SIMplyBee (version 0.4.1)

nVirginQueensPoisson: 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,
  ...
)

Value

numeric, number of virgin queens

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

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

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).

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$new(founderGenomes)
SP$nThreads = 1L
# 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)