inst/examples/testPhenoFunction.R

In stranda/kernelPop2: Spatially explicit population genetic simulations

library(kernelPop2)
library(ggplot2)
library(dplyr)
source("helpers.R")
source("analysis.R")

### this script maks a 250 population grid and 
###


gapprop = 0



rland <- NULL
rland <- landscape.new.empty()
rland <- landscape.new.intparam(rland, h=1024, s=2,np=0,totgen=20000,maxland=3e5)
rland <- landscape.new.switchparam(rland,mp=0)
rland <- landscape.new.floatparam(rland,s=0,seedscale=c(50,500),
                                  seedshape=c(1,100),seedmix=c(0.4),
                                  pollenscale=c(50,200),pollenshape=c(1,1),
                                  pollenmix=0.1 , asp=0.5)


S <- matrix(c(
    0, 0,
    0.8, 0.0
), byrow=T, nrow = 2)

R <- matrix(c(
      0, 12,
      0,   0
  ), byrow=T, nrow = 2)

M <- matrix(c(
    0, 0,
                0, 1
), byrow=T, nrow = 2)

rland <- landscape.new.local.demo(rland,S,R,M)

S <- matrix(0,ncol = (rland$intparam$habitats*rland$intparam$stages),
            nrow = (rland$intparam$habitats*rland$intparam$stages))

R <- S
M <- S

rights <- floor(seq(00,30000,length=33))
tops <-   floor(seq(0,30000,length=33))
locs=NULL
for (i in 1:(length(tops)-1))
{
    locs <- rbind(locs,
                  data.frame(lft=c(rights[-1]-diff(rights[])+1),
                             bot=rep(tops[i+1]-(tops[2]-tops[1]),16),
                             rgt=rights[-1],
                             top=tops[i+1]))
}

lfts=which(locs$lft==1)
k=(0.30 * (sqrt((locs[,3]-locs[,1])*(locs[,4]-locs[,2]))))
e=rep(gapprop,rland$intparam$habitat)
k[c(528:529,560:561)] <- k[c(528:529,560:561)]*2
rland <- landscape.new.epoch(rland,S=S,R=R,M=M,
                             carry=k,
                             extinct=e,
                             leftx=locs[,1],
                             rightx=locs[,3],
                             boty=locs[,2],
                             topy=locs[,4],
                             maxland=c(min(locs[1]),min(locs[2]),max(locs[3]),max(locs[4])))


for (i in 1:16)
    rland <- landscape.new.locus(rland,type=1,ploidy=2,mutationrate=0.00,transmission=0,numalleles=2)


expmat <- matrix(c(
    1,   0,   0, 0,
    1,   0,   0, 0,
    1,   0,   0, 0,
    1,   0,   0, 0,
    0, 1,   0, 0,
    0, 1,   0, 0,
    0, 1,   0, 0,
    0, 1,   0, 0,
    0,   0, 1, 0,
    0,   0, 1, 0,
    0,   0, 1, 0,
    0,   0, 1, 0,
    0,   0,   0, 1,
    0,   0,   0, 1,
    0,   0,   0, 1,
    0,   0,   0, 1
             ),byrow=T,ncol=4)
hsq <- c(1,1,1,1)
rland <- landscape.new.expression(rland,expmat=expmat*0.125,hsq=hsq)
rland <- landscape.new.gpmap(rland,
                             matrix(c(-1,0,1, #short scale
                                      1,-0.5,1, #long scale
                                      -1,0,1, #long shape
                                      0,-0.5,1,     #mixture
                                      -1,0,1),ncol=3,byrow=T),
                             matrix(c(-1,0,1,
                                      -1,0,1,
                                      2,-0.5,1    #reproduction
                                      ),ncol=3,byrow=T))
initpopsize <- 2
inits <- matrix(0,ncol=rland$intparam$habitats,nrow=2)
inits[1:2,529:531] <- initpopsize  
rland <- landscape.new.individuals(rland,c(inits))

#rland$individuals[,5] <- 4500+floor(rland$individuals[,5]/10)
###############################
print("setup landscape")
l=rland
landscape.plot.phenotypes(l,1)