQueens).

This is just an example. You can provide your own functions that satisfy your needs!

Usage

nVirginQueensPoisson(colony, n = 1, average = 10)

nVirginQueensTruncPoisson(colony, n = 1, average = 10, lowerLimit = 0)

nVirginQueensColonyPhenotype(
  colony,
  queenTrait = 1,
  workersTrait = 2,
  checkProduction = FALSE,
  lowerLimit = 0,
  simParamBee = NULL,
  ...
)

Arguments

colony	Colony-class
n	integer, number of samples
average	numeric, average number of virgin queens
lowerLimit	numeric, returned numbers will be above this value
queenTrait	numeric (column position) or character (column name), trait that represents queen's effect on the colony phenotype (defined in SimParamBee - see examples); if NULL then this effect is 0
workersTrait	numeric (column position) or character (column name), trait that represents workers's effect on the colony phenotype (defined in SimParamBee - see examples); if NULL then this effect is 0
checkProduction	logical, does the phenotype depend on the production status of colony; if yes and production is not TRUE, the result is above lowerLimit
simParamBee	SimParamBee, global simulation parameters
...	other arguments of mapCasteToColonyPheno

Details

nVirginQueensPoisson samples from a Poisson distribution, which can return a value 0 (that would mean a colony will fail to raise a single virgin queen after the queen swarms or dies).

nVirginQueensTruncPoisson samples from a truncated Poisson distribution (truncated at zero) to avoid failure.

nVirginQueensColonyPhenotype returns a number (above lowerLimit) as a function of colony phenotype, say swarming tendency. Colony phenotype is provided by mapCasteToColonyPheno. You need to set up traits influencing the colony phenotype and their parameters (mean and variances) via SimParamBee (see examples).

Value

numeric, number of virgin queens

Functions

• nVirginQueensTruncPoisson(): Sample a non-zero number of virgin queens

• nVirginQueensColonyPhenotype(): Sample a non-zero number of virgin queens based on colony's phenotype, say, swarming tendency

See Also

SimParamBee field nVirginQueens and vignette(topic = "QuantitativeGenetics", package = "SIMplyBee")

Examples

nVirginQueensPoisson()
nVirginQueensPoisson()
n <- nVirginQueensPoisson(n = 1000)
hist(n, breaks = seq(from = min(n), to = max(n)), xlim = c(0, 30))
table(n)

nVirginQueensTruncPoisson()
nVirginQueensTruncPoisson()
n <- nVirginQueensTruncPoisson(n = 1000)
hist(n, breaks = seq(from = min(n), to = max(n)), xlim = c(0, 30))
table(n)

# Example for nVirginQueensColonyPhenotype()
founderGenomes <- quickHaplo(nInd = 3, nChr = 1, segSites = 100)
SP <- SimParamBee$new(founderGenomes)

# Setting trait scale such that mean is 10 split into queen and workers effects
meanP <- c(5, 5 / SP$nWorkers)
# setup variances such that the total phenotype variance will match the mean
varA <- c(3 / 2, 3 / 2 / SP$nWorkers)
corA <- matrix(data = c(
  1.0, -0.5,
  -0.5, 1.0
), nrow = 2, byrow = TRUE)
varE <- c(7 / 2, 7 / 2 / SP$nWorkers)
varA / (varA + varE)
varP <- varA + varE
varP[1] + varP[2] * SP$nWorkers
SP$addTraitA(nQtlPerChr = 100, mean = meanP, var = varA, corA = corA)
SP$setVarE(varE = varE)
basePop <- createVirginQueens(founderGenomes)
drones <- createDrones(x = basePop[1], nInd = 50)
droneGroups <- pullDroneGroupsFromDCA(drones, n = 2, nDrones = 15)
colony1 <- createColony(x = basePop[2])
colony2 <- createColony(x = basePop[3])
colony1 <- cross(colony1, drones = droneGroups[[1]])
colony2 <- cross(colony2, drones = droneGroups[[2]])
colony1 <- buildUp(colony1)
colony2 <- buildUp(colony2)
nVirginQueensColonyPhenotype(colony1)
nVirginQueensColonyPhenotype(colony2)

SIMplyBee documentation built on Sept. 20, 2024, 5:07 p.m.