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Poisson point process simulation of spatial population dynamics

This repository contains source code (R/Rcpp) required to run spatial population dynamics simualation.

Installation

Windows

  • Install R (R Installer) - latest version used is 3.6.2
  • Install Rtools (Rtools Installer)
  • Install required R packages from CRAN (devtools, Rcpp, BH)
  • Install this source package using devtools
devtools::install_github("YegorGalkin/RcppSim")

Linux \ Ubuntu

TODO: like windows but without Rtools

MacOS

TODO: Like Linux

Simulator usage (1d)

  1. Prepare named list of simulation options. Parameters:
    • area_length_x - area size
    • cell_count_x - used to split simulation interval into subintervals for filtering negliable interactions.
    • b - poisson birth rate
    • d - poisson death rate
    • dd - competitive death rate
    • seed - RNG seed
    • init_density - per unit starting density of speciments
    • death_kernel_r - max death kernel interaction radius
    • death_kernel_y - death kernel values on uniform grid on [0, death_kernel_r] interval
    • birth_kernel_r - max birth kernel interaction radius
    • birth_kernel_y - birth kernel values on uniform grid on [0, birth_kernel_r] interval
    • spline_precision - tries to keep spline approximation precise up to this value (for numerical integration and cdf inversal)
  2. Run simulation for specified time or number of events 
    sim$run_events(1e9)
sim$run_for(100)
  3. Extract and visualize results (using packages spatstat and tidyverse) 
    sim$time
sim$total_population
sim$get_all_coordinates()

points <- ppp(sim$get_all_coordinates(),
              rep(0,sim$total_population),
              c(0,sim$area_length_x),
              c(-sim$area_length_x/2,sim$area_length_x/2)
              )
              
pcf_grid = seq(0,1,length.out = 1001)    

K_estimate <- Kest(points,r=pcf_grid,correction="Ripley")

pcf_estimate <- data.frame(Kest=K_estimate$iso/2,x=pcf_grid)%>%
                mutate(pfc=(Kest-lag(Kest))/(pcf_grid-lag(pcf_grid))/sim$area_length_x)%>%
                pull(pfc)
                
ggplot(data=data.frame(x=pcf_grid,y=pcf_estimate),aes(x=x,y=y))+
geom_point()
  4. Check examples in this repository (tests, examples)