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ResistanceGA_SD_Noise_simFunction.R
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#ResistanceGA_sim multisurface 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,
sd = c(0.5, 1.25), ## Specify level(s) of noise to add to resitance distance
start = NULL,
seed = NULL,
iters = 50,
method = 'standard',
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/"){
library(ResistanceGA)
library(RandomFields)
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/",
'SD__', SD,"/",
'iter__', z),
recursive = TRUE)
out <- paste0(out.dir, "Results/",
'SD__', SD,"/",
'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]],
# maxiter = 1,
select.trans = list('A','A'),
# select.trans = list(c(1,2,3,5,7,8,9),c(1,2,3,5,7,8,9)),
max.cat = 500,
max.cont = 500,
Results.dir = tmp,
quiet = TRUE)
#habitat <- 0.4 # Could add this to the function call for easy manipulation
sample.thresh <- as.numeric(quantile(orig_1, habitat))
# Select cells away from the edge that are suitable habitat
rast.df <- as.data.frame(orig_1, 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_samplepoints, replace = F)
sample.xy <- rast.df[sample.sites, 1:2]
Sample.coord <- SpatialPoints(sample.xy)
# # * Julia Prep -----------------------------------------------------------
#
# jl.inputs <- jl.prep(n.Pops = n_samplepoints,
# CS_Point.File = Sample.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)
#
# 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$x) - mn.cell
#
# #Cells buffered from edge
# habitat.rast <- which(rast.df$x > mn.cell &
# rast.df$x < mx.cell &
# rast.df$y > mn.cell &
# rast.df$y < mx.cell)
#
# sample.sites <- sample(habitat.rast)
# sample.xy <- rast.df[sample.sites, 1:3]
# sample.xy <- as.data.frame(sample.xy, xy = T)
#
# rast_min <- min(sample.xy$layer)
# rast_max <- max(sample.xy$layer)
#
# for(j in 1:nrow(sample.xy)){
# sample.xy$random[j] <- runif(1,rast_min, rast_max)
# col }
#
# sample.xy$keep <- ifelse(sample.xy$random >= sample.xy$layer, 1,0)
#
# sample.xy <- subset(sample.xy, keep == 1)
#
# sample.cells <- dplyr::sample_n(sample.xy, n_samplepoints)
# sample.cells <- sample.cells[,1:2]
# #sample.random <- rast.df[sample.cells]
# Sample.coord <- SpatialPoints(sample.cells)
#
#
# #plot(Resist, col=resistcols)
# #plot(Sample.coord, pch=15, col="black", add= TRUE)
#
#
# # Re-run Julia
# jl.inputs <- jl.prep(n.Pops = n_samplepoints,
# CS_Point.File = Sample.coord,
# JULIA_HOME = JULIA_HOME)
#
# #Pairwise resistance distacnes
# jl.response <- Run_CS.jl(jl.inputs = jl.inputs,
# r = Resist,
# full.mat = T
# )
#
# #Add noise
# ## The idea here is to have it be a relative error
# noise <- rnorm(length(lower(jl.response)), mean = lower(jl.response), sd = i * lower(jl.response))
# noise0 <- rnorm(length(lower(jl.response)), mean = 0, sd = i * mean(lower(jl.response)))
#
# jl.response_noise <- lower(jl.response) + noise
# jl.response_noise0 <- lower(jl.response) + noise0
#
# resp1 <- ResistanceGA:::SCALE.vector(jl.response_noise, 0, 1)
# resp2 <- ResistanceGA:::SCALE.vector(jl.response_noise0, 0, 1)
#
# par(mfrow = c(2,1))
# plot(jl.response_noise ~ lower(jl.response))
# plot(jl.response_noise0 ~ lower(jl.response))
# par(mfrow = c(1,1))
#
# par(mfrow = c(2,1))
# plot(resp1 ~ lower(jl.response))
# plot(resp2 ~ lower(jl.response))
# par(mfrow = c(1,1))
#
#
# n.obs <- jl.inputs$n.Pops
# genetic.mat <- matrix(0, n.obs, n.obs)
# genetic.mat[lower.tri(genetic.mat)] <- resp1
#
# graphics.off()
#
#
#
# # * gdist Prep -----------------------------------------------------------
#
# gdist.inputs <- gdist.prep(n.Pops = n_samplepoints,
# samples = Sample.coord)
#
# 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,
# gdist.inputs = gdist.inputs,
# GA.inputs = GA.inputs,
# out = NULL,
# rescale = TRUE)
#
# 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$x) - mn.cell
#
# #Cells buffered from edge
# habitat.rast <- which(rast.df$x > mn.cell &
# rast.df$x < mx.cell &
# rast.df$y > mn.cell &
# rast.df$y < mx.cell)
#
# sample.sites <- sample(habitat.rast)
# sample.xy <- rast.df[sample.sites, 1:3]
# sample.xy <- as.data.frame(sample.xy, xy = T)
#
# rast_min <- min(sample.xy$layer)
# rast_max <- max(sample.xy$layer)
#
# for(j in 1:nrow(sample.xy)){
# sample.xy$random[j] <- runif(1,rast_min, rast_max)
# col }
#
# sample.xy$keep <- ifelse(sample.xy$random >= sample.xy$layer, 1,0)
#
# sample.xy <- subset(sample.xy, keep == 1)
#
# sample.cells <- dplyr::sample_n(sample.xy, n_samplepoints)
# sample.cells <- sample.cells[,1:2]
# #sample.random <- rast.df[sample.cells]
# Sample.coord <- SpatialPoints(sample.cells)
#
#
# #plot(Resist, col=resistcols)
# #plot(Sample.coord, pch=15, col="black", add= TRUE)
#
#
# # Re-run gdistance
# gdist.inputs <- gdist.prep(n.Pops = n_samplepoints,
# samples = Sample.coord)
#
# #Pairwise resistance distacnes
# gdist.response <- Run_gdistance(gdist.inputs = gdist.inputs,
# r = Resist
# )
#
# gdist.response <- as.matrix(gdist.response)
#
# #Add noise
# ## The idea here is to have it be a relative error
# noise <- rnorm(length(lower(gdist.response)), mean = lower(gdist.response), sd = i * lower(gdist.response))
# noise0 <- rnorm(length(lower(gdist.response)), mean = 0, sd = i * mean(lower(gdist.response)))
#
# gd.response_noise <- lower(gdist.response) + noise
# gd.response_noise0 <- lower(gdist.response) + noise0
#
# resp1 <- ResistanceGA:::SCALE.vector(gd.response_noise, 0, 1)
# resp2 <- ResistanceGA:::SCALE.vector(gd.response_noise0, 0, 1)
#
# par(mfrow = c(2,1))
# plot(gd.response_noise ~ lower(gdist.response))
# plot(gd.response_noise0 ~ lower(gdist.response))
# par(mfrow = c(1,1))
#
# par(mfrow = c(2,1))
# plot(resp1 ~ lower(gdist.response))
# plot(resp2 ~ lower(cs.response))
# par(mfrow = c(1,1))
#
#
# n.obs <- cs.inputs$n.Pops
# genetic.mat <- matrix(0, n.obs, n.obs)
# genetic.mat[lower.tri(genetic.mat)] <- resp1
#
# graphics.off()
#
#
#
# * CS Prep -----------------------------------------------------------
cs.inputs <- CS.prep(n.Pops = n_samplepoints,
CS_Point.File = Sample.coord)
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,
CS.inputs = cs.inputs,
GA.inputs = GA.inputs,
out = NULL,
rescale = TRUE)
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$x) - mn.cell
#Cells buffered from edge
habitat.rast <- which(rast.df$x > mn.cell &
rast.df$x < mx.cell &
rast.df$y > mn.cell &
rast.df$y < mx.cell)
sample.sites <- sample(habitat.rast)
sample.xy <- rast.df[sample.sites, 1:3]
sample.xy <- as.data.frame(sample.xy, xy = T)
rast_min <- min(sample.xy$layer)
rast_max <- max(sample.xy$layer)
for(j in 1:nrow(sample.xy)){
sample.xy$random[j] <- runif(1,rast_min, rast_max)
col }
sample.xy$keep <- ifelse(sample.xy$random >= sample.xy$layer, 1,0)
sample.xy <- subset(sample.xy, keep == 1)
sample.cells <- dplyr::sample_n(sample.xy, n_samplepoints)
sample.cells <- sample.cells[,1:2]
#sample.random <- rast.df[sample.cells]
Sample.coord <- SpatialPoints(sample.cells)
# Re-run CS
cs.inputs <- CS.prep(n.Pops = n_samplepoints,
CS_Point.File = Sample.coord)
#Pairwise resistance distacnes
cs.response <- Run_CS(CS.inputs = cs.inputs,
r = Resist,
full.mat = T
)
#Add noise
## The idea here is to have it be a relative error
noise <- rnorm(length(lower(cs.response)), mean = lower(cs.response), sd = i * lower(cs.response))
noise0 <- rnorm(length(lower(cs.response)), mean = 0, sd = i * mean(lower(cs.response)))
cs.response_noise <- lower(cs.response) + noise
cs.response_noise0 <- lower(cs.response) + noise0
resp1 <- ResistanceGA:::SCALE.vector(cs.response_noise, 0, 1)
resp2 <- ResistanceGA:::SCALE.vector(cs.response_noise0, 0, 1)
par(mfrow = c(2,1))
plot(cs.response_noise ~ lower(cs.response))
plot(cs.response_noise0 ~ lower(cs.response))
par(mfrow = c(1,1))
par(mfrow = c(2,1))
plot(resp1 ~ lower(cs.response))
plot(resp2 ~ lower(cs.response))
par(mfrow = c(1,1))
n.obs <- cs.inputs$n.Pops
genetic.mat <- matrix(0, n.obs, n.obs)
genetic.mat[lower.tri(genetic.mat)] <- resp1
graphics.off()
# 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', 'A'),
maxiter = 40,
run = 8,
island.pop = 40,
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 = resp1,
# JULIA_HOME = JULIA_HOME)
# cs.inputs <- CS.prep(n.Pops = n_samplepoints,
# CS_Point.File = Sample.coord,
# response = resp1)
gdist.inputs <- gdist.prep(n.Pops = n_samplepoints,
samples = Sample.coord,
response = resp1)
Rga_out <- all_comb(gdist.inputs = gdist.inputs,
GA.inputs = GA.inputs,
# iters = 10,
results.dir = out
)
write.csv(genetic.mat,
file = paste0(out, "jl.response_noise.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)
} # end iteration loop (z)
} ## End SD loop
} # end function