population: R6 class representing population of multiple moths

In data-zoo-gang/ABMR6: Example of agent-based model using R6

Description

R6 class representing population of multiple moths.

Public fields

individuals

List. List of R6 objects of class 'moth'.

Methods

Public methods

• population$new()

• population$computefitness()

• population$mutation()

• population$reproduction()

• population$generation()

• population$clone()

---

Method new()

Initialize new R6 object of class 'population'.

Usage

population$new(N = 100, mutation_rate = 1e-06, world)

Arguments

N

Integer. Number of individual moths in the population.

mutation_rate

Probability. Probability that moths can change colour in a timestep.

world

R6 object of class world. World in which moths live.

Returns

An R6 object of class 'population'

---

Method computefitness()

Compute fitness of all moths in the population.

Usage

population$computefitness()

Details

Run the method 'computefitness' of each moth object in the population.

Examples

set.seed(123L)
my_world <- world$new()
my_pop   <- population$new(N = 50, world = my_world)

#Fitness of all moths is NA on initialisation
my_pop$individuals[[1]]$fitness

#Compute fitness of all moths
my_pop$computefitness()

#Plot fitness of moths in the world
all_fitness <- sapply(my_pop$individuals, function(x){x$fitness}) 
hist(all_fitness)

---

Method mutation()

Change colour of all moths.

Usage

population$mutation()

Details

Run the method 'mutation' of each moth object in the population.

Examples

set.seed(123L)
my_world <- world$new()
my_pop   <- population$new(N = 50, mutation_rate = 0.75, world = my_world)

#Colour of moths should be ~equal at start
all_colour <- sapply(my_pop$individuals, function(x){x$colour})
hist(all_colour)

#Change colour of all moths with probability defined by `mutation_rate`
#Colours have changed
my_pop$mutation()
all_colour <- sapply(my_pop$individuals, function(x){x$colour}) 
hist(all_colour)

---

Method reproduction()

Moths reproduce with a probability defined by their fitness in the environment.

Usage

population$reproduction()

Details

Offspring are clones of their parents (i.e. they have the same colour!)

Examples

set.seed(123L)
my_world <- world$new()
my_pop   <- population$new(N = 50, world = my_world)

#At initialization colour of moths is ~even
all_colour <- sapply(my_pop$individuals, function(x){x$colour})
hist(all_colour)

#The world is black (i.e. colour = 1)
#So black moths will have higher fitness than white moths
my_world$colour
my_pop$computefitness()

#Black moths have higher fitness and more more likely to reproduce
#After reproduction, black individuals are much more common
my_pop$reproduction()
all_colour <- sapply(my_pop$individuals, function(x){x$colour})
hist(all_colour)

---

Method generation()

Run all demographic methods.

Usage

population$generation()

Details

Compute fitness, mutation and reproduction for all moths.

Examples

set.seed(123L)
my_world <- world$new()
my_pop   <- population$new(N = 50, world = my_world)

#At initialization colour of moths is ~even
all_colour <- sapply(my_pop$individuals, function(x){x$colour})
hist(all_colour)

#The world is black (i.e. colour = 1)
#So black moths will have higher fitness than white moths
my_world$colour
my_pop$generation()

#There will be more black moths in the next generation
all_colour <- sapply(my_pop$individuals, function(x){x$colour})
hist(all_colour)

---

Method clone()

The objects of this class are cloneable with this method.

Usage

population$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

 set.seed(2L)
 my_world <- world$new()
 my_population <- population$new(N = 4, world = my_world)
 my_population$individuals[[1]]$colour <- 1
 my_population$computefitness()
 print(unlist(lapply(my_population$individuals, function(i) i$colour)))
 my_population$reproduction()
 print(unlist(lapply(my_population$individuals, function(i) i$colour)))

## ------------------------------------------------
## Method `population$computefitness`
## ------------------------------------------------

set.seed(123L)
my_world <- world$new()
my_pop   <- population$new(N = 50, world = my_world)

#Fitness of all moths is NA on initialisation
my_pop$individuals[[1]]$fitness

#Compute fitness of all moths
my_pop$computefitness()

#Plot fitness of moths in the world
all_fitness <- sapply(my_pop$individuals, function(x){x$fitness}) 
hist(all_fitness)

## ------------------------------------------------
## Method `population$mutation`
## ------------------------------------------------

set.seed(123L)
my_world <- world$new()
my_pop   <- population$new(N = 50, mutation_rate = 0.75, world = my_world)

#Colour of moths should be ~equal at start
all_colour <- sapply(my_pop$individuals, function(x){x$colour})
hist(all_colour)

#Change colour of all moths with probability defined by `mutation_rate`
#Colours have changed
my_pop$mutation()
all_colour <- sapply(my_pop$individuals, function(x){x$colour}) 
hist(all_colour)

## ------------------------------------------------
## Method `population$reproduction`
## ------------------------------------------------

set.seed(123L)
my_world <- world$new()
my_pop   <- population$new(N = 50, world = my_world)

#At initialization colour of moths is ~even
all_colour <- sapply(my_pop$individuals, function(x){x$colour})
hist(all_colour)

#The world is black (i.e. colour = 1)
#So black moths will have higher fitness than white moths
my_world$colour
my_pop$computefitness()

#Black moths have higher fitness and more more likely to reproduce
#After reproduction, black individuals are much more common
my_pop$reproduction()
all_colour <- sapply(my_pop$individuals, function(x){x$colour})
hist(all_colour)

## ------------------------------------------------
## Method `population$generation`
## ------------------------------------------------

set.seed(123L)
my_world <- world$new()
my_pop   <- population$new(N = 50, world = my_world)

#At initialization colour of moths is ~even
all_colour <- sapply(my_pop$individuals, function(x){x$colour})
hist(all_colour)

#The world is black (i.e. colour = 1)
#So black moths will have higher fitness than white moths
my_world$colour
my_pop$generation()

#There will be more black moths in the next generation
all_colour <- sapply(my_pop$individuals, function(x){x$colour})
hist(all_colour)

data-zoo-gang/ABMR6 documentation built on Dec. 19, 2021, 8:12 p.m.