inst/examples/estTransitionExamples.R
In SMBC-NZP/MigConnectivity: Estimate Migratory Connectivity for Migratory Animals
\donttest{
##############################################################################
# Examples 1 (banding data: first example is based on common tern banding
# data; the second is made up data to demonstrate data with two ages)
##############################################################################
COTE_banded <- c(10360, 1787, 2495, 336)
COTE_reencountered <- matrix(c(12, 0, 38, 15,
111, 7, 6, 2,
5, 0, 19, 4,
1123, 40, 41, 7),
4, 4,
dimnames = list(LETTERS[1:4], 1:4))
COTE_psi <- estTransition(originNames = LETTERS[1:4],
targetNames = 1:4,
banded = COTE_banded,
reencountered = COTE_reencountered,
verbose = 1,
nSamples = 60000, nBurnin = 20000,
method = "MCMC")
COTE_psi
COTE_banded2 <- matrix(rep(COTE_banded, 2), 4, 2)
COTE_reencountered2 <- array(c(12, 0, 38, 15, 6, 0, 17, 7,
111, 7, 6, 2, 55, 3, 3, 1,
5, 0, 19, 4, 2, 0, 10, 2,
1123, 40, 41, 7, 660, 20, 20, 3),
c(4, 2, 4),
dimnames = list(LETTERS[1:4], c("J", "A"), 1:4))
COTE_psi2 <- estTransition(originNames = LETTERS[1:4],
targetNames = 1:4,
banded = COTE_banded2,
reencountered = COTE_reencountered2,
verbose = 0,
nSamples = 60000, nBurnin = 20000,
method = "MCMC")
COTE_psi2
##############################################################################
# Example 2 (geolocator and telemetry ovenbirds captured on origin sites)
##############################################################################
data(OVENdata) # Ovenbird
nSamplesGLGPS <- 100 # Number of bootstrap iterations
# Estimate transition probabilities; treat all data as geolocator
GL_psi <- estTransition(isGL=TRUE,
geoBias = OVENdata$geo.bias,
geoVCov = OVENdata$geo.vcov,
targetSites = OVENdata$targetSites,
originSites = OVENdata$originSites,
originPoints = OVENdata$originPoints,
targetPoints = OVENdata$targetPoints,
verbose = 2,
nSamples = nSamplesGLGPS,
resampleProjection=sf::st_crs(OVENdata$targetPoints))
# Treat all data as is
Combined.psi <- estTransition(isGL=OVENdata$isGL,
isTelemetry = !OVENdata$isGL,
geoBias = OVENdata$geo.bias, # Light-level GL location bias
geoVCov = OVENdata$geo.vcov, # Location covariance matrix
targetSites = OVENdata$targetSites, # Nonbreeding/target sites
originSites = OVENdata$originSites, # Breeding/origin sites
originPoints = OVENdata$originPoints, # Capture Locations
targetPoints = OVENdata$targetPoints, #Device target locations
verbose = 2, # output options
nSamples = nSamplesGLGPS, # This is set low for example
resampleProjection = sf::st_crs(OVENdata$targetPoints))
print(Combined.psi)
# For treating all data as GPS,
# Move the latitude of birds with locations that fall offshore
int <- sf::st_intersects(OVENdata$targetPoints, OVENdata$targetSites)
any(lengths(int)<1)
plot(OVENdata$targetPoints)
plot(OVENdata$targetSites,add=TRUE)
tp<-sf::st_coordinates(OVENdata$targetPoints)
text(tp[,1], tp[,2], label=c(1:39))
tp[5,2] <- 2450000
tp[10,2]<- 2240496
tp[1,2]<- 2240496
tp[11,2]<- 2026511
tp[15,2]<- 2031268
tp[16,2]<- 2031268
oven_targetPoints<-sf::st_as_sf(as.data.frame(tp),
coords = c("X","Y"),
crs = sf::st_crs(OVENdata$targetPoints))
inter <- sf::st_intersects(oven_targetPoints, OVENdata$targetSites)
any(lengths(inter)<1)
plot(oven_targetPoints,add=TRUE, col = "green")
plot(oven_targetPoints[lengths(inter)<1,],add=TRUE, col = "darkblue")
# Treat all data as GPS
GPS_psi <- estTransition(isTelemetry = TRUE,
targetSites = OVENdata$targetSites, # Non-breeding/target sites
originSites = OVENdata$originSites, # Breeding/origin sites
originPoints = OVENdata$originPoints, # Capture Locations
targetPoints = oven_targetPoints, # Device target locations
verbose = 2, # output options
nSamples = nSamplesGLGPS) # This is set low for example
##############################################################################
# Example 3 (all released origin; some telemetry, some GL, some probability
# tables, some both GL and probability tables; data modified from ovenbird
# example)
##############################################################################
library(VGAM)
nAnimals <- 40
isGL <- c(OVENdata$isGL, FALSE)
isTelemetry <- c(!OVENdata$isGL, FALSE)
isRaster <- rep(FALSE, nAnimals)
isProb <- rep(FALSE, nAnimals)
targetPoints <- rbind(OVENdata$targetPoints, OVENdata$targetPoints[1,])
targetSites <- OVENdata$targetSites
originSites <- OVENdata$originSites
resampleProjection <- sf::st_crs(OVENdata$targetPoints)
targetNames <- OVENdata$targetNames
originNames <- OVENdata$originNames
targetAssignment <- array(0, dim = c(nAnimals, 3),
dimnames = list(NULL, targetNames))
assignment0 <- unclass(sf::st_intersects(x = targetPoints, y = targetSites,
sparse = TRUE))
assignment0[sapply(assignment0, function(x) length(x)==0)] <- 0
assignment0 <- array(unlist(assignment0), nAnimals)
for (ani in 1:nAnimals) {
if (assignment0[ani]>0)
targetAssignment[ani, assignment0[ani]] <- 1
else{
targetAssignment[ani, ] <- rdiric(1, c(15, 1, 1))
isProb[ani] <- TRUE
}
}
targetAssignment
isProb
nSamplesTry <- 100 # Number of bootstrap iterations
originPoints <- rbind(OVENdata$originPoints,
OVENdata$originPoints[39,])
system.time(psi3 <-
estTransition(isGL = isGL, isRaster = isRaster,
isProb = isProb,
isTelemetry = isTelemetry,
geoBias = OVENdata$geo.bias,
geoVCov = OVENdata$geo.vcov,
targetPoints = targetPoints,
targetAssignment = targetAssignment,
targetSites = targetSites,
resampleProjection = resampleProjection,
nSim = 20000, maxTries = 300,
originSites = originSites,
originPoints = originPoints,
captured = "origin",
originNames = OVENdata$originNames,
targetNames = OVENdata$targetNames,
verbose = 3,
nSamples = nSamplesTry))
psi3
nNonBreeding <- nrow(OVENdata$targetSites)
plot(psi3, legend = "top",
main = paste("OVENlike w/", sum(isGL & !isProb), "GL,",
sum(!isGL & isProb), "probs,",
sum(isGL & isProb), "both, and", sum(isTelemetry), "GPS"))
##############################################################################
# Example 4 (add probability animals released on other end)
##############################################################################
nAnimals <- 45
captured <- rep(c("origin", "target"), c(40, 5))
isGL <- c(OVENdata$isGL, rep(FALSE, 6))
isTelemetry <- c(!OVENdata$isGL, rep(FALSE, 6))
isRaster <- rep(FALSE, nAnimals)
isProb <- rep(FALSE, nAnimals)
targetPoints <- rbind(OVENdata$targetPoints,
OVENdata$targetPoints[c(1:3,19,23,31),])
targetAssignment <- array(0, dim = c(nAnimals, 3),
dimnames = list(NULL, targetNames))
assignment0 <- unclass(sf::st_intersects(x = targetPoints, y = targetSites,
sparse = TRUE))
assignment0[sapply(assignment0, function(x) length(x)==0)] <- 0
assignment0 <- array(unlist(assignment0), nAnimals)
for (ani in 1:nAnimals) {
if (assignment0[ani]>0)
targetAssignment[ani, assignment0[ani]] <- 1
else{
targetAssignment[ani, ] <- rdiric(1, c(15, 1, 1))
isProb[ani] <- TRUE
}
}
targetAssignment
isProb
originPoints <- rbind(OVENdata$originPoints,
OVENdata$originPoints[34:39,])
originPoints <- sf::st_transform(originPoints, crs = resampleProjection)
originSites <- sf::st_transform(OVENdata$originSites,
crs = resampleProjection)
assignment1 <- unclass(sf::st_intersects(x = originPoints, y = originSites,
sparse = TRUE))
assignment1[sapply(assignment1, function(x) length(x)==0)] <- 0
assignment1 <- array(unlist(assignment1), nAnimals)
nOriginSites <- nrow(originSites)
originAssignment <- array(0, dim = c(nAnimals, nOriginSites),
dimnames = list(NULL, originNames))
for (ani in 1:40) {
originAssignment[ani, assignment1[ani]] <- 1
}
for (ani in 41:nAnimals) {
originAssignment[ani, ] <- rdiric(1, c(1, 1))
isProb[ani] <- TRUE
}
originAssignment
isProb
system.time(psi4 <-
estTransition(isGL = isGL, isRaster = isRaster,
isProb = isProb,
isTelemetry = isTelemetry,
geoBias = OVENdata$geo.bias,
geoVCov = OVENdata$geo.vcov,
targetPoints = targetPoints,
targetAssignment = targetAssignment,
targetSites = targetSites,
resampleProjection = resampleProjection,
nSim = 15000, maxTries = 300,
originSites = originSites,
originAssignment = originAssignment,
captured = captured,
originNames = OVENdata$originNames,
targetNames = OVENdata$targetNames,
verbose = 2,
nSamples = nSamplesTry,
targetRelAbund = c(0.1432, 0.3577, 0.4991)))
psi4
plot(psi4, legend = "top",
main = paste(sum(isGL & !isProb), "GL,",
sum(!isGL & isProb & captured == "origin"), "prob.,",
sum(isGL & isProb), "both,",
sum(isTelemetry), "GPS (all\ncaptured origin), and",
sum(isProb & captured == "target"),
"prob. (captured target)"))
MC4 <- estStrength(OVENdata$originDist, OVENdata$targetDist,
OVENdata$originRelAbund, psi4,
sampleSize = nAnimals)
MC4
##############################################################################
# Example 5 (all raster, from our OVEN example)
##############################################################################
getCSV <- function(filename) {
tmp <- tempdir()
url1 <- paste0(
'https://github.com/SMBC-NZP/MigConnectivity/blob/master/data-raw/',
filename, '?raw=true')
temp <- paste(tmp, filename, sep = '/')
utils::download.file(url1, temp, mode = 'wb')
csv <- read.csv(temp)
unlink(temp)
return(csv)
}
getRDS <- function(speciesDist) {
tmp <- tempdir()
extension <- '.rds'
filename <- paste0(speciesDist, extension)
url1 <- paste0(
'https://github.com/SMBC-NZP/MigConnectivity/blob/master/data-raw/Spatial_Layers/',
filename, '?raw=true')
temp <- paste(tmp, filename, sep = '/')
utils::download.file(url1, temp, mode = 'wb')
shp <- readRDS(temp)
unlink(temp)
return(shp)
}
OVENdist <- getRDS("OVENdist")
OVENdist <- sf::st_as_sf(OVENdist)
OVENdist <- sf::st_transform(OVENdist, 4326)
OVENvals <- getCSV("deltaDvalues.csv")
OVENvals <- OVENvals[grep(x=OVENvals$Sample,"NH", invert = TRUE),]
originSites <- getRDS("originSites")
originSites <- sf::st_as_sf(originSites)
EVER <- length(grep(x=OVENvals$Sample,"EVER"))
JAM <- length(grep(x=OVENvals$Sample,"JAM"))
originRelAbund <- matrix(c(EVER,JAM),nrow = 1,byrow = TRUE)
originRelAbund <- prop.table(originRelAbund,1)
op <- sf::st_centroid(originSites)
originPoints <- array(NA,c(EVER+JAM,2), list(NULL, c("x","y")))
originPoints[grep(x = OVENvals$Sample,"JAM"),1] <- sf::st_coordinates(op)[1,1]
originPoints[grep(x = OVENvals$Sample,"JAM"),2] <- sf::st_coordinates(op)[1,2]
originPoints[grep(x = OVENvals$Sample,"EVER"),1]<-sf::st_coordinates(op)[2,1]
originPoints[grep(x = OVENvals$Sample,"EVER"),2]<-sf::st_coordinates(op)[2,2]
originPoints <- sf::st_as_sf(data.frame(originPoints),
coords = c("x", "y"),
crs = sf::st_crs(originSites))
iso <- isoAssign(isovalues = OVENvals[,2],
isoSTD = 12, # this value is for demonstration only
intercept = -10, # this value is for demonstration only
slope = 0.8, # this value is for demonstration only
odds = NULL,
restrict2Likely = FALSE,
nSamples = 1000,
sppShapefile = terra::vect(OVENdist),
assignExtent = c(-179,-60,15,89),
element = "Hydrogen",
period = "GrowingSeason",#this setting for demonstration only
seed = 12345,
verbose=1)
nAnimals <- dim(iso$probassign)[3]
isGL <-rep(FALSE, nAnimals); isRaster <- rep(TRUE, nAnimals)
isProb <- rep(FALSE, nAnimals); isTelemetry <- rep(FALSE, nAnimals)
targetSites <- sf::st_as_sf(iso$targetSites)
targetSites <- sf::st_make_valid(targetSites)
targetSites <- sf::st_union(targetSites, by_feature = TRUE)
system.time(psi5 <-
estTransition(isGL = isGL,
isRaster = isRaster,
isProb = isProb,
isTelemetry = isTelemetry,
targetSites = targetSites,
resampleProjection = resampleProjection,
targetRaster = iso,
originSites = originSites,
originPoints = originPoints,
captured = rep("origin", nAnimals),
verbose = 2,
nSamples = nSamplesTry))
psi5
}
SMBC-NZP/MigConnectivity documentation built on March 26, 2024, 4:22 p.m.
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\donttest{
##############################################################################
# Examples 1 (banding data: first example is based on common tern banding
# data; the second is made up data to demonstrate data with two ages)
##############################################################################
COTE_banded <- c(10360, 1787, 2495, 336)
COTE_reencountered <- matrix(c(12, 0, 38, 15,
111, 7, 6, 2,
5, 0, 19, 4,
1123, 40, 41, 7),
4, 4,
dimnames = list(LETTERS[1:4], 1:4))
COTE_psi <- estTransition(originNames = LETTERS[1:4],
targetNames = 1:4,
banded = COTE_banded,
reencountered = COTE_reencountered,
verbose = 1,
nSamples = 60000, nBurnin = 20000,
method = "MCMC")
COTE_psi
COTE_banded2 <- matrix(rep(COTE_banded, 2), 4, 2)
COTE_reencountered2 <- array(c(12, 0, 38, 15, 6, 0, 17, 7,
111, 7, 6, 2, 55, 3, 3, 1,
5, 0, 19, 4, 2, 0, 10, 2,
1123, 40, 41, 7, 660, 20, 20, 3),
c(4, 2, 4),
dimnames = list(LETTERS[1:4], c("J", "A"), 1:4))
COTE_psi2 <- estTransition(originNames = LETTERS[1:4],
targetNames = 1:4,
banded = COTE_banded2,
reencountered = COTE_reencountered2,
verbose = 0,
nSamples = 60000, nBurnin = 20000,
method = "MCMC")
COTE_psi2
##############################################################################
# Example 2 (geolocator and telemetry ovenbirds captured on origin sites)
##############################################################################
data(OVENdata) # Ovenbird
nSamplesGLGPS <- 100 # Number of bootstrap iterations
# Estimate transition probabilities; treat all data as geolocator
GL_psi <- estTransition(isGL=TRUE,
geoBias = OVENdata$geo.bias,
geoVCov = OVENdata$geo.vcov,
targetSites = OVENdata$targetSites,
originSites = OVENdata$originSites,
originPoints = OVENdata$originPoints,
targetPoints = OVENdata$targetPoints,
verbose = 2,
nSamples = nSamplesGLGPS,
resampleProjection=sf::st_crs(OVENdata$targetPoints))
# Treat all data as is
Combined.psi <- estTransition(isGL=OVENdata$isGL,
isTelemetry = !OVENdata$isGL,
geoBias = OVENdata$geo.bias, # Light-level GL location bias
geoVCov = OVENdata$geo.vcov, # Location covariance matrix
targetSites = OVENdata$targetSites, # Nonbreeding/target sites
originSites = OVENdata$originSites, # Breeding/origin sites
originPoints = OVENdata$originPoints, # Capture Locations
targetPoints = OVENdata$targetPoints, #Device target locations
verbose = 2, # output options
nSamples = nSamplesGLGPS, # This is set low for example
resampleProjection = sf::st_crs(OVENdata$targetPoints))
print(Combined.psi)
# For treating all data as GPS,
# Move the latitude of birds with locations that fall offshore
int <- sf::st_intersects(OVENdata$targetPoints, OVENdata$targetSites)
any(lengths(int)<1)
plot(OVENdata$targetPoints)
plot(OVENdata$targetSites,add=TRUE)
tp<-sf::st_coordinates(OVENdata$targetPoints)
text(tp[,1], tp[,2], label=c(1:39))
tp[5,2] <- 2450000
tp[10,2]<- 2240496
tp[1,2]<- 2240496
tp[11,2]<- 2026511
tp[15,2]<- 2031268
tp[16,2]<- 2031268
oven_targetPoints<-sf::st_as_sf(as.data.frame(tp),
coords = c("X","Y"),
crs = sf::st_crs(OVENdata$targetPoints))
inter <- sf::st_intersects(oven_targetPoints, OVENdata$targetSites)
any(lengths(inter)<1)
plot(oven_targetPoints,add=TRUE, col = "green")
plot(oven_targetPoints[lengths(inter)<1,],add=TRUE, col = "darkblue")
# Treat all data as GPS
GPS_psi <- estTransition(isTelemetry = TRUE,
targetSites = OVENdata$targetSites, # Non-breeding/target sites
originSites = OVENdata$originSites, # Breeding/origin sites
originPoints = OVENdata$originPoints, # Capture Locations
targetPoints = oven_targetPoints, # Device target locations
verbose = 2, # output options
nSamples = nSamplesGLGPS) # This is set low for example
##############################################################################
# Example 3 (all released origin; some telemetry, some GL, some probability
# tables, some both GL and probability tables; data modified from ovenbird
# example)
##############################################################################
library(VGAM)
nAnimals <- 40
isGL <- c(OVENdata$isGL, FALSE)
isTelemetry <- c(!OVENdata$isGL, FALSE)
isRaster <- rep(FALSE, nAnimals)
isProb <- rep(FALSE, nAnimals)
targetPoints <- rbind(OVENdata$targetPoints, OVENdata$targetPoints[1,])
targetSites <- OVENdata$targetSites
originSites <- OVENdata$originSites
resampleProjection <- sf::st_crs(OVENdata$targetPoints)
targetNames <- OVENdata$targetNames
originNames <- OVENdata$originNames
targetAssignment <- array(0, dim = c(nAnimals, 3),
dimnames = list(NULL, targetNames))
assignment0 <- unclass(sf::st_intersects(x = targetPoints, y = targetSites,
sparse = TRUE))
assignment0[sapply(assignment0, function(x) length(x)==0)] <- 0
assignment0 <- array(unlist(assignment0), nAnimals)
for (ani in 1:nAnimals) {
if (assignment0[ani]>0)
targetAssignment[ani, assignment0[ani]] <- 1
else{
targetAssignment[ani, ] <- rdiric(1, c(15, 1, 1))
isProb[ani] <- TRUE
}
}
targetAssignment
isProb
nSamplesTry <- 100 # Number of bootstrap iterations
originPoints <- rbind(OVENdata$originPoints,
OVENdata$originPoints[39,])
system.time(psi3 <-
estTransition(isGL = isGL, isRaster = isRaster,
isProb = isProb,
isTelemetry = isTelemetry,
geoBias = OVENdata$geo.bias,
geoVCov = OVENdata$geo.vcov,
targetPoints = targetPoints,
targetAssignment = targetAssignment,
targetSites = targetSites,
resampleProjection = resampleProjection,
nSim = 20000, maxTries = 300,
originSites = originSites,
originPoints = originPoints,
captured = "origin",
originNames = OVENdata$originNames,
targetNames = OVENdata$targetNames,
verbose = 3,
nSamples = nSamplesTry))
psi3
nNonBreeding <- nrow(OVENdata$targetSites)
plot(psi3, legend = "top",
main = paste("OVENlike w/", sum(isGL & !isProb), "GL,",
sum(!isGL & isProb), "probs,",
sum(isGL & isProb), "both, and", sum(isTelemetry), "GPS"))
##############################################################################
# Example 4 (add probability animals released on other end)
##############################################################################
nAnimals <- 45
captured <- rep(c("origin", "target"), c(40, 5))
isGL <- c(OVENdata$isGL, rep(FALSE, 6))
isTelemetry <- c(!OVENdata$isGL, rep(FALSE, 6))
isRaster <- rep(FALSE, nAnimals)
isProb <- rep(FALSE, nAnimals)
targetPoints <- rbind(OVENdata$targetPoints,
OVENdata$targetPoints[c(1:3,19,23,31),])
targetAssignment <- array(0, dim = c(nAnimals, 3),
dimnames = list(NULL, targetNames))
assignment0 <- unclass(sf::st_intersects(x = targetPoints, y = targetSites,
sparse = TRUE))
assignment0[sapply(assignment0, function(x) length(x)==0)] <- 0
assignment0 <- array(unlist(assignment0), nAnimals)
for (ani in 1:nAnimals) {
if (assignment0[ani]>0)
targetAssignment[ani, assignment0[ani]] <- 1
else{
targetAssignment[ani, ] <- rdiric(1, c(15, 1, 1))
isProb[ani] <- TRUE
}
}
targetAssignment
isProb
originPoints <- rbind(OVENdata$originPoints,
OVENdata$originPoints[34:39,])
originPoints <- sf::st_transform(originPoints, crs = resampleProjection)
originSites <- sf::st_transform(OVENdata$originSites,
crs = resampleProjection)
assignment1 <- unclass(sf::st_intersects(x = originPoints, y = originSites,
sparse = TRUE))
assignment1[sapply(assignment1, function(x) length(x)==0)] <- 0
assignment1 <- array(unlist(assignment1), nAnimals)
nOriginSites <- nrow(originSites)
originAssignment <- array(0, dim = c(nAnimals, nOriginSites),
dimnames = list(NULL, originNames))
for (ani in 1:40) {
originAssignment[ani, assignment1[ani]] <- 1
}
for (ani in 41:nAnimals) {
originAssignment[ani, ] <- rdiric(1, c(1, 1))
isProb[ani] <- TRUE
}
originAssignment
isProb
system.time(psi4 <-
estTransition(isGL = isGL, isRaster = isRaster,
isProb = isProb,
isTelemetry = isTelemetry,
geoBias = OVENdata$geo.bias,
geoVCov = OVENdata$geo.vcov,
targetPoints = targetPoints,
targetAssignment = targetAssignment,
targetSites = targetSites,
resampleProjection = resampleProjection,
nSim = 15000, maxTries = 300,
originSites = originSites,
originAssignment = originAssignment,
captured = captured,
originNames = OVENdata$originNames,
targetNames = OVENdata$targetNames,
verbose = 2,
nSamples = nSamplesTry,
targetRelAbund = c(0.1432, 0.3577, 0.4991)))
psi4
plot(psi4, legend = "top",
main = paste(sum(isGL & !isProb), "GL,",
sum(!isGL & isProb & captured == "origin"), "prob.,",
sum(isGL & isProb), "both,",
sum(isTelemetry), "GPS (all\ncaptured origin), and",
sum(isProb & captured == "target"),
"prob. (captured target)"))
MC4 <- estStrength(OVENdata$originDist, OVENdata$targetDist,
OVENdata$originRelAbund, psi4,
sampleSize = nAnimals)
MC4
##############################################################################
# Example 5 (all raster, from our OVEN example)
##############################################################################
getCSV <- function(filename) {
tmp <- tempdir()
url1 <- paste0(
'https://github.com/SMBC-NZP/MigConnectivity/blob/master/data-raw/',
filename, '?raw=true')
temp <- paste(tmp, filename, sep = '/')
utils::download.file(url1, temp, mode = 'wb')
csv <- read.csv(temp)
unlink(temp)
return(csv)
}
getRDS <- function(speciesDist) {
tmp <- tempdir()
extension <- '.rds'
filename <- paste0(speciesDist, extension)
url1 <- paste0(
'https://github.com/SMBC-NZP/MigConnectivity/blob/master/data-raw/Spatial_Layers/',
filename, '?raw=true')
temp <- paste(tmp, filename, sep = '/')
utils::download.file(url1, temp, mode = 'wb')
shp <- readRDS(temp)
unlink(temp)
return(shp)
}
OVENdist <- getRDS("OVENdist")
OVENdist <- sf::st_as_sf(OVENdist)
OVENdist <- sf::st_transform(OVENdist, 4326)
OVENvals <- getCSV("deltaDvalues.csv")
OVENvals <- OVENvals[grep(x=OVENvals$Sample,"NH", invert = TRUE),]
originSites <- getRDS("originSites")
originSites <- sf::st_as_sf(originSites)
EVER <- length(grep(x=OVENvals$Sample,"EVER"))
JAM <- length(grep(x=OVENvals$Sample,"JAM"))
originRelAbund <- matrix(c(EVER,JAM),nrow = 1,byrow = TRUE)
originRelAbund <- prop.table(originRelAbund,1)
op <- sf::st_centroid(originSites)
originPoints <- array(NA,c(EVER+JAM,2), list(NULL, c("x","y")))
originPoints[grep(x = OVENvals$Sample,"JAM"),1] <- sf::st_coordinates(op)[1,1]
originPoints[grep(x = OVENvals$Sample,"JAM"),2] <- sf::st_coordinates(op)[1,2]
originPoints[grep(x = OVENvals$Sample,"EVER"),1]<-sf::st_coordinates(op)[2,1]
originPoints[grep(x = OVENvals$Sample,"EVER"),2]<-sf::st_coordinates(op)[2,2]
originPoints <- sf::st_as_sf(data.frame(originPoints),
coords = c("x", "y"),
crs = sf::st_crs(originSites))
iso <- isoAssign(isovalues = OVENvals[,2],
isoSTD = 12, # this value is for demonstration only
intercept = -10, # this value is for demonstration only
slope = 0.8, # this value is for demonstration only
odds = NULL,
restrict2Likely = FALSE,
nSamples = 1000,
sppShapefile = terra::vect(OVENdist),
assignExtent = c(-179,-60,15,89),
element = "Hydrogen",
period = "GrowingSeason",#this setting for demonstration only
seed = 12345,
verbose=1)
nAnimals <- dim(iso$probassign)[3]
isGL <-rep(FALSE, nAnimals); isRaster <- rep(TRUE, nAnimals)
isProb <- rep(FALSE, nAnimals); isTelemetry <- rep(FALSE, nAnimals)
targetSites <- sf::st_as_sf(iso$targetSites)
targetSites <- sf::st_make_valid(targetSites)
targetSites <- sf::st_union(targetSites, by_feature = TRUE)
system.time(psi5 <-
estTransition(isGL = isGL,
isRaster = isRaster,
isProb = isProb,
isTelemetry = isTelemetry,
targetSites = targetSites,
resampleProjection = resampleProjection,
targetRaster = iso,
originSites = originSites,
originPoints = originPoints,
captured = rep("origin", nAnimals),
verbose = 2,
nSamples = nSamplesTry))
psi5
}
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