byandell/ewing
Ewing's Quantitative Population Ethology

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README.md
In byandell/ewing: Ewing's Quantitative Population Ethology

Ewing's Quantitative Population Ethology

Before installing the package, you may want to examine the interactive Shiny app https://data-viz.it.wisc.edu/ewing/, which provides web access to this package.

Change one of the simulation values on the Shiny app, say number or hosts or parasites, or number of simulation steps. Then click on Start Simulation button to start a simulation. You can redo a simulation with the same settings, or change the settings.

Install and Run Package

To install, first do

install.packages("devtools")

If on Windows, you will then need to install Rtools from http://cran.r-project.org/bin/windows/Rtools. This is an executable that will install some applications in c:\Rtools. You will also need pdflatex, which means you need a TeX distribution such as MikTeX or TeX Live.

library(devtools)

install_github("byandell/ewing")

If you have pandoc, you can install with vignette:

install_github("byandell/ewing", build_vignettes = TRUE)

See vignettes/ewing.Rmd for example use of code. A simple example is below:

library(ewing) # attach package
mysim <- init.simulation() # initialize simulation
simres <- future.events(mysim, plotit = FALSE) # simulate future events
plot(simres) # plot populations by stage or substrate over time
plot_current(simres, "host") # plot current (last) individuals over space

To change the number of initial individuals, do something like:

mysim <- init.simulation(count = c(200, 100))

will simulate 200 hosts and 100 parasites. You can also change the name of simulation file results from "sim.out" to something like "sim_200_100.out" to reflect the conditions of the simulation. Note that this file is written into the directory where you are using R.

simres <- future.events(mysim, "mysim_200_100.out", plotit = FALSE)

Here are some commands to use tidyverse for newer plots:

library(tidyverse)
ggplot_ewing(simres)
ggplot_current(simres, "host")

For more information, visit http://www.stat.wisc.edu/~yandell/ewing

Reference: B Ewing, BS Yandell, JF Barbieri, and RF Luck (2002) "Event-driven competing risks," Ecological Modelling 158: 35--50. http://doi.org/10.1016/S0304-3800(02)00218-1

Data organization

Community object

The object community has several elements that contain the current state of a simulation. This object is updated at every simulation step, which then changes the future event structure. Past events are removed and cannot be recovered, except in the sense that each individual has their current state and anticipated future state recorded.

For some reason, I set up community$pop entries for each species as matrices by row rather than column, with each column being an individual. It may be useful to flip it. The row names for each individual provide a complete picture of their current and next potential future state. It will take more digging to explain each of these row labels.

At each time step, the individual at the top of the leftist tree is examined, and actions may be taken. This typically involves a change in the leftist tree affecting just a few individuals. It might involve scheduling a future event for that individual, a death, one or more birth events. Events are either monadic, involving only that individual, or dyadic, involving two individuals, such as a parasite ovipositing (preying) on a host. In the case of a dyadic event, both individuals have altered futures.

At each time step where there is a change in the population structure, totals are written out with writeCount to a file, which can be read by readCount for instance to create plots over time using ggplot_ewing or plot_ewing. This file loses the individual information, which is only maintained in the community object at the current step. That is, there is temporal information without individual information.

It might be possible to store the updates as the simulation goes along in such a way that one could construct intermediate snapshots of the community. One interesting question is how to capture the spatial migration of individuals over the substrates (see ggplot_current).

Global datafiles

These datafiles are in the package directory ewing/data and are hard-wired into the code through calls in community.R file to internal function mydata. If the objects already exist, say by user supplying them, then the provided versions will be used. However, this is tricky, as one cannot at present provide user versions to the shiny app. In the future, these will be adapted more robustly to user input.

The file redscale.txt is an initial attempt to adapt to redscale-aphytis system. See https://github.com/byandell/redscale for more ideas on this system.

organism.features

future.host

substrate.host

future.parasite

substrate.PARASITE

host.parasite

TemperaturePar

TemperatureBase

Plot routines

The primary summary plot is plot.ewing, or ggplot_ewing, in plot.R. This gives a summary over the whole simulation. However, other plot routines were created, some being interactive:

Code layout

The init.simulation and future.events are the high level routines to initialize and run a simulation. They draw on default settings pulled from global datafiles. Plan is to make this more adaptable, of course.

Code Issues

init.R

init.simulation # starts simulation with default info

init.population

Initialize Organism Population

Create data structure with n organisms distributed across life stages. Includes leftist tree structure. Does various initializations based in information in future.xxx, where xxx is replaced by the name of the dataset. Normally, init.population is called by init.simulation.

init.population # initialize populations by species

Other routines in init.R

initOffspring( community, species )

getOffspring( community, species, offspring )

getOrgFeature

getOrgHosts

getOrgInteract

getOrgFuture( community, species, feature, current, future )

getOrgAlive( community, species, element )

getOrgAgeClass( community, species, stage, future)

getOrgMeanValue( community, species )

get.offspring( community, species )

community.R

initOrgInfo()

setOrgInfo()

getOrgInfo()

initCount() # simulation count object administration

initTemp( community, lo.hour, hi.hour ) # creates Temperature object

getTemp( community, element, sub )

getCount( community, species, "mintime")

setCount( community, species, elements )

get.species( community, species )

put.species( community, species, value )

get.individual( community, species, id )

put.individual( community, species, individual, id )

get.base( community, species )

setTemp( community, element, value )

init.timing( community )

set.timing( community, string, flag )

temp.R

activeTemp( community, lo.hour, hi.hour ) # updates Temperature object

showTemp( community )

getDegreeDay( community, hour )

temp.spline( community, hour, temp, start = 0, mintemp, cumulative = TRUE, mult )

break.backSpline( tmp )

checkTime( ) # check if activeTemp needs updating to cover time interval

getTime( community, species, x )

future.R

future.events

Realize future events in quantitative population ethology simulation

This is the main routine for Ewing's Quantitative Population Ethology. It steps through future events for individuals starting with the next minimum future event time. Steps through future events for community with one or more species. Keeps track of counts by age classes and substrates in external file.

The routine future.events builds the name of the event processor function based on event type, which is established in the future.host or future.parasite data structure. The event.attack uses further information from organism.features about the type of parasite (endo or ecto) in conjunction with the current event (feed or ovip) to determine the nature of attack.

A plot is created periodically unless plotit=FALSE. An external file is written with simulation counts for re-plotting.

future.events( community )

event.future

Process immediate, pending and future events in quantitative population ethology simulation.

Process an event for next individual in a species. Events may be monadic (one individual) or dyadic (involving two individuals). These are the main event processors for Ewing's Quantitative Population Ethology. They all take the same arguments, but handle events in quite different ways.

At present, the search strategies of parasites and health consequences of hosts are crude. However, search is over substrates and depends on the stage of hosts. A crude sex determination strategy is in place, with smaller (younger) hosts more likely to get male parasite eggs. The external file community.out is written with simulation counts following each event that changes the number or stage of individuals.

User-supplied event.blah functions can be specified in user-supplied libraries. Note that user code needs to comply with the arguments and values, and needs to process future events for individuals as they progress through life. Further, user routines need to properly update counts to the external file. This is at present an advanced topic, but can be figured out by examining the above routines.

event.future(community, species) # process immediate, pending and future events

event.death(community, species, id)

Other future.R routines

get.future # Get birth and future event

set.birth( community, species, neworg )

update.mintime( object, species, ... )

event.R

event.birth(community, species)

event.attack(community, species) # Interaction Events (only attack for now)

Other event.R routines

set.future( community, species, stage )

get.birth( community, species, offspring )

get.deplete( community, species )

get.attack( community, species, individual )

event.parsed( community, species, host, found ) # created by get() of function

This function is dynamically created in multiple places

move.R

event.move( community, species, individual )

is.move( community, species, individual )

event.find( community, species, host, event )

sim.R

updateCount( community, species, individual, is.death = FALSE, step )

triangle.R

rtri()



byandell/ewing documentation built on Jan. 3, 2024, 7:27 p.m.

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