Nothing
## -----------------------------------------------------------------------------
library(rmetasim)
rland <- landscape.new.example()
rland <- landscape.simulate(rland,10)
landscape.amova(rland)
## -----------------------------------------------------------------------------
rland <- landscape.new.example()
for (gen in 1:10)
{
rland <- landscape.extinct(rland)
rland <- landscape.reproduce(rland)
rland <- landscape.survive(rland)
rland <- landscape.carry(rland)
rland <- landscape.advance(rland)
}
landscape.amova(rland)
## -----------------------------------------------------------------------------
library(magrittr)
rland <- landscape.new.example()
for (gen in 1:10)
{
rland <- rland %>% landscape.extinct() %>% landscape.reproduce() %>%
landscape.survive() %>% landscape.carry()%>%landscape.advance()
}
landscape.amova(rland)
## -----------------------------------------------------------------------------
create.land <- function()
{
### CREATE A LANDSCAPE
###first set up the matrices for local demographies
S <- matrix(c(0, 0, 1, 0), byrow = TRUE, nrow = 2)
R <- matrix(c(0, 1.1, 0, 0), byrow = TRUE, nrow = 2)
M <- matrix(c(0, 0, 0, 1), byrow = TRUE, nrow = 2)
#and epochs
S.epoch <- matrix(rep(0, 36), nrow = 6)
R.epoch <- matrix(rep(0, 36), nrow = 6)
M.epoch <- matrix(rep(0, 36), nrow = 6)
##now create the landscape
landscape.new.empty() %>%
landscape.new.intparam(h=3,s=2) %>%
landscape.new.switchparam(mp=0) %>%
landscape.new.floatparam() %>%
landscape.new.local.demo( S, R, M) %>%
landscape.new.epoch(S = S.epoch, R = R.epoch, M = M.epoch,
carry = c(1000, 1000, 1000)) %>%
landscape.new.locus(type = 1, ploidy = 2,
mutationrate = 0.001, transmission = 0, numalleles = 5) %>%
landscape.new.locus(type = 0, ploidy = 1,
mutationrate = 0.005, numalleles = 3, frequencies = c(0.2, 0.2, 0.6)) %>%
landscape.new.locus(type = 2, ploidy = 1,
mutationrate = 0.007, transmission = 1, numalleles = 3,
allelesize = 75) %>%
landscape.new.individuals(rep(c(500,500),3))
}
## ----eval=FALSE---------------------------------------------------------------
# rland <- create.land()
# retval <- matrix(0,ncol=4,nrow=11) #store the results col1 = gen, cols2-3 PhiST
# retval[1,] <- c(0,landscape.amova(landscape.sample(rland,ns=30))) #before any evolution occurs. Both populations from same source
# for (i in 2:11)
# {
# rland <- landscape.simulate(rland,10)
# retval[i,] <- c(rland$intparam$currentgen,landscape.amova(landscape.sample(rland,ns=30)))
# }
# ###the rest is just plotting the matrix of PhiSTs
# ###you can use any kind of graphics, of course. using lattice here.
# colnames(retval) <- c("gen","Loc1","Loc2","Loc3")
# library(lattice)
# xyplot(Loc1+Loc2+Loc3~gen, data=as.data.frame(retval), type=c("p","smooth"),auto.key=T,ylab="phiST",main="Change in phiST over time")
## ----eval=FALSE---------------------------------------------------------------
# number.reps=3
# retlst <- lapply(1:number.reps,function(x)
# {
# rland <- create.land()
# retval <- matrix(0,ncol=5,nrow=11)
# retval[1,] <- c(0,landscape.amova(landscape.sample(rland,ns=30)),x)
# for (i in 2:11)
# {
# rland <- landscape.simulate(rland,10)
# retval[i,] <- c(rland$intparam$currentgen,landscape.amova(landscape.sample(rland,ns=30)),x)
# }
# colnames(retval) <- c("gen","Loc1","Loc2","Loc3","replicate")
# retval
# })
# retdf <- as.data.frame(do.call(rbind,retlst))
# mns = with(retdf, aggregate(cbind(Loc1=Loc1,Loc2=Loc2,Loc3=Loc3),list(gen=gen),mean))
# mns$gen=as.numeric(mns$gen)
#
# xyplot(Loc1+Loc2+Loc3~gen,data=mns,auto.key=T,type=c("p","smooth"),ylab="phiST",main=paste("means of",number.reps,"replicates"))
# xyplot(Loc1+Loc2+Loc3~gen,data=retdf,auto.key=T,type=c("p","smooth"),ylab="phiST",main=paste(number.reps,"replicates for each locus"))
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