In choisy/gamar: Interface to the Agent-Based Modeling Platform GAMA

knitr::knit_hooks$set(margin = function(before,options,envir) {
  if(before) par(mgp=c(1.5,0.5,0),bty="n",plt=c(.105,.97,.13,.97))
  else NULL
})

knitr::opts_chunk$set(margin=T,prompt=T,comment="",collapse=T,cache=T,
                      dev.args=list(pointsize=11),fig.height=3.5,
                      fig.width=4.24725,fig.retina=2,fig.align="center")

dir.create("tmp")
knitr::opts_knit$set(root.dir=paste0(getwd(),"/tmp"))

This vignette shows how to install and use the gamar package.

Installing gamar

The package is currently on the GitHub repository. To install it from this repository you can use the install_github function from the devtools package. If this package is not installed on your system, install it

install.packages("devtools")

Then you can install the gamar package by typing:

devtools::install_github("choisy/gamar")

Note that the gamar package depends on the XML package. The installation of gamar package automatically installs the XML too, if not already installed on your system. Then, you can load and attach the gamar library:

Using gamar

library(gamar)

Note that you need the GAMA platform (version > 1.7.0) to be installed on your machine. For now, you have to set the path to the GAMA folder yourself:

defpath("path_to_your_GAMA_folder")

On most Mac it would be:

defpath("/Applications/Gama.app")

There are a number of example files in the examples directory of the library:

(f <- system.file("examples",package="gamar"))
dir(f)

An SIR model

Loading the sir experiment defined in the sir.gaml file:

dir()
experiment1 <- getmodelparameter(paste0(f,"/sir.gaml"),"sir")
dir()
class(experiment1)

The experiment:

experiment1

Note that the function getmodelparameter loads GAMA and creates the workgamar folder:

dir()

This folder contains at this stage an XML file containing the specifications of the newly created experiment:

dir("workgamar")

The parameters of the model:

getparameternames(experiment1)

The variables monitored during the simulation:

getoutputnames(experiment1)

Setting the values of the parameters (i.e. initial values of state variables and model paramters):

experiment1 <- setparametervalue(experiment1,"S0",990)
experiment1 <- setparametervalue(experiment1,"I0",10)
experiment1 <- setparametervalue(experiment1,"R0",0)
experiment1 <- setparametervalue(experiment1,"beta",.3)
experiment1 <- setparametervalue(experiment1,"gamma",.1)

Setting the frame rates of the monitored variables:

experiment1 <- setoutputframerate(experiment1,"susceptibles",1)
experiment1 <- setoutputframerate(experiment1,"infected",1)
experiment1 <- setoutputframerate(experiment1,"recovered",1)

Setting the number of steps to simulate:

experiment1 <- setfinalstep(experiment1,200)

Making a second experiment, modified from the first experiment (with new starting values of susceptibles and infected):

experiment2 <- setparametervalue(experiment1,"S0",995)
experiment2 <- setparametervalue(experiment2,"I0",5)

Putting the two experiment in one single experiment plan:

experimentplan <- addtoexperimentplan(experiment1)
experimentplan <- addtoexperimentplan(experimentplan,experiment2)

Running the experiment plan on two cores:

dir("workgamar")
output <- runexpplan(experimentplan,2)

Note the creation of another XML file and a folder

dir("workgamar")

This later folder contains one XML file per experiment, containing the experiment's output, and another folder:

dir("workgamar/out_sir_1")

This later folder may contain the snapshots of the simulations.

At the end, we can also remove this workgamar folder:

dir()
cleaning()
dir()

Plotting the dynamics of infected of the first experiment of the plan:

with(output[[1]],{
  plot(step,I,type="l",lwd=2,col="red",
       ylab="number of infected",ylim=c(0,1000))
  lines(step,S,lwd=2,col="blue")
  lines(step,R,lwd=2,col="green")
})

A Lotka-Volterra model

Loading the experiment prey_predator from the predator_prey model:

experiment1 <- getmodelparameter(
  paste0(system.file("examples",package="gamar"),
         "/predator_prey/models/predator_prey.gaml"),"prey_predator")

The parameters of the model:

getparameternames(experiment1)

The monitored variables of the model:

getoutputnames(experiment1)

Setting the model parameters to values:

experiment1 <- setparametervalue(experiment1,"Initial number of preys: ",990)
experiment1 <- setparametervalue(experiment1,"Initial number of predators: ",100)
experiment1 <- setparametervalue(experiment1,"Predator probability reproduce: ",0.1)

Specifying the number of steps:

experiment1 <- setfinalstep(experiment1,100)

Adding the experiment to an experiment plan:

experimentplan <- addtoexperimentplan(experiment1)

Running the experiment (only one here) of the plan:

output <- runexpplan(experimentplan,1)

Plotting the dynamics of the number of preys:

with(output[[1]],plot(step,`Number of preys`,type="l",lwd=2,col="red",
                      ylim=c(0,1050)))

We can also generate a movie of the simulation of one experiment:

dir()
#makemovie(output[[1]])
dir()

setwd("..")
knitr::opts_knit$set(root.dir=".")

unlink("tmp",T,T)

choisy/gamar documentation built on May 13, 2019, 5:30 p.m.