growthFunction {rSHAPE}

This is a wrapper function where the birth and death related parameters of a SHAPE run are passed before the appropriate functions (and their associated methods) are called. This function will be called once per time step of a SHAPE run.

Description

This is a wrapper function where the birth and death related parameters of a SHAPE run are passed before the appropriate functions (and their associated methods) are called. This function will be called once per time step of a SHAPE run.

Usage

growthFunction(func_inSize, func_inFitness, func_bProb, func_dProb,
  func_deathDen_logical = FALSE, func_deathDen_max = NULL,
  func_deathDen_power = 4, func_sizeStep,
  func_growthForm = c("logistic", "exponential", "constant", "poisson"),
  func_carryingCapacity = NULL, func_basalRate = NULL,
  func_deathScale = FALSE, func_drift = TRUE,
  func_roundValues = FALSE, func_inIDs = NULL)

Arguments

func_inSize

This is the vector of population sizes within the community

func_inFitness

This is the vector of fitness value for the community

func_bProb

This is the general bith probability defined for this run of SHAPE

func_dProb

This is the general death probability defined for this run of SHAPE

func_deathDen_logical

This is a logical toggle to define if deaths are calculated in a density dependent manner.

func_deathDen_max

This is the community size at which maximum density dependent deaths (ie: 100% of func_inSize) occur.

func_deathDen_power

This is a scaling factor that controls the rate of transition between minimal and maximal values of the density dependent deaths. Higher values mean a steeper transition such that there are fewer deaths until higher densities are reached.

func_sizeStep

This is a proportional scalar that will control what proportion of a standard "generation" is simulated for each step within a SHAPE run. NOTE: This parameter is not perfectly validated to run as may be expected with all models. For now, it should be left as a value of "1", but exists for future implementation and testing.

func_growthForm

This is the implemeted growth model to be simulated in this run. Currently this can be one of "logistic","exponential","constant","poisson".

func_carryingCapacity

This is the maximum community size supported by tge simulated environment.

func_basalRate

This is the basal growth rate, otherwise definable as the number of offspring an individual will produce from a single birth event.

func_deathScale

This is a logical toggle to define if the number of births should be scaled by the number of deaths. The exact interpretation of this varies by growth model, but in general it forces growth to follow rates expected by standard pure birth models while still simulating deaths within the community.

func_drift

This is a logical toggle as to whether or not stochasticity is introduced into the deterministic calculations that may be encountered within the growth function. Its exact implementation varies based on the growth model being simulated.

func_roundValues

This is a logical toggle to define if the number of births and deaths are forced to be tracked as integer values. If TRUE, then any fractional amounts will be stochastically rounded to the nearest integer with a probability of being rounded up equal to the decimal value – ie: 0.32 means 32% chance of being rounded up –

func_inIDs

This is a vector of the genotype IDs passed to this function, its order should be representative of the ordered genotypeIDs passed for func_inSize and func_inFitness.

Value

A 2 column matrix of numeric values with columns "births" and "deaths", and rownames equal to func_inIDs (as.character).

Examples

# Imagine you've got an evolving community of three populations where
# in each time step 100% of individuals die and individuals with relateive
# fitness of 1 produce 2 offspring.  This growth function calculates the births
# and deaths of that community.
# First I show you when births are deterministic (proof of implementation):
growthFunction(func_inSize = c(100,100,100), func_inFitness = c(1,2,1.05),
                  func_bProb = 1, func_dProb = 1,
                  func_sizeStep = 1, func_growthForm = "exponential",
                  func_drift = FALSE, func_deathScale = TRUE)
# Now same things but with evolutionary drift thrown in
growthFunction(func_inSize = c(100,100,100), func_inFitness = c(1,2,1.05),
               func_bProb = 1, func_dProb = 1, func_sizeStep = 1,
               func_growthForm = "exponential", func_drift = TRUE,
               func_deathScale = TRUE)
# Now technically the values in the birth column is really the net population
# size and I'd previously set the births to be scaled by deaths but if this were
# not the case you'd get final population sizes of:
growthFunction(func_inSize = c(100,100,100), func_inFitness = c(1,2,1.05),
                   func_bProb = 1, func_dProb = 1, func_sizeStep = 1,
                   func_growthForm = "exponential", func_drift = TRUE,
                   func_deathScale = FALSE)