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ResistanceGA_PopGen_simFunction.R
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ResistanceGA_PopGen_simFunction.R
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#ResistanceGA_sim function
ResistanceGAMS.sim <- function(r.dim = 50,
RMexpvar_1 = 1,
RMexpscale_1 = 1,
RMexpvar_2 = 1,
RMexpscale_2 = 2,
transformation_1 = 3,
r.tran_shape_1 = 3,
r.tran_max_1 = 100,
transformation_2 = 8,
r.tran_shape_2 = 3,
r.tran_max_2 = 100,
RMexpvar_r = 1,
RMexpscale_r = 15,
RMexpvar_r2 = 1,
RMexpscale_r2 = 10,
habitat = 0.4,
n_samplepoints = 75,
start = NULL,
seed = NULL,
iters = 50,
method = 'standard',
island.pop = 50, ## 40
JULIA_HOME = "C:/Users/peterman.73/AppData/Local/Julia-1.1.0/bin/",
out.dir = "G:/BoxSync/Research/Manuscripts/Manuscripts_in_Progress/Collaborations/Winiarski/resistance_ms/multisurface/",
n.pops = 100, # total of plots to simulate (100)
n.ind = 30, #50 # Number of individuals per pop (50)
sex.ratio = 0.5,
n.loci = 15, # 15
n.allels = 15, # 15
steps = 200, # Generations (250) - 100 generations generally gives same results, just quicker to run.
n.offspring = 2, # 10 [2]
mig.rate = 0.2, #0.05 # 0.10 [0.075] 0.25
disp.max = 0.125, # Percent of max (0.15) 0.125;
disp.rate = 0.25, #0.25# 0.35 [0.25]
mut.rate = 0.0005 # 0.0005
){
library(ResistanceGA)
library(RandomFields)
library(PopGenReport)
if(is.null(start)) {
stop("Specify integer `start` value!!!")
}
# Helper Function ---------------------------------------------------------
surface_corrs <- function(A, orig, rep) {
rep <- rep * A
rep <- orig + rep
}
# Main Function -----------------------------------------------------------
# for(i in sd) {
for(z in start:iters){
# seed <- seed + 1
# >> Make Random Surfaces -------------------------------------------------
# Specify random model
# * Surface 1 ------------------------------------------------------------
# Create original and correlated surfaces for surface 1.
set.seed(z)
corr = c(0.9, 0.5, 0.1)
A <- sqrt((1/(corr^2)) - 1) # calcualate A
coo <- 1:r.dim
model <- RMexp(var=RMexpvar_1, scale=RMexpscale_1)
rf.sim <- RFsimulate(model = model, x=coo, y=coo, grid=TRUE)
surface_orig <- as.matrix(rf.sim)
rf.sim <- RFsimulate(model = model, x=coo, y=coo, grid=TRUE)
surface_rep <- as.matrix(rf.sim)
corr_surfaces <- lapply(A, surface_corrs, orig = surface_orig, rep = surface_rep)
out_rasts <- lapply(c(list(surface_orig), corr_surfaces), function(r) raster(scale(r)))
names(out_rasts) <- c("orig", paste0("cor_", corr))
bb <- extent(0.5, 50.5, 0.5, 50.5)
extent(out_rasts$orig) <- bb
out_rasts$orig <- setExtent(out_rasts$orig, bb, keepres=TRUE)
orig <- out_rasts$orig
extent(out_rasts$cor_0.9) <- bb
out_rasts$cor_0.9 <- setExtent(out_rasts$cor_0.9, bb, keepres=TRUE)
cor_0.9 <- out_rasts$cor_0.9
extent(out_rasts$cor_0.5) <- bb
out_rasts$cor_0.5 <- setExtent(out_rasts$cor_0.5, bb, keepres=TRUE)
cor_0.5 <- out_rasts$cor_0.5
extent(out_rasts$cor_0.1) <- bb
out_rasts$cor_0.1 <- setExtent(out_rasts$cor_0.1, bb, keepres=TRUE)
cor_0.1 <- out_rasts$cor_0.1
orig_1 <- orig
cor_0.9_1 <- cor_0.9
cor_0.5_1 <- cor_0.5
cor_0.1_1 <- cor_0.1
# * Surface 2 ------------------------------------------------------------
# Create original and correlated surfaces for surface 2.
corr = c(0.9, 0.5, 0.1)
A <- sqrt((1/(corr^2)) - 1) # calcualate A
coo <- 1:r.dim
model <- RMexp(var=RMexpvar_2, scale=RMexpscale_2)
rf.sim <- RFsimulate(model = model, x=coo, y=coo, grid=TRUE)
surface_orig <- as.matrix(rf.sim)
rf.sim <- RFsimulate(model = model, x=coo, y=coo, grid=TRUE)
surface_rep <- as.matrix(rf.sim)
corr_surfaces <- lapply(A, surface_corrs, orig = surface_orig, rep = surface_rep)
out_rasts <- lapply(c(list(surface_orig), corr_surfaces), function(r) raster(scale(r)))
names(out_rasts) <- c("orig", paste0("cor_", corr))
bb <- extent(0.5, 50.5, 0.5, 50.5)
extent(out_rasts$orig) <- bb
out_rasts$orig <- setExtent(out_rasts$orig, bb, keepres=TRUE)
orig <- out_rasts$orig
extent(out_rasts$cor_0.9) <- bb
out_rasts$cor_0.9 <- setExtent(out_rasts$cor_0.9, bb, keepres=TRUE)
cor_0.9 <- out_rasts$cor_0.9
extent(out_rasts$cor_0.5) <- bb
out_rasts$cor_0.5 <- setExtent(out_rasts$cor_0.5, bb, keepres=TRUE)
cor_0.5 <- out_rasts$cor_0.5
extent(out_rasts$cor_0.1) <- bb
out_rasts$cor_0.1 <- setExtent(out_rasts$cor_0.1, bb, keepres=TRUE)
cor_0.1 <- out_rasts$cor_0.1
#Create original and correlated surfaces for surface 2.
orig_2 <- orig
cor_0.9_2 <- cor_0.9
cor_0.5_2 <- cor_0.5
cor_0.1_2 <- cor_0.1
# * Random surface ------------------------------------------------------------
corr = c(0.9, 0.5, 0.1)
A <- sqrt((1/(corr^2)) - 1) # calcualate A
coo <- 1:r.dim
model <- RMexp(var=RMexpvar_r, scale=RMexpscale_r)
rf.sim <- RFsimulate(model = model, x=coo, y=coo, grid=TRUE)
surface_orig <- as.matrix(rf.sim)
rf.sim <- RFsimulate(model = model, x=coo, y=coo, grid=TRUE)
surface_rep <- as.matrix(rf.sim)
corr_surfaces <- lapply(A, surface_corrs, orig = surface_orig, rep = surface_rep)
out_rasts <- lapply(c(list(surface_orig), corr_surfaces), function(r) raster(scale(r)))
names(out_rasts) <- c("orig", paste0("cor_", corr))
bb <- extent(0.5, 50.5, 0.5, 50.5)
extent(out_rasts$orig) <- bb
out_rasts$orig <- setExtent(out_rasts$orig, bb, keepres=TRUE)
orig <- out_rasts$orig
extent(out_rasts$cor_0.9) <- bb
out_rasts$cor_0.9 <- setExtent(out_rasts$cor_0.9, bb, keepres=TRUE)
cor_0.9 <- out_rasts$cor_0.9
extent(out_rasts$cor_0.5) <- bb
out_rasts$cor_0.5 <- setExtent(out_rasts$cor_0.5, bb, keepres=TRUE)
cor_0.5 <- out_rasts$cor_0.5
extent(out_rasts$cor_0.1) <- bb
out_rasts$cor_0.1 <- setExtent(out_rasts$cor_0.1, bb, keepres=TRUE)
cor_0.1 <- out_rasts$cor_0.1
#Create original and correlated surfaces for surface 2.
random_1 <- orig
cor_0.9_3 <- cor_0.9
cor_0.5_3 <- cor_0.5
cor_0.1_3 <- cor_0.1
# >> Create Directory -----------------------------------------------------
# SD <- ifelse(i == 0.5, '0_5', '1_25')
dir.create(paste0(out.dir, "Results/",
'PopGen/',
'iter__', z),
recursive = TRUE)
out <- paste0(out.dir, "Results/",
'PopGen/',
'iter__', z, "/")
tmp <- paste0(tempdir(), '\\')
# >> Create Truth ---------------------------------------------------------
r.stack <- stack(orig_1,
orig_2,
random_1)
names(r.stack) <- c('cont1', 'cont2', 'rand')
# #Run 'GA.prep'
GA.inputs <- GA.prep(ASCII.dir = r.stack[[-3]],
select.trans = list('A','A'),
max.cat = 500,
max.cont = 500,
Results.dir = tmp,
quiet = TRUE)
# * Julia Prep -----------------------------------------------------------
x.coord <- SpatialPoints(cbind(runif(n_samplepoints, 10, 40),
runif(n_samplepoints, 10, 40)))
jl.inputs <- jl.prep(n.Pops = n_samplepoints,
CS_Point.File = x.coord,
JULIA_HOME = JULIA_HOME)
PARM <- c(transformation_1, #Transformation for continuous surface 1
r.tran_shape_1, #shape parameter for continuous surface 1
r.tran_max_1, #max parameter for continuous surface 1
transformation_2, #Transformation for continuous surface 1
r.tran_shape_2, #shape parameter for continuous surface 1
r.tran_max_2) #max parameter for continuous surface 2
# Combine resistance surfaces
Resist <- Combine_Surfaces(PARM = PARM,
jl.inputs = jl.inputs,
GA.inputs = GA.inputs,
out = NULL,
rescale = TRUE)
sample.thresh <- as.numeric(quantile(Resist, habitat))
# Select cells away from the edge that are suitable habitat
rast.df <- as.data.frame(Resist, xy = T) # convert raster to data frame for sampling
mn.cell <- floor(min(rast.df$x) + (.20*max(rast.df$x)))
mx.cell <- max(rast.df$x) - mn.cell
habitat.rast <- which(rast.df$x > mn.cell &
rast.df$x < mx.cell &
rast.df$y > mn.cell &
rast.df$y < mx.cell &
rast.df$layer <= sample.thresh)
sample.sites <- sample(habitat.rast, size = n.pops, replace = F)
sample.xy <- rast.df[sample.sites, 1:2]
sp.coord <- SpatialPoints(sample.xy)
# >> Calculate cost distance -------------------------------------------------
jl.inputs <- jl.prep(n.Pops = n.pops,
CS_Point.File = sp.coord,
JULIA_HOME = JULIA_HOME)
## Pairwise resistance distances
r.dist <- Run_CS.jl(jl.inputs = jl.inputs,
r = Resist,
full.mat = T
)
plot(Resist)
plot(sp.coord, add = T)
# Conduct simulation ------------------------------------------------------
pop.sim_init <- init.popgensim(n.pops = n.pops,
n.ind = n.ind,
sex.ratio = sex.ratio,
n.loci = n.loci,
n.allels = n.allels,
n.cov = 3)
sim.out <- run.popgensim(simpops = pop.sim_init,
steps = steps,
cost.mat = r.dist,
n.offspring = n.offspring,
n.ind = n.ind,
mig.rate = mig.rate,
disp.max = disp.max * max(r.dist),
disp.rate = disp.rate,
n.allels = n.allels,
mut.rate = mut.rate)
pops.gi <- pops2genind(sim.out)
fst.out <- pairwise.fstb(pops.gi)
## Convert `genind` object to `genpop` object
pop.gp <- genind2genpop(pops.gi)
## Calculate Dc from `genpop` object
dc.out <- dist.genpop(pop.gp, method = 2, diag = T, upper = T) %>% as.matrix()
## Need to subsample here from dc.out
select.pops <- sort(sample(length(sp.coord),
size = n_samplepoints,
replace = F))
dc.select <- dc.out[select.pops, select.pops]
fst.select <- fst.out[select.pops, select.pops]
Sample.coord <- sp.coord[select.pops]
r.select <- r.dist[select.pops,select.pops]
## Quick plot select
par(mfrow = c(2,2))
plot(lower(dc.select) ~ lower(r.select))
plot(lower(fst.select) ~ lower(r.select))
plot(lower(dc.select) ~ c(dist(Sample.coord@coords)))
plot(lower(fst.select) ~ c(dist(Sample.coord@coords)))
par(mfrow = c(1,1))
# Optimize: All Comb ----------------------------------------------------------------
if(method == 'island') {
GA.inputs <- GA.prep(ASCII.dir = r.stack,
Results.dir = 'all_comb',
max.cont = 500,
select.trans = list('A', 'A', 4),
maxiter = 52,
run = 8,
island.pop = island.pop,
numIslands = 4,
migrationInterval = 4,
gaisl = T
)
} else {
GA.inputs <- GA.prep(ASCII.dir = r.stack,
Results.dir = 'all_comb',
max.cont = 500,
select.trans = list('A', 'A', 'A'),
maxiter = 65, #65
run = 15,
# pop.size = 5,
parallel = 4
)
}
jl.inputs <- jl.prep(n.Pops = n_samplepoints,
CS_Point.File = Sample.coord,
response = lower(dc.select),
JULIA_HOME = JULIA_HOME)
Rga_out <- all_comb(jl.inputs = jl.inputs,
GA.inputs = GA.inputs,
# iters = 10,
results.dir = out
)
write.csv(dc.select,
file = paste0(out, "dc_select.csv"))
write.csv(dc.out,
file = paste0(out, "dc_all.csv"))
writeRaster(Resist,
paste0(out, "true_resist.asc"))
sample.pts <- cbind((1:n_samplepoints),Sample.coord@coords)
colnames(sample.pts) <- c("pop","x","y")
write.table(sample.pts, paste0(out,"sample_pts.txt"),
sep = '\t',
row.names = F,
col.names = T)
sample.pts_all <- cbind((1:n_samplepoints),sp.coord@coords)
colnames(sample.pts_all) <- c("pop","x","y")
write.table(sample.pts_all, paste0(out,"sample_pts_all.txt"),
sep = '\t',
row.names = F,
col.names = T)
} # end iteration loop (z)
} # end function