Nothing
R/simConnectivity.R
In MigConnectivity: Estimate Migratory Connectivity for Migratory Animals
Defines functions
simCMRData
simProbData
simTelemetryData
simGLData
simMove
Documented in
simCMRData
simGLData
simMove
simProbData
simTelemetryData
###############################################################################
# Simulates position of birds by individual, season, year, and month.
# Incorporates migratory connectivity, movement within season, and dispersal
# between seasons.
# Does not incorporate births or deaths.
###############################################################################
#' Simulates position of birds by individual, season, year, and month.
#'
#' Incorporates migratory connectivity, movement within season, and dispersal
#' between seasons. Does not incorporate births or deaths.
#'
#' @param breedingAbund Vector with number of birds to simulate starting at
#' each breeding site.
#' @param breedingDist Distances between the breeding sites. Symmetric matrix.
#' @param winteringDist Distances between the wintering sites. Symmetric
#' matrix.
#' @param psi Transition probabilities between B origin and W target sites.
#' A matrix with B rows and W columns where rows sum to 1.
#' @param nYears Number of years to simulate movement.
#' @param nMonths Number of months per breeding and wintering season.
#' @param winMoveRate Within winter movement rate. Defaults to 0 (no
#' movement).
#' @param sumMoveRate Within summer movement rate. Defaults to 0 (no
#' movement).
#' @param winDispRate Between winter dispersal rate. Defaults to 0 (no
#' dispersal).
#' @param sumDispRate Between summer dispersal rate. Defaults to 0 (no
#' dispersal). Setting this to a value above 0 is equivalent to setting
#' both natal and breeding dispersal to that same value.
#' @param natalDispRate Natal dispersal rate. Controls the movement of
#' animals from their birthplace on their first return to the breeding
#' grounds. Defaults to 0 (return to the birthplace for all).
#' @param breedDispRate Breeding dispersal rate. Controls the movement of
#' animals between breeding sites on spring migrations after the first.
#' Defaults to 0 (return to the same breeding site each year).
#' @param verbose If set to a value > 0, informs the user on the passage
#' of years and seasons during the simulation. Defaults to 0 (no output
#' during simulation).
#'
#' @return \code{simMove} returns a list with elements:
#' \describe{
#' \item{\code{animalLoc}}{\code{sum(breedingAbund)} (number of animals)
#' by 2 by \code{nYears} by \code{nMonths} array with the simulated
#' locations of each animal in each month of each season (summer or
#' winter) of each year. Values of cells are 1...B (first column) and
#' 1...W (second column) where B is the number of breeding sites and W is
#' the number of wintering sites.}
#' \item{\code{breedDispMat}}{B by B matrix of probabilities of breeding
#' dispersal between each pair of 1...B breeding sites. Direction is from
#' row to column, so each row sums to 1.}
#' \item{\code{natalDispMat}}{B by B matrix of probabilities of natal
#' dispersal between each pair of 1...B breeding sites. Direction is from
#' row to column, so each row sums to 1.}
#' \item{\code{sumMoveMat}}{B by B matrix of probabilities of within season
#' movement between each pair of 1...B breeding sites. Direction is from
#' row to column, so each row sums to 1.}
#' \item{\code{winDispMat}}{W by W matrix of probabilities of dispersal
#' between each pair of 1...W nonbreeding sites. Direction is from
#' row to column, so each row sums to 1.}
#' \item{\code{winMoveMat}}{W by W matrix of probabilities of within season
#' movement between each pair of 1...W nonbreeding sites. Direction is
#' from row to column, so each row sums to 1.}
#' }
#'
#' @export
#' @example inst/examples/simMoveExamples.R
#' @references
#' Cohen, E. B., J. A. Hostetler, M. T. Hallworth, C. S. Rushing, T. S. Sillett,
#' and P. P. Marra. 2018. Quantifying the strength of migratory connectivity.
#' Methods in Ecology and Evolution 9: 513-524.
#' \doi{10.1111/2041-210X.12916}
simMove <- function(breedingAbund, breedingDist, winteringDist, psi,
nYears = 10, nMonths = 3, winMoveRate = 0,
sumMoveRate = 0, winDispRate = 0, sumDispRate = 0,
natalDispRate = 0, breedDispRate = 0, verbose = 0)
{
nSeasons <- 2
nBreeding <- length(breedingAbund)
nWintering <- nrow(winteringDist)
if (sumDispRate>0 && (natalDispRate>0 || breedDispRate>0) &&
(sumDispRate!=natalDispRate || sumDispRate!=breedDispRate))
stop("Can't specify summer dispersal separately from breeding or natal dispersal")
if (sumDispRate<0 || natalDispRate<0 || breedDispRate<0 ||sumMoveRate<0 ||
winMoveRate<0)
stop("Can't specify negative movement or dispersal rates")
# Turn rate terms into probabilities
if (winMoveRate>0) {
winMoveMat <- mlogitMat(1/sqrt(winMoveRate), winteringDist)
}
else
winMoveMat <- NULL
if (sumMoveRate>0) {
sumMoveMat <- mlogitMat(1/sqrt(sumMoveRate), breedingDist)
}
else
sumMoveMat <- NULL
if (winDispRate>0) {
winDispMat <- mlogitMat(1/sqrt(winDispRate), winteringDist)
}
else
winDispMat <- NULL
if (sumDispRate>0) {
natalDispRate <- sumDispRate
breedDispRate <- sumDispRate
}
if (natalDispRate>0) {
natalDispMat <- mlogitMat(1/sqrt(natalDispRate), breedingDist)
}
else
natalDispMat <- NULL
if (breedDispRate>0) {
breedDispMat <- mlogitMat(1/sqrt(breedDispRate), breedingDist)
}
else
breedDispMat <- NULL
# Storage of locations
animalLoc <- array(NA, c(sum(breedingAbund), nSeasons, nYears, nMonths))
animalLoc[,1,1,1] <- rep(1:nBreeding, breedingAbund)
# Run simulation
for (y in 1:nYears) {
if (verbose>0)
cat("Year", y, "Summer, ")
if (nMonths > 1)
for (sm in 2:nMonths) {
if (sumMoveRate==0)
animalLoc[,1,y,sm] <- animalLoc[,1,y,sm-1]
else
for (i in 1:sum(breedingAbund))
animalLoc[i,1,y,sm] <- which(rmultinom(1,1, sumMoveMat[animalLoc[i,1,y,sm-1], ])>0)
}
if (verbose>0)
cat("Fall, ")
if (y == 1) {
for (i in 1:sum(breedingAbund))
animalLoc[i,2,y,1] <- which(rmultinom(1,1, psi[animalLoc[i,1,y,1], ])>0)
}
else if (winDispRate==0)
animalLoc[,2,y,1] <- animalLoc[,2,y-1,1]
else
for (i in 1:sum(breedingAbund))
animalLoc[i,2,y,1] <- which(rmultinom(1,1, winDispMat[animalLoc[i,2,y-1,1], ])>0)
if (verbose>0)
cat("Winter, ")
if (nMonths > 1)
for (wm in 2:nMonths) {
if (winMoveRate==0)
animalLoc[,2,y,wm] <- animalLoc[,2,y,wm-1]
else
for (i in 1:sum(breedingAbund))
animalLoc[i,2,y,wm] <- which(rmultinom(1,1, winMoveMat[animalLoc[i,2,y,wm-1], ])>0)
}
if (verbose>0)
cat("Spring\n")
if (y == 1 & nYears>1) {
if (natalDispRate==0)
animalLoc[,1,y+1,1] <- animalLoc[,1,y,1]
else
for (i in 1:sum(breedingAbund))
animalLoc[i,1,y+1,1] <- which(rmultinom(1,1, natalDispMat[animalLoc[i,1,y,1], ])>0)
}
else if (y < nYears) {
if (breedDispRate==0)
animalLoc[,1,y+1,1] <- animalLoc[,1,y,1]
else
for (i in 1:sum(breedingAbund))
animalLoc[i,1,y+1,1] <- which(rmultinom(1,1, breedDispMat[animalLoc[i,1,y,1], ])>0)
}
}
return(list(animalLoc = animalLoc, natalDispMat = natalDispMat,
breedDispMat = breedDispMat, sumMoveMat = sumMoveMat,
winDispMat = winDispMat, winMoveMat = winMoveMat))
}
###############################################################################
# Function for generating simulated count data
###############################################################################
#' Simulates Breeding Bird Survey-style count data
#'
#' Recently updated (version 0.4.3) to more properly match current BBS models.
#' \code{modelCountDataJAGS} has not been updated yet
#'
#' @param nStrata Number of populations/regions/strata
#' @param routesPerStrata Vector of length 1 or nStrata containing the number of
#' routes (i.e. counts) per stratum. If length(routesPerStrata) == 1, number of
#' routes is identical for each population
#' @param nYears Number of years surveys were conducted
#' @param alphaStrat Vector of length 1 or nStrata containing the log expected
#' number of individuals counted at each route for each population. If
#' length(alphaStrat) == 1, expected counts are identical for each population
#' @param beta Coefficient of linear year effect (default 0)
#' @param eta Coefficient of first time run effect (default 0)
#' @param sdRoute Standard deviation of random route-level variation. Default is
#' 0, and if you're setting sdObs, it's probably best to keep it that way
#' @param sdYear Standard deviation of random year-level variation (default 0)
#' @param sdObs Standard deviation of random observer variation (default 0)
#' @param sdCount Standard deviation of random count-level variation (default 0)
#' @param model One of "S" (default), "Sh", "D", and "Dh". See Link et al.
#' (2020) for descriptions of these models
#' @param obsSurvival Annual probability that the observer that ran a route one
#' year will run it the next. Default 1 (each route has only one observer)
#' @param fixedyear The year within nYears that alphaStrat applies directly to
#' (default halfway through)
#' @param nuCount For the "h" models, parameter for extra variation in counts
#' (default 1)
#'
#' @return \code{simCountData} returns a list containing:
#' \describe{
#' \item{\code{nStrata}}{Number of populations/regions.}
#' \item{\code{nRoutes}}{Total number of routes.}
#' \item{\code{nYears}}{Number of years.}
#' \item{\code{routesPerStrata}}{Number of routes per population.}
#' \item{\code{year}}{Vector of length nYears with standardized year values.}
#' \item{\code{strat}}{Vector of length nRoutes indicating the
#' population/region in which each route is located.}
#' \item{\code{alphaStrat}}{log expected count for each populations.}
#' \item{\code{epsRoute}}{realized deviation from alphaStrat for each route.}
#' \item{\code{epsYear}}{realized deviation from alphaStrat for each year.}
#' \item{\code{beta}}{linear year effect.}
#' \item{\code{sdRoute}}{standard deviation of random route-level variation.}
#' \item{\code{sdYear}}{standard deviation of random year-level variation.}
#' \item{\code{expectedCount}}{nRoutes by nYears matrix containing
#' deterministic expected counts.}
#' \item{\code{C}}{nRoutes by nYears matrix containing observed counts.}
#' }
#'
#'
#' @export
#' @example inst/examples/simCountExamples.R
#' @references
#' Cohen, E. B., J. A. Hostetler, M. T. Hallworth, C. S. Rushing, T. S. Sillett,
#' and P. P. Marra. 2018. Quantifying the strength of migratory connectivity.
#' Methods in Ecology and Evolution 9: 513-524.
#' \doi{10.1111/2041-210X.12916}
#'
#' Link, W. A., J. R. Sauer, and D. K. Niven. 2020. Model selection for the
#' North American Breeding Bird Survey. Ecological Applications 30: e02137.
#' \doi{10.1002/eap.2137}
simCountData <- function (nStrata, routesPerStrata, nYears, alphaStrat, beta = 0,
eta = 0, sdRoute = 0, sdYear = 0, sdObs = 0, sdCount = 0,
model = c("S", "Sh", "D", "Dh"), obsSurvival = 1,
fixedyear = round(nYears/2), nuCount = 1){
model <- match.arg(model)
if(length(routesPerStrata) == 1){
routesPerStrata <- rep(routesPerStrata, nStrata)
}
nRoutes <- sum(routesPerStrata)
strat <- rep(1:nStrata, routesPerStrata) # Population index for each route
if(length(alphaStrat) == 1) {
alphaStrat <- rep(alphaStrat, nStrata)
}
if(length(beta) == 1) {
beta <- rep(beta, nStrata)
}
if(length(sdRoute) == 1) {
sdRoute <- rep(sdRoute, nStrata)
}
if(length(sdYear) == 1) {
sdYear <- rep(sdYear, nStrata)
}
# Generate data structure to hold counts and log (lambda)
first <- obser <- C <- log.expectedCount <- array(0, dim = c(nYears, nRoutes))
expectedCount <- array(0, dim = c(nYears, nRoutes))
epsYear <- array(0, dim = c(nYears, nStrata))
# Generate covariate values
year <- 1:nYears
yr <- year - fixedyear # Standardize
# Generate sequence of observers
nObs <- 0
for (r in 1:nRoutes) {
nObs <- nObs + 1
obser[1, r] <- nObs
first[1, r] <- 1
for (y in 2:nYears) {
if (rbinom(1, 1, obsSurvival)==0){
nObs <- nObs + 1
first[y, r] <- 1
}
obser[y, r] <- nObs
}
}
# Draw two sets of random effects from their respective distributions
epsObs <- rnorm(n = nObs, mean = 0, sd = sdObs)
epsCount <- rnorm(n = nRoutes * nYears, mean = 0, sd = sdCount)
if (model == "Sh" || model == "Dh"){
V <- rgamma(n = nRoutes * nYears, shape = nuCount/2, rate = nuCount/2)
epsCount <- epsCount / sqrt(V)
}
epsRoute <- vector("list", nStrata)
place <- 0
for (s in 1:nStrata) {
epsRoute[[s]] <- rnorm(n = routesPerStrata[s], mean = 0, sd = sdRoute[s])
if (model %in% c("S", "Sh"))
epsYear[ , s] <- rnorm(n = nYears, mean = 0, sd = sdYear[s])
else {
for (y in ((fixedyear-1):1))
epsYear[y, s] <- rnorm(n = 1, mean = epsYear[y+1, s], sd = sdYear[s])
for (y in ((fixedyear+1):nYears))
epsYear[y, s] <- rnorm(n = 1, mean = epsYear[y-1, s], sd = sdYear[s])
}
}
# Loop over routes
for (i in 1:nRoutes){
if (model %in% c("S", "Sh"))
# Build up systematic part of the GLM including random effects
log.expectedCount[,i] <- alphaStrat[strat[i]] + beta[strat[i]]*yr +
epsRoute[[strat[i]]][i] + epsYear[ , strat[i]] + epsCount[(i-1)*nYears + 1:nYears] +
epsObs[obser[ , i]] + eta * first[ , i]
else { # model D or Dh, ignore beta
log.expectedCount[,i] <- alphaStrat[strat[i]] +
epsRoute[[strat[i]]][i] + epsYear[ , strat[i]] + epsCount[(i-1)*nYears + 1:nYears] +
epsObs[obser[ , i]] + eta * first[ , i]
}
expectedCount[ , i] <- exp(log.expectedCount[,i])
C[,i] <- rpois(n = nYears, lambda = expectedCount)
}
return(list(C = C, strat = strat, obser = obser, first = first,
epsRoute = epsRoute, epsYear = epsYear, epsObs = epsObs,
epsCount = epsCount, expectedCount = expectedCount,
input = list(nStrata = nStrata, nRoutes = nRoutes, nYears = nYears,
routesPerStrata = routesPerStrata, year = yr,
alphaStrat = alphaStrat, beta = beta,
sdRoute = sdRoute, sdYear = sdYear, sdObs = sdObs,
sdCount = sdCount)))
}
###############################################################################
# Estimates population-level relative abundance from count data
###############################################################################
#' Estimates population-level relative abundance from count data
#'
#' Uses a Bayesian hierarchical model to estimate relative abundance of regional
#' populations from count-based data (e.g., Breeding Bird Survey)
#'
#' @param count_data List containing the following elements:
#' ' \describe{
#' \item{\code{C}}{nYears by nRoutes matrix containing the observed number of individuals counted at each route in each year.}
#' \item{\code{strat}}{Vector of length nRoutes indicating the population/region in which each route is located.}
#' \item{\code{routesPerStrata}}{Vector of length 1 or nStrata containing the number of routes (i.e. counts) per population. If length(routesPerStrata) == 1, number of routes is identical for each population.}
#' }
#' @param ni Number of MCMC iterations. Default = 20000.
#' @param nt Thinning rate. Default = 5.
#' @param nb Number of MCMC iterations to discard as burn-in. Default = 5000.
#' @param nc Number of chains. Default = 3.
#'
#' @return \code{modelCountDataJAGS} returns an mcmc object containing posterior samples for each monitored parameter.
#
#'
#' @export
#' @example inst/examples/simCountExamples.R
#' @references
#' Cohen, E. B., J. A. Hostetler, M. T. Hallworth, C. S. Rushing, T. S. Sillett,
#' and P. P. Marra. 2018. Quantifying the strength of migratory connectivity.
#' Methods in Ecology and Evolution 9: 513-524.
#' \doi{10.1111/2041-210X.12916}
#'
#' Link, W. A. and J. R. Sauer. 2002. A hierarchical analysis of population
#' change with application to Cerulean Warblers. Ecology 83: 2832-2840.
#' \doi{10.1890/0012-9658(2002)083[2832:AHAOPC]2.0.CO;2}
#'
modelCountDataJAGS <- function (count_data, ni = 20000, nt = 5, nb = 5000, nc = 3) {
nPops <- length(unique(count_data$strat))
nRoutes <- dim(count_data$C)[2]
nYears = dim(count_data$C)[1]
if(length(count_data$routesPerStrata) == 1){
routesPerStrata = rep(count_data$input$routesPerStrata, nPops)
} else {
routesPerStrata = count_data$input$routesPerStrata
}
# Initial values
jags.inits <- function()list(mu = runif(1,0,2), alpha = runif(nPops, -1,1), beta1 = runif(1,-1,1),
tau.alpha = runif(1,0,0.1), tau.noise = runif(1,0,0.1),
tau.rte = runif(1,0,0.1), route = runif(nRoutes,-1,1))
# Parameters to monitor
params <- c("mu", "alpha", "beta1", "sd.alpha", "sd.rte", "sd.noise", "totalN", "popN", "relN")
# Data
jags.data <- list(C = count_data$C, nPops = length(unique(count_data$strat)), nRoutes = nRoutes,
routePerPop = routesPerStrata,
year = seq(from = 0, to = 1, length.out = nYears), nYears = nYears, pop = count_data$strat)
file <- tempfile(fileext = ".txt")
sink(file = file)
cat("
model{
# Priors
for(p in 1:nPops){
alpha[p] ~ dnorm(mu, tau.alpha)
}
mu ~ dnorm(0, 0.01)
tau.alpha ~ dgamma(0.001,0.001)
sd.alpha <- 1/pow(tau.alpha, 0.5)
for(r in 1:nRoutes){
route[r] ~ dnorm(0, tau.rte)
}
tau.rte ~ dgamma(0.001, 0.001)
sd.rte <- 1/pow(tau.rte, 0.5)
tau.noise ~ dgamma(0.001, 0.001)
sd.noise <- 1/pow(tau.noise,0.5)
beta1 ~ dnorm(0, 0.01)
# Likelihood
for(i in 1:nYears){
eps[i] ~ dnorm(0, tau.noise)
for(j in 1:nRoutes){
C[i,j] ~ dpois(lambda[i,j])
log(lambda[i,j]) <- alpha[pop[j]] + beta1*year[i] + route[j] + eps[i]
}
}
# Derived parameters
totalN <- sum(popN[1:nPops])
for(i in 1:nPops){
popN[i] <- exp(alpha[i] + beta1*nYears + eps[nYears])*routePerPop[i]
relN[i] <- popN[i]/totalN
}
}")
sink()
out <- R2jags::jags(data = jags.data, inits = jags.inits, params,
file,
n.chains = nc, n.thin = nt, n.iter = ni, n.burnin = nb,
progress.bar = 'none')
file.remove(file)
return(coda::as.mcmc(out))
}
#' Simulate geolocator (GL) migratory movement data
#'
#' @param psi Transition probabilities between B origin and W target sites.
#' A matrix with B rows and W columns where rows sum to 1.
#' @param originRelAbund Relative abundances at B origin sites. Numeric vector
#' of length B that sums to 1.
#' @param sampleSize List of length two. The first element is either a vector of
#' length B with the number of simulated animals to release with geolocators
#' at each of the B origin sites, a single integer with the total number of
#' simulated animals to release with geolocators at origin sites (in which
#' case, the origin sites will be sampled according to the relative
#' abundance), or NULL if all animals are released at target sites. The
#' second element is either a vector of length W with the number of simulated
#' animals to release with geolocators at each of the W target sites, a
#' single integer with the total number of simulated animals to release with
#' geolocators at target sites (in which case, the target sites will be
#' sampled according to their relative abundance), or NULL if all animals are
#' released at origin sites.
#' @param originSites A polygon spatial layer (sf - MULTIPOLYGON) defining the
#' geographic representation of sites in the origin season.
#' @param targetSites A polygon spatial layer (sf - MULTIPOLYGON) defining the
#' geographic representation of sites in the target season.
# @param captured
#' @param geoBias Vector of length 2 indicating expected bias in longitude and
#' latitude of animals captured and released at origin sites, in
#' \code{targetSites} units.
#' @param geoVCov 2x2 matrix with expected variance/covariance in longitude and
#' latitude of animals captured and released at origin sites, in
#' \code{targetSites} units.
#' @param geoBiasOrigin Vector of length 2 indicating expected bias in longitude
#' and latitude of animals captured and released at target sites, in
#' \code{originSites} units.
#' @param geoVCovOrigin 2x2 matrix with expected variance/covariance in
#' longitude and latitude of animals captured and released at target sites,
#' in \code{originSites} units.
#' @param S Survival probabilities of released geolocator animals. Either a
#' matrix with B rows and W columns (if survival depends on both origin site
#' and target site), a vector of length W (if survival depends only on target
#' site), or a single number (if survival is the same for all animals).
#' Default 1 (all animals with geolocators survive a year).
#' @param p Recapture probabilities of released geolocator animals; list of
#' length two. The first element is either a vector of length B (if recapture
#' depends on origin site), or a single number (if recapture is the same for
#' all animals released on origin sites). The second element is either a
#' vector of length W (if recapture depends on target site), or a single
#' number (if recapture is the same for all animals released on target
#' sites). Default list(1, 1) (all animals that survive are recaptured).
#' @param requireEveryOrigin If TRUE, the function will throw an error if it
#' looks like at least one origin site has no animals released in or
#' migrating to it, or if it can, keep simulating until representation is
#' met. This helps estTransition or estMC not throw an error. Default FALSE.
#' @return \code{simGLData} returns a list with the elements:
#' \describe{
#' \item{\code{originAssignment}}{Vector with true origin site of each animal}
#' \item{\code{targetAssignment}}{Vector with true target site of each animal}
#' \item{\code{originPointsTrue}}{True origin location of each animal, type sf,
#' same projection as originSites}
#' \item{\code{targetPointsTrue}}{True target location of each animal, type sf,
#' same projection as targetSites}
#' \item{\code{originPointsObs}}{Observed origin location of each animal that
#' survived and was recaptured, type sf, same projection as originSites. Same
#' as originPointsTrue for animals captured at origin sites when S and p==1}
#' \item{\code{targetPointsObs}}{Observed target location of each animal that
#' survived and was recaptured, type sf, same projection as targetSites. Same
#' as targetPointsTrue for animals captured at target sites when S and p==1}
#' \item{\code{lived}}{0/1 vector for each animal, indicating which survived}
#' \item{\code{recaptured}}{0/1 vector for each animal, indicating which were
#' recaptured}
#' \item{\code{input}}{List containing the inputs to function}
#' }
#' @export
#'
# @examples
simGLData <- function(psi, originRelAbund = NULL, sampleSize,
originSites = NULL, targetSites = NULL,
#captured = "origin",
geoBias = NULL, geoVCov = NULL,
geoBiasOrigin = geoBias, geoVCovOrigin = geoVCov,
S = 1, p = list(1, 1),
requireEveryOrigin = FALSE) {
nOriginSites <- nrow(psi)
nTargetSites <- ncol(psi)
if (!is.null(originRelAbund)) {
rev <- reversePsiRelAbund(psi, originRelAbund)
gamma <- rev$gamma
targetRelAbund <- rev$targetRelAbund
}
else if (!is.null(sampleSize[[2]]) || length(sampleSize[[1]]) < 2)
stop("Need to enter in originRelAbund for target data or for single origin sample size")
if (length(dim(S))<2) {
S <- matrix(S, nOriginSites, nTargetSites, byrow = TRUE)
}
if (length(p[[1]]==1))
p[[1]] <- rep(p[[1]], nOriginSites)
if (length(p[[2]]==1))
p[[2]] <- rep(p[[2]], nTargetSites)
nAnimals <- sum(sampleSize[[1]]) + sum(sampleSize[[2]])
captured <- rep(c("origin", "target"),
c(sum(sampleSize[[1]]), sum(sampleSize[[2]])))
# if (length(captured)==1)
# captured <- rep(captured, nAnimals)
targetAssignment <- rep(NA, nAnimals)
originAssignment <- rep(NA, nAnimals)
lived <- rep(1, nAnimals)
recaptured <- rep(1, nAnimals)
if (length(sampleSize[[1]])>1){
originAssignment[captured=="origin"] <- rep(1:nOriginSites, sampleSize[[1]])
if (requireEveryOrigin && is.null(sampleSize[[2]])){
if (any(sampleSize[[1]] < 1))
stop("Some origin site or sites have no samples ", sampleSize[[1]])
}
}
if (length(sampleSize[[2]])>1){
targetAssignment[captured=="target"] <- rep(1:nTargetSites, sampleSize[[2]])
}
runWell <- FALSE
while (!runWell){
for (a in 1:nAnimals) {
if (captured[a]=="origin") {
if (is.na(originAssignment[a]))
originAssignment[a] <- sample.int(nOriginSites, 1, prob = originRelAbund)
targetAssignment[a] <- sample.int(nTargetSites, 1, prob = psi[originAssignment[a], ])
lived[a] <- rbinom(1, 1, S[originAssignment[a], targetAssignment[a]])
recaptured[a] <- rbinom(1, 1, lived[a] * p[[1]][originAssignment[a]])
}
else {
if (is.na(targetAssignment[a]))
targetAssignment[a] <- sample.int(nTargetSites, 1, prob = targetRelAbund)
originAssignment[a] <- sample.int(nOriginSites, 1, prob = gamma[targetAssignment[a], ])
lived[a] <- rbinom(1, 1, S[originAssignment[a], targetAssignment[a]])
recaptured[a] <- rbinom(1, 1, lived[a] * p[[2]][targetAssignment[a]])
}
}
oa1 <- sort(unique(originAssignment))
runWell <- !requireEveryOrigin || any(captured=="target") ||
isTRUE(all.equal(oa1, 1:nOriginSites))
}
oaTest <- list(1:2); taTest <- list(2:3)
while (any(lengths(oaTest)>1) || any(lengths(taTest)>1)) {
originPointsTrue <- randomPoints(originSites, originAssignment)
targetPointsTrue <- randomPoints(targetSites, targetAssignment)
originPointsObs <- array(NA, c(sum(recaptured), 2),
dimnames = list(NULL, c("x","y")))
targetPointsObs <- array(NA, c(sum(recaptured), 2),
dimnames = list(NULL, c("x","y")))
linkerOrigin <- which(recaptured == 1) %in%
which(captured=="origin" & recaptured == 1)
linkerTarget <- which(recaptured == 1) %in%
which(captured=="target" & recaptured == 1)
if (any(captured=="origin" & recaptured == 1)) {
geoBias2 <- array(rep(geoBias, sum(captured=="origin" & recaptured == 1)),
c(2, sum(captured=="origin" & recaptured == 1)))
targetPointsObs[linkerOrigin, ] <-
t(array(apply(sf::st_coordinates(targetPointsTrue)[captured=="origin" & recaptured == 1, , drop = FALSE], 1,
MASS::mvrnorm, n=1, Sigma=geoVCov),
c(2, sum(captured=="origin" & recaptured == 1))) + geoBias2)
originPointsObs[linkerOrigin, ] <-
sf::st_coordinates(originPointsTrue)[captured=="origin" & recaptured == 1, ,
drop = FALSE]
}
if (any(captured == "target" & recaptured == 1)) {
geoBias2 <- array(rep(geoBiasOrigin, sum(captured == "target" & recaptured == 1)),
c(2, sum(captured == "target" & recaptured == 1)))
originPointsObs[linkerTarget, ] <-
t(array(apply(sf::st_coordinates(originPointsTrue)[captured == "target" & recaptured == 1, , drop = FALSE], 1,
MASS::mvrnorm, n=1, Sigma=geoVCovOrigin),
c(2, sum(captured == "target" & recaptured == 1))) + geoBias2)
targetPointsObs[linkerTarget, ] <-
sf::st_coordinates(targetPointsTrue)[captured=="target" & recaptured == 1, ,
drop = FALSE]
}
targetPointsObs <- sf::st_as_sf(data.frame(targetPointsObs),
coords = c("x","y"),
crs = sf::st_crs(targetSites))
originPointsObs <- sf::st_as_sf(data.frame(originPointsObs),
coords = c("x","y"),
crs = sf::st_crs(originSites))
oaTest <- suppressMessages(unclass(sf::st_intersects(x = originPointsObs,
y = originSites,
sparse = TRUE)))
taTest <- suppressMessages(unclass(sf::st_intersects(x = targetPointsObs,
y = targetSites,
sparse = TRUE)))
}
return(list(originAssignment = originAssignment,
targetAssignment = targetAssignment,
originPointsTrue = originPointsTrue,
targetPointsTrue = targetPointsTrue,
originPointsObs = originPointsObs,
targetPointsObs = targetPointsObs,
lived = lived,
recaptured = recaptured,
input = list(psi = psi, originRelAbund = originRelAbund,
originSites = originSites,
targetSites = targetSites,
captured = captured,
geoBias = geoBias, geoVCov = geoVCov,
geoBiasOrigin = geoBiasOrigin,
geoVCovOrigin = geoVCovOrigin,
S = S, p = p)))
}
#' Simulate telemetry/GPS data
#'
#' @param psi Transition probabilities between B origin and W target sites.
#' A matrix with B rows and W columns where rows sum to 1.
#' @param sampleSize If captured is "origin", either a vector of
#' length B with the number of simulated animals to release with geolocators
#' at each of the B origin sites or a single integer with the total number of
#' simulated animals to release with GPS at origin sites (in which
#' case, the origin sites will be sampled according to the relative
#' abundance). If captured is "target", either a vector of length W with the
#' number of simulated animals to release with GPS at each of the W target
#' sites or a single integer with the total number of simulated animals to
#' release at target sites (in which case, the target sites will be
#' sampled according to their relative abundance).
#' @param originRelAbund Relative abundances at B origin sites. Numeric vector
#' of length B that sums to 1. Optional unless providing target data and/or
#' sample size of length 1.
#' @param originSites A polygon spatial layer (sf - MULTIPOLYGON) defining the
#' geographic representation of sites in the origin season. If left NULL, the
#' simulation won't provide origin points.
#' @param targetSites A polygon spatial layer (sf - MULTIPOLYGON) defining the
#' geographic representation of sites in the target season. If left NULL, the
#' simulation won't provide target points.
#' @param captured Either "origin" (the default) or "target".
#' @param S Survival probabilities of released animals. Probably only
#' relevant for simulating archival tags. Either a matrix with B rows and W
#' columns (if survival depends on both origin site and target site), a vector
#' of length W (if survival depends only on target site), or a single number
#' (if survival is the same for all animals). Default 1 (all tagged animals
#' survive a year).
#' @param p Recapture probabilities of released animals. Only relevant for
#' simulating archival tags. Either a vector of length B (if captured on origin
#' and recapture depends on origin site), a vector of length W (if captured on
#' target and recapture depends on target site), or a single number (if
#' recapture is the same for all animals). Default 1 (all animals that survive
#' are recaptured).
#' @param requireEveryOrigin If TRUE, the function will throw an error if it
#' looks like at least one origin site has no animals released in or
#' migrating to it, or if it can, keep simulating until representation is
#' met. This helps estTransition not throw an error. Default FALSE.
#' @return \code{simTelemetryData} returns a list with the elements:
#' \describe{
#' \item{\code{originAssignment}}{Vector with true origin site of each animal}
#' \item{\code{targetAssignment}}{Vector with true target site of each animal}
#' \item{\code{originPointsTrue}}{True origin location of each animal, type sf,
#' same projection as originSites}
#' \item{\code{targetPointsTrue}}{True target location of each animal, type sf,
#' same projection as targetSites}
#' \item{\code{originPointsObs}}{Observed origin location of each animal that
#' survived and was recaptured, type sf, same projection as originSites. Same
#' as originPointsTrue when S and p==1}
#' \item{\code{targetPointsObs}}{Observed target location of each animal that
#' survived and was recaptured, type sf, same projection as targetSites. Same
#' as targetPointsTrue when S and p==1}
#' \item{\code{lived}}{0/1 vector for each animal, indicating which survived}
#' \item{\code{recaptured}}{0/1 vector for each animal, indicating which were
#' recaptured}
#' \item{\code{input}}{List containing the inputs to function}
#' }
#' @export
#'
# @examples
simTelemetryData <- function(psi, sampleSize, originRelAbund = NULL,
originSites = NULL, targetSites = NULL,
captured = "origin",
S = 1, p = 1,
requireEveryOrigin = FALSE) {
nOriginSites <- nrow(psi)
nTargetSites <- ncol(psi)
if (!is.null(originRelAbund)) {
rev <- reversePsiRelAbund(psi, originRelAbund)
gamma <- rev$gamma
targetRelAbund <- rev$targetRelAbund
}
else if (captured != "origin" || length(sampleSize) < 2)
stop("Need to enter in originRelAbund for target data or for single sample size")
if (length(dim(S))<2) {
S <- matrix(S, nOriginSites, nTargetSites, byrow = TRUE)
}
if (length(p==1)){
if (captured=="origin")
p <- rep(p, nOriginSites)
else
p <- rep(p, nTargetSites)
}
nAnimals <- sum(sampleSize)
targetAssignment <- rep(NA, nAnimals)
originAssignment <- rep(NA, nAnimals)
lived <- rep(1, nAnimals)
recaptured <- rep(1, nAnimals)
if (length(sampleSize)>1 && captured=="origin"){
originAssignment <- rep(1:nOriginSites, sampleSize)
if (requireEveryOrigin){
if (any(sampleSize < 1))
stop("Some origin site or sites have no samples ", sampleSize)
}
}
else if (length(sampleSize)>1 && captured=="origin"){
targetAssignment <- rep(1:nTargetSites, sampleSize)
}
runWell <- FALSE
while (!runWell){
for (a in 1:nAnimals) {
if (captured=="origin") {
if (is.na(originAssignment[a]))
originAssignment[a] <- sample.int(nOriginSites, 1, prob = originRelAbund)
targetAssignment[a] <- sample.int(nTargetSites, 1, prob = psi[originAssignment[a], ])
lived[a] <- rbinom(1, 1, S[originAssignment[a], targetAssignment[a]])
recaptured[a] <- rbinom(1, 1, lived[a] * p[originAssignment[a]])
}
else {
if (is.na(targetAssignment[a]))
targetAssignment[a] <- sample.int(nTargetSites, 1, prob = targetRelAbund)
originAssignment[a] <- sample.int(nOriginSites, 1, prob = gamma[targetAssignment[a], ])
lived[a] <- rbinom(1, 1, S[originAssignment[a], targetAssignment[a]])
recaptured[a] <- rbinom(1, 1, lived[a] * p[targetAssignment[a]])
}
}
oa1 <- sort(unique(originAssignment))
runWell <- !requireEveryOrigin || (captured=="target") ||
isTRUE(all.equal(oa1, 1:nOriginSites))
}
if (!is.null(originSites))
originPointsTrue <- randomPoints(originSites, originAssignment)
else
originPointsTrue <- NULL
if (!is.null(originSites))
targetPointsTrue <- randomPoints(targetSites, targetAssignment)
else
targetPointsTrue <- NULL
return(list(originAssignment = originAssignment,
targetAssignment = targetAssignment,
originPointsTrue = originPointsTrue,
targetPointsTrue = targetPointsTrue,
originPointsObs = originPointsTrue[recaptured==1, ],
targetPointsObs = targetPointsTrue[recaptured==1, ],
lived = lived,
recaptured = recaptured,
input = list(psi = psi, originRelAbund = originRelAbund,
originSites = originSites,
targetSites = targetSites,
captured = captured,
S = S, p = p)))
}
#' Simulate Dirichlet-based probability table data
#'
#' @param psi Transition probabilities between B origin sites and W target
#' sites. B by W matrix
#' @param originRelAbund Vector of relative abundances at B origin sites
#' @param sampleSize Either the total number of data points to simulate or a
#' vector with the number at each target or origin site. If only the total is
#' provided, sampling will be done in proportion to abundance
#' @param shapes If captured == "target", a B by B matrix, each row of which is
#' the shape parameters for the Dirichlet distribution of an animal whose true
#' origin assignment is that row's. If captured == "origin", a W by W matrix,
#' each row of which is the shape parameters for the Dirichlet distribution of
#' an animal whose true target assignment is that row's.
#' @param captured Either "target" (the default) or "origin", indicating which
#' side animal data were collected on
#' @param requireEveryOrigin If TRUE, the function will throw an error if it
#' looks like at least one origin site has no animals released in or
#' migrating to it, or if it can, keep simulating until representation is
#' met. This helps estTransition or estMC not throw an error. Default FALSE
#'
#' @return \code{simProbData} returns a list with the elements:
#' \describe{
#' \item{\code{originAssignment}}{Vector with true origin site of each animal}
#' \item{\code{targetAssignment}}{Vector with true target site of each animal}
#' \item{\code{genProbs}}{Table of assignment site probabilities for each
#' animal}
#' \item{\code{input}}{List containing the inputs to function}
#' }
#' @export
#'
# @examples
simProbData <- function(psi,
originRelAbund,
sampleSize,
shapes,
captured = "target",
requireEveryOrigin = FALSE) {
nOriginSites <- nrow(psi)
nTargetSites <- ncol(psi)
rev <- reversePsiRelAbund(psi, originRelAbund)
gamma <- rev$gamma
targetRelAbund <- rev$targetRelAbund
nAnimals <- sum(sampleSize)
if (length(captured)>1)
captured <- captured[1]
targetAssignment <- rep(NA, nAnimals)
originAssignment <- rep(NA, nAnimals)
if (captured=="origin" && length(sampleSize)>1){
originAssignment <- rep(1:nOriginSites, sampleSize)
if (requireEveryOrigin){
if (any(sampleSize < 1))
stop("Some origin site or sites have no samples ", sampleSize)
}
}
if (captured=="target" && length(sampleSize)>1){
targetAssignment <- rep(1:nTargetSites, sampleSize)
}
runWell <- FALSE
while (!runWell){
for (a in 1:nAnimals) {
if (captured=="origin") {
if (is.na(originAssignment[a]))
originAssignment[a] <- sample.int(nOriginSites, 1, prob = originRelAbund)
targetAssignment[a] <- sample.int(nTargetSites, 1, prob = psi[originAssignment[a], ])
}
else {
if (is.na(targetAssignment[a]))
targetAssignment[a] <- sample.int(nTargetSites, 1, prob = targetRelAbund)
originAssignment[a] <- sample.int(nOriginSites, 1, prob = gamma[targetAssignment[a], ])
}
}
runWell <- !requireEveryOrigin || captured=="target" ||
all.equal(unique(originAssignment), 1:nOriginSites)
}
if (captured == "target") {
genProbs <- array(NA, c(nAnimals, nOriginSites))
for (a in 1:nAnimals) {
genProbs[a, ] <- VGAM::rdiric(1, shapes[originAssignment[a], ])
}
}
else {
genProbs <- array(NA, c(nAnimals, nTargetSites))
for (a in 1:nAnimals) {
genProbs[a, ] <- VGAM::rdiric(1, shapes[targetAssignment[a], ])
}
}
return(list(originAssignment = originAssignment,
targetAssignment = targetAssignment,
genProbs = genProbs,
input = list(psi = psi,
originRelAbund = originRelAbund,
captured = captured,
shapes = shapes)))
}
#' Simulate capture-mark-reencounter (CMR) migratory movement data
#'
#' @param psi Transition probabilities between B origin sites and W target
#' sites. B by W matrix
#' @param banded A vector of the number of released animals from each origin
#' site (including those never reencountered in a target site). Length B
#' @param r A vector (length W) of reencounter probabilities at each target site
#'
#' @return \code{simCMRData} returns a list with the elements:
#' \describe{
#' \item{\code{reencountered}}{B by W matrix with numbers reencountered at
#' each target site, by origin site}
#' \item{\code{migrated}}{B by W matrix with numbers migrated to
#' each target site, by origin site. Assumes survival to arrival is 1}
#' \item{\code{input}}{List containing the inputs to function}
#' }
#' @export
#'
#' @example inst/examples/simCMRExamples2.R
simCMRData <- function(psi, banded, r) {
moved <- reencountered <- array(0, dim(psi), dimnames = dimnames(psi))
nOriginSites <- nrow(psi)
nTargetSites <- nrow(psi)
for (i in 1:nOriginSites) {
moved[i, ] <- stats::rmultinom(1, banded[i], psi[i, ])
for (j in 1:nTargetSites) {
reencountered[i,j] <- stats::rbinom(1, moved[i,j], r[j])
}
}
return(list(reencountered = reencountered,
migrated = moved,
input = list(psi = psi, banded = banded, r = r)))
}
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Defines functions simCMRData simProbData simTelemetryData simGLData simMove
Documented in simCMRData simGLData simMove simProbData simTelemetryData
###############################################################################
# Simulates position of birds by individual, season, year, and month.
# Incorporates migratory connectivity, movement within season, and dispersal
# between seasons.
# Does not incorporate births or deaths.
###############################################################################
#' Simulates position of birds by individual, season, year, and month.
#'
#' Incorporates migratory connectivity, movement within season, and dispersal
#' between seasons. Does not incorporate births or deaths.
#'
#' @param breedingAbund Vector with number of birds to simulate starting at
#' each breeding site.
#' @param breedingDist Distances between the breeding sites. Symmetric matrix.
#' @param winteringDist Distances between the wintering sites. Symmetric
#' matrix.
#' @param psi Transition probabilities between B origin and W target sites.
#' A matrix with B rows and W columns where rows sum to 1.
#' @param nYears Number of years to simulate movement.
#' @param nMonths Number of months per breeding and wintering season.
#' @param winMoveRate Within winter movement rate. Defaults to 0 (no
#' movement).
#' @param sumMoveRate Within summer movement rate. Defaults to 0 (no
#' movement).
#' @param winDispRate Between winter dispersal rate. Defaults to 0 (no
#' dispersal).
#' @param sumDispRate Between summer dispersal rate. Defaults to 0 (no
#' dispersal). Setting this to a value above 0 is equivalent to setting
#' both natal and breeding dispersal to that same value.
#' @param natalDispRate Natal dispersal rate. Controls the movement of
#' animals from their birthplace on their first return to the breeding
#' grounds. Defaults to 0 (return to the birthplace for all).
#' @param breedDispRate Breeding dispersal rate. Controls the movement of
#' animals between breeding sites on spring migrations after the first.
#' Defaults to 0 (return to the same breeding site each year).
#' @param verbose If set to a value > 0, informs the user on the passage
#' of years and seasons during the simulation. Defaults to 0 (no output
#' during simulation).
#'
#' @return \code{simMove} returns a list with elements:
#' \describe{
#' \item{\code{animalLoc}}{\code{sum(breedingAbund)} (number of animals)
#' by 2 by \code{nYears} by \code{nMonths} array with the simulated
#' locations of each animal in each month of each season (summer or
#' winter) of each year. Values of cells are 1...B (first column) and
#' 1...W (second column) where B is the number of breeding sites and W is
#' the number of wintering sites.}
#' \item{\code{breedDispMat}}{B by B matrix of probabilities of breeding
#' dispersal between each pair of 1...B breeding sites. Direction is from
#' row to column, so each row sums to 1.}
#' \item{\code{natalDispMat}}{B by B matrix of probabilities of natal
#' dispersal between each pair of 1...B breeding sites. Direction is from
#' row to column, so each row sums to 1.}
#' \item{\code{sumMoveMat}}{B by B matrix of probabilities of within season
#' movement between each pair of 1...B breeding sites. Direction is from
#' row to column, so each row sums to 1.}
#' \item{\code{winDispMat}}{W by W matrix of probabilities of dispersal
#' between each pair of 1...W nonbreeding sites. Direction is from
#' row to column, so each row sums to 1.}
#' \item{\code{winMoveMat}}{W by W matrix of probabilities of within season
#' movement between each pair of 1...W nonbreeding sites. Direction is
#' from row to column, so each row sums to 1.}
#' }
#'
#' @export
#' @example inst/examples/simMoveExamples.R
#' @references
#' Cohen, E. B., J. A. Hostetler, M. T. Hallworth, C. S. Rushing, T. S. Sillett,
#' and P. P. Marra. 2018. Quantifying the strength of migratory connectivity.
#' Methods in Ecology and Evolution 9: 513-524.
#' \doi{10.1111/2041-210X.12916}
simMove <- function(breedingAbund, breedingDist, winteringDist, psi,
nYears = 10, nMonths = 3, winMoveRate = 0,
sumMoveRate = 0, winDispRate = 0, sumDispRate = 0,
natalDispRate = 0, breedDispRate = 0, verbose = 0)
{
nSeasons <- 2
nBreeding <- length(breedingAbund)
nWintering <- nrow(winteringDist)
if (sumDispRate>0 && (natalDispRate>0 || breedDispRate>0) &&
(sumDispRate!=natalDispRate || sumDispRate!=breedDispRate))
stop("Can't specify summer dispersal separately from breeding or natal dispersal")
if (sumDispRate<0 || natalDispRate<0 || breedDispRate<0 ||sumMoveRate<0 ||
winMoveRate<0)
stop("Can't specify negative movement or dispersal rates")
# Turn rate terms into probabilities
if (winMoveRate>0) {
winMoveMat <- mlogitMat(1/sqrt(winMoveRate), winteringDist)
}
else
winMoveMat <- NULL
if (sumMoveRate>0) {
sumMoveMat <- mlogitMat(1/sqrt(sumMoveRate), breedingDist)
}
else
sumMoveMat <- NULL
if (winDispRate>0) {
winDispMat <- mlogitMat(1/sqrt(winDispRate), winteringDist)
}
else
winDispMat <- NULL
if (sumDispRate>0) {
natalDispRate <- sumDispRate
breedDispRate <- sumDispRate
}
if (natalDispRate>0) {
natalDispMat <- mlogitMat(1/sqrt(natalDispRate), breedingDist)
}
else
natalDispMat <- NULL
if (breedDispRate>0) {
breedDispMat <- mlogitMat(1/sqrt(breedDispRate), breedingDist)
}
else
breedDispMat <- NULL
# Storage of locations
animalLoc <- array(NA, c(sum(breedingAbund), nSeasons, nYears, nMonths))
animalLoc[,1,1,1] <- rep(1:nBreeding, breedingAbund)
# Run simulation
for (y in 1:nYears) {
if (verbose>0)
cat("Year", y, "Summer, ")
if (nMonths > 1)
for (sm in 2:nMonths) {
if (sumMoveRate==0)
animalLoc[,1,y,sm] <- animalLoc[,1,y,sm-1]
else
for (i in 1:sum(breedingAbund))
animalLoc[i,1,y,sm] <- which(rmultinom(1,1, sumMoveMat[animalLoc[i,1,y,sm-1], ])>0)
}
if (verbose>0)
cat("Fall, ")
if (y == 1) {
for (i in 1:sum(breedingAbund))
animalLoc[i,2,y,1] <- which(rmultinom(1,1, psi[animalLoc[i,1,y,1], ])>0)
}
else if (winDispRate==0)
animalLoc[,2,y,1] <- animalLoc[,2,y-1,1]
else
for (i in 1:sum(breedingAbund))
animalLoc[i,2,y,1] <- which(rmultinom(1,1, winDispMat[animalLoc[i,2,y-1,1], ])>0)
if (verbose>0)
cat("Winter, ")
if (nMonths > 1)
for (wm in 2:nMonths) {
if (winMoveRate==0)
animalLoc[,2,y,wm] <- animalLoc[,2,y,wm-1]
else
for (i in 1:sum(breedingAbund))
animalLoc[i,2,y,wm] <- which(rmultinom(1,1, winMoveMat[animalLoc[i,2,y,wm-1], ])>0)
}
if (verbose>0)
cat("Spring\n")
if (y == 1 & nYears>1) {
if (natalDispRate==0)
animalLoc[,1,y+1,1] <- animalLoc[,1,y,1]
else
for (i in 1:sum(breedingAbund))
animalLoc[i,1,y+1,1] <- which(rmultinom(1,1, natalDispMat[animalLoc[i,1,y,1], ])>0)
}
else if (y < nYears) {
if (breedDispRate==0)
animalLoc[,1,y+1,1] <- animalLoc[,1,y,1]
else
for (i in 1:sum(breedingAbund))
animalLoc[i,1,y+1,1] <- which(rmultinom(1,1, breedDispMat[animalLoc[i,1,y,1], ])>0)
}
}
return(list(animalLoc = animalLoc, natalDispMat = natalDispMat,
breedDispMat = breedDispMat, sumMoveMat = sumMoveMat,
winDispMat = winDispMat, winMoveMat = winMoveMat))
}
###############################################################################
# Function for generating simulated count data
###############################################################################
#' Simulates Breeding Bird Survey-style count data
#'
#' Recently updated (version 0.4.3) to more properly match current BBS models.
#' \code{modelCountDataJAGS} has not been updated yet
#'
#' @param nStrata Number of populations/regions/strata
#' @param routesPerStrata Vector of length 1 or nStrata containing the number of
#' routes (i.e. counts) per stratum. If length(routesPerStrata) == 1, number of
#' routes is identical for each population
#' @param nYears Number of years surveys were conducted
#' @param alphaStrat Vector of length 1 or nStrata containing the log expected
#' number of individuals counted at each route for each population. If
#' length(alphaStrat) == 1, expected counts are identical for each population
#' @param beta Coefficient of linear year effect (default 0)
#' @param eta Coefficient of first time run effect (default 0)
#' @param sdRoute Standard deviation of random route-level variation. Default is
#' 0, and if you're setting sdObs, it's probably best to keep it that way
#' @param sdYear Standard deviation of random year-level variation (default 0)
#' @param sdObs Standard deviation of random observer variation (default 0)
#' @param sdCount Standard deviation of random count-level variation (default 0)
#' @param model One of "S" (default), "Sh", "D", and "Dh". See Link et al.
#' (2020) for descriptions of these models
#' @param obsSurvival Annual probability that the observer that ran a route one
#' year will run it the next. Default 1 (each route has only one observer)
#' @param fixedyear The year within nYears that alphaStrat applies directly to
#' (default halfway through)
#' @param nuCount For the "h" models, parameter for extra variation in counts
#' (default 1)
#'
#' @return \code{simCountData} returns a list containing:
#' \describe{
#' \item{\code{nStrata}}{Number of populations/regions.}
#' \item{\code{nRoutes}}{Total number of routes.}
#' \item{\code{nYears}}{Number of years.}
#' \item{\code{routesPerStrata}}{Number of routes per population.}
#' \item{\code{year}}{Vector of length nYears with standardized year values.}
#' \item{\code{strat}}{Vector of length nRoutes indicating the
#' population/region in which each route is located.}
#' \item{\code{alphaStrat}}{log expected count for each populations.}
#' \item{\code{epsRoute}}{realized deviation from alphaStrat for each route.}
#' \item{\code{epsYear}}{realized deviation from alphaStrat for each year.}
#' \item{\code{beta}}{linear year effect.}
#' \item{\code{sdRoute}}{standard deviation of random route-level variation.}
#' \item{\code{sdYear}}{standard deviation of random year-level variation.}
#' \item{\code{expectedCount}}{nRoutes by nYears matrix containing
#' deterministic expected counts.}
#' \item{\code{C}}{nRoutes by nYears matrix containing observed counts.}
#' }
#'
#'
#' @export
#' @example inst/examples/simCountExamples.R
#' @references
#' Cohen, E. B., J. A. Hostetler, M. T. Hallworth, C. S. Rushing, T. S. Sillett,
#' and P. P. Marra. 2018. Quantifying the strength of migratory connectivity.
#' Methods in Ecology and Evolution 9: 513-524.
#' \doi{10.1111/2041-210X.12916}
#'
#' Link, W. A., J. R. Sauer, and D. K. Niven. 2020. Model selection for the
#' North American Breeding Bird Survey. Ecological Applications 30: e02137.
#' \doi{10.1002/eap.2137}
simCountData <- function (nStrata, routesPerStrata, nYears, alphaStrat, beta = 0,
eta = 0, sdRoute = 0, sdYear = 0, sdObs = 0, sdCount = 0,
model = c("S", "Sh", "D", "Dh"), obsSurvival = 1,
fixedyear = round(nYears/2), nuCount = 1){
model <- match.arg(model)
if(length(routesPerStrata) == 1){
routesPerStrata <- rep(routesPerStrata, nStrata)
}
nRoutes <- sum(routesPerStrata)
strat <- rep(1:nStrata, routesPerStrata) # Population index for each route
if(length(alphaStrat) == 1) {
alphaStrat <- rep(alphaStrat, nStrata)
}
if(length(beta) == 1) {
beta <- rep(beta, nStrata)
}
if(length(sdRoute) == 1) {
sdRoute <- rep(sdRoute, nStrata)
}
if(length(sdYear) == 1) {
sdYear <- rep(sdYear, nStrata)
}
# Generate data structure to hold counts and log (lambda)
first <- obser <- C <- log.expectedCount <- array(0, dim = c(nYears, nRoutes))
expectedCount <- array(0, dim = c(nYears, nRoutes))
epsYear <- array(0, dim = c(nYears, nStrata))
# Generate covariate values
year <- 1:nYears
yr <- year - fixedyear # Standardize
# Generate sequence of observers
nObs <- 0
for (r in 1:nRoutes) {
nObs <- nObs + 1
obser[1, r] <- nObs
first[1, r] <- 1
for (y in 2:nYears) {
if (rbinom(1, 1, obsSurvival)==0){
nObs <- nObs + 1
first[y, r] <- 1
}
obser[y, r] <- nObs
}
}
# Draw two sets of random effects from their respective distributions
epsObs <- rnorm(n = nObs, mean = 0, sd = sdObs)
epsCount <- rnorm(n = nRoutes * nYears, mean = 0, sd = sdCount)
if (model == "Sh" || model == "Dh"){
V <- rgamma(n = nRoutes * nYears, shape = nuCount/2, rate = nuCount/2)
epsCount <- epsCount / sqrt(V)
}
epsRoute <- vector("list", nStrata)
place <- 0
for (s in 1:nStrata) {
epsRoute[[s]] <- rnorm(n = routesPerStrata[s], mean = 0, sd = sdRoute[s])
if (model %in% c("S", "Sh"))
epsYear[ , s] <- rnorm(n = nYears, mean = 0, sd = sdYear[s])
else {
for (y in ((fixedyear-1):1))
epsYear[y, s] <- rnorm(n = 1, mean = epsYear[y+1, s], sd = sdYear[s])
for (y in ((fixedyear+1):nYears))
epsYear[y, s] <- rnorm(n = 1, mean = epsYear[y-1, s], sd = sdYear[s])
}
}
# Loop over routes
for (i in 1:nRoutes){
if (model %in% c("S", "Sh"))
# Build up systematic part of the GLM including random effects
log.expectedCount[,i] <- alphaStrat[strat[i]] + beta[strat[i]]*yr +
epsRoute[[strat[i]]][i] + epsYear[ , strat[i]] + epsCount[(i-1)*nYears + 1:nYears] +
epsObs[obser[ , i]] + eta * first[ , i]
else { # model D or Dh, ignore beta
log.expectedCount[,i] <- alphaStrat[strat[i]] +
epsRoute[[strat[i]]][i] + epsYear[ , strat[i]] + epsCount[(i-1)*nYears + 1:nYears] +
epsObs[obser[ , i]] + eta * first[ , i]
}
expectedCount[ , i] <- exp(log.expectedCount[,i])
C[,i] <- rpois(n = nYears, lambda = expectedCount)
}
return(list(C = C, strat = strat, obser = obser, first = first,
epsRoute = epsRoute, epsYear = epsYear, epsObs = epsObs,
epsCount = epsCount, expectedCount = expectedCount,
input = list(nStrata = nStrata, nRoutes = nRoutes, nYears = nYears,
routesPerStrata = routesPerStrata, year = yr,
alphaStrat = alphaStrat, beta = beta,
sdRoute = sdRoute, sdYear = sdYear, sdObs = sdObs,
sdCount = sdCount)))
}
###############################################################################
# Estimates population-level relative abundance from count data
###############################################################################
#' Estimates population-level relative abundance from count data
#'
#' Uses a Bayesian hierarchical model to estimate relative abundance of regional
#' populations from count-based data (e.g., Breeding Bird Survey)
#'
#' @param count_data List containing the following elements:
#' ' \describe{
#' \item{\code{C}}{nYears by nRoutes matrix containing the observed number of individuals counted at each route in each year.}
#' \item{\code{strat}}{Vector of length nRoutes indicating the population/region in which each route is located.}
#' \item{\code{routesPerStrata}}{Vector of length 1 or nStrata containing the number of routes (i.e. counts) per population. If length(routesPerStrata) == 1, number of routes is identical for each population.}
#' }
#' @param ni Number of MCMC iterations. Default = 20000.
#' @param nt Thinning rate. Default = 5.
#' @param nb Number of MCMC iterations to discard as burn-in. Default = 5000.
#' @param nc Number of chains. Default = 3.
#'
#' @return \code{modelCountDataJAGS} returns an mcmc object containing posterior samples for each monitored parameter.
#
#'
#' @export
#' @example inst/examples/simCountExamples.R
#' @references
#' Cohen, E. B., J. A. Hostetler, M. T. Hallworth, C. S. Rushing, T. S. Sillett,
#' and P. P. Marra. 2018. Quantifying the strength of migratory connectivity.
#' Methods in Ecology and Evolution 9: 513-524.
#' \doi{10.1111/2041-210X.12916}
#'
#' Link, W. A. and J. R. Sauer. 2002. A hierarchical analysis of population
#' change with application to Cerulean Warblers. Ecology 83: 2832-2840.
#' \doi{10.1890/0012-9658(2002)083[2832:AHAOPC]2.0.CO;2}
#'
modelCountDataJAGS <- function (count_data, ni = 20000, nt = 5, nb = 5000, nc = 3) {
nPops <- length(unique(count_data$strat))
nRoutes <- dim(count_data$C)[2]
nYears = dim(count_data$C)[1]
if(length(count_data$routesPerStrata) == 1){
routesPerStrata = rep(count_data$input$routesPerStrata, nPops)
} else {
routesPerStrata = count_data$input$routesPerStrata
}
# Initial values
jags.inits <- function()list(mu = runif(1,0,2), alpha = runif(nPops, -1,1), beta1 = runif(1,-1,1),
tau.alpha = runif(1,0,0.1), tau.noise = runif(1,0,0.1),
tau.rte = runif(1,0,0.1), route = runif(nRoutes,-1,1))
# Parameters to monitor
params <- c("mu", "alpha", "beta1", "sd.alpha", "sd.rte", "sd.noise", "totalN", "popN", "relN")
# Data
jags.data <- list(C = count_data$C, nPops = length(unique(count_data$strat)), nRoutes = nRoutes,
routePerPop = routesPerStrata,
year = seq(from = 0, to = 1, length.out = nYears), nYears = nYears, pop = count_data$strat)
file <- tempfile(fileext = ".txt")
sink(file = file)
cat("
model{
# Priors
for(p in 1:nPops){
alpha[p] ~ dnorm(mu, tau.alpha)
}
mu ~ dnorm(0, 0.01)
tau.alpha ~ dgamma(0.001,0.001)
sd.alpha <- 1/pow(tau.alpha, 0.5)
for(r in 1:nRoutes){
route[r] ~ dnorm(0, tau.rte)
}
tau.rte ~ dgamma(0.001, 0.001)
sd.rte <- 1/pow(tau.rte, 0.5)
tau.noise ~ dgamma(0.001, 0.001)
sd.noise <- 1/pow(tau.noise,0.5)
beta1 ~ dnorm(0, 0.01)
# Likelihood
for(i in 1:nYears){
eps[i] ~ dnorm(0, tau.noise)
for(j in 1:nRoutes){
C[i,j] ~ dpois(lambda[i,j])
log(lambda[i,j]) <- alpha[pop[j]] + beta1*year[i] + route[j] + eps[i]
}
}
# Derived parameters
totalN <- sum(popN[1:nPops])
for(i in 1:nPops){
popN[i] <- exp(alpha[i] + beta1*nYears + eps[nYears])*routePerPop[i]
relN[i] <- popN[i]/totalN
}
}")
sink()
out <- R2jags::jags(data = jags.data, inits = jags.inits, params,
file,
n.chains = nc, n.thin = nt, n.iter = ni, n.burnin = nb,
progress.bar = 'none')
file.remove(file)
return(coda::as.mcmc(out))
}
#' Simulate geolocator (GL) migratory movement data
#'
#' @param psi Transition probabilities between B origin and W target sites.
#' A matrix with B rows and W columns where rows sum to 1.
#' @param originRelAbund Relative abundances at B origin sites. Numeric vector
#' of length B that sums to 1.
#' @param sampleSize List of length two. The first element is either a vector of
#' length B with the number of simulated animals to release with geolocators
#' at each of the B origin sites, a single integer with the total number of
#' simulated animals to release with geolocators at origin sites (in which
#' case, the origin sites will be sampled according to the relative
#' abundance), or NULL if all animals are released at target sites. The
#' second element is either a vector of length W with the number of simulated
#' animals to release with geolocators at each of the W target sites, a
#' single integer with the total number of simulated animals to release with
#' geolocators at target sites (in which case, the target sites will be
#' sampled according to their relative abundance), or NULL if all animals are
#' released at origin sites.
#' @param originSites A polygon spatial layer (sf - MULTIPOLYGON) defining the
#' geographic representation of sites in the origin season.
#' @param targetSites A polygon spatial layer (sf - MULTIPOLYGON) defining the
#' geographic representation of sites in the target season.
# @param captured
#' @param geoBias Vector of length 2 indicating expected bias in longitude and
#' latitude of animals captured and released at origin sites, in
#' \code{targetSites} units.
#' @param geoVCov 2x2 matrix with expected variance/covariance in longitude and
#' latitude of animals captured and released at origin sites, in
#' \code{targetSites} units.
#' @param geoBiasOrigin Vector of length 2 indicating expected bias in longitude
#' and latitude of animals captured and released at target sites, in
#' \code{originSites} units.
#' @param geoVCovOrigin 2x2 matrix with expected variance/covariance in
#' longitude and latitude of animals captured and released at target sites,
#' in \code{originSites} units.
#' @param S Survival probabilities of released geolocator animals. Either a
#' matrix with B rows and W columns (if survival depends on both origin site
#' and target site), a vector of length W (if survival depends only on target
#' site), or a single number (if survival is the same for all animals).
#' Default 1 (all animals with geolocators survive a year).
#' @param p Recapture probabilities of released geolocator animals; list of
#' length two. The first element is either a vector of length B (if recapture
#' depends on origin site), or a single number (if recapture is the same for
#' all animals released on origin sites). The second element is either a
#' vector of length W (if recapture depends on target site), or a single
#' number (if recapture is the same for all animals released on target
#' sites). Default list(1, 1) (all animals that survive are recaptured).
#' @param requireEveryOrigin If TRUE, the function will throw an error if it
#' looks like at least one origin site has no animals released in or
#' migrating to it, or if it can, keep simulating until representation is
#' met. This helps estTransition or estMC not throw an error. Default FALSE.
#' @return \code{simGLData} returns a list with the elements:
#' \describe{
#' \item{\code{originAssignment}}{Vector with true origin site of each animal}
#' \item{\code{targetAssignment}}{Vector with true target site of each animal}
#' \item{\code{originPointsTrue}}{True origin location of each animal, type sf,
#' same projection as originSites}
#' \item{\code{targetPointsTrue}}{True target location of each animal, type sf,
#' same projection as targetSites}
#' \item{\code{originPointsObs}}{Observed origin location of each animal that
#' survived and was recaptured, type sf, same projection as originSites. Same
#' as originPointsTrue for animals captured at origin sites when S and p==1}
#' \item{\code{targetPointsObs}}{Observed target location of each animal that
#' survived and was recaptured, type sf, same projection as targetSites. Same
#' as targetPointsTrue for animals captured at target sites when S and p==1}
#' \item{\code{lived}}{0/1 vector for each animal, indicating which survived}
#' \item{\code{recaptured}}{0/1 vector for each animal, indicating which were
#' recaptured}
#' \item{\code{input}}{List containing the inputs to function}
#' }
#' @export
#'
# @examples
simGLData <- function(psi, originRelAbund = NULL, sampleSize,
originSites = NULL, targetSites = NULL,
#captured = "origin",
geoBias = NULL, geoVCov = NULL,
geoBiasOrigin = geoBias, geoVCovOrigin = geoVCov,
S = 1, p = list(1, 1),
requireEveryOrigin = FALSE) {
nOriginSites <- nrow(psi)
nTargetSites <- ncol(psi)
if (!is.null(originRelAbund)) {
rev <- reversePsiRelAbund(psi, originRelAbund)
gamma <- rev$gamma
targetRelAbund <- rev$targetRelAbund
}
else if (!is.null(sampleSize[[2]]) || length(sampleSize[[1]]) < 2)
stop("Need to enter in originRelAbund for target data or for single origin sample size")
if (length(dim(S))<2) {
S <- matrix(S, nOriginSites, nTargetSites, byrow = TRUE)
}
if (length(p[[1]]==1))
p[[1]] <- rep(p[[1]], nOriginSites)
if (length(p[[2]]==1))
p[[2]] <- rep(p[[2]], nTargetSites)
nAnimals <- sum(sampleSize[[1]]) + sum(sampleSize[[2]])
captured <- rep(c("origin", "target"),
c(sum(sampleSize[[1]]), sum(sampleSize[[2]])))
# if (length(captured)==1)
# captured <- rep(captured, nAnimals)
targetAssignment <- rep(NA, nAnimals)
originAssignment <- rep(NA, nAnimals)
lived <- rep(1, nAnimals)
recaptured <- rep(1, nAnimals)
if (length(sampleSize[[1]])>1){
originAssignment[captured=="origin"] <- rep(1:nOriginSites, sampleSize[[1]])
if (requireEveryOrigin && is.null(sampleSize[[2]])){
if (any(sampleSize[[1]] < 1))
stop("Some origin site or sites have no samples ", sampleSize[[1]])
}
}
if (length(sampleSize[[2]])>1){
targetAssignment[captured=="target"] <- rep(1:nTargetSites, sampleSize[[2]])
}
runWell <- FALSE
while (!runWell){
for (a in 1:nAnimals) {
if (captured[a]=="origin") {
if (is.na(originAssignment[a]))
originAssignment[a] <- sample.int(nOriginSites, 1, prob = originRelAbund)
targetAssignment[a] <- sample.int(nTargetSites, 1, prob = psi[originAssignment[a], ])
lived[a] <- rbinom(1, 1, S[originAssignment[a], targetAssignment[a]])
recaptured[a] <- rbinom(1, 1, lived[a] * p[[1]][originAssignment[a]])
}
else {
if (is.na(targetAssignment[a]))
targetAssignment[a] <- sample.int(nTargetSites, 1, prob = targetRelAbund)
originAssignment[a] <- sample.int(nOriginSites, 1, prob = gamma[targetAssignment[a], ])
lived[a] <- rbinom(1, 1, S[originAssignment[a], targetAssignment[a]])
recaptured[a] <- rbinom(1, 1, lived[a] * p[[2]][targetAssignment[a]])
}
}
oa1 <- sort(unique(originAssignment))
runWell <- !requireEveryOrigin || any(captured=="target") ||
isTRUE(all.equal(oa1, 1:nOriginSites))
}
oaTest <- list(1:2); taTest <- list(2:3)
while (any(lengths(oaTest)>1) || any(lengths(taTest)>1)) {
originPointsTrue <- randomPoints(originSites, originAssignment)
targetPointsTrue <- randomPoints(targetSites, targetAssignment)
originPointsObs <- array(NA, c(sum(recaptured), 2),
dimnames = list(NULL, c("x","y")))
targetPointsObs <- array(NA, c(sum(recaptured), 2),
dimnames = list(NULL, c("x","y")))
linkerOrigin <- which(recaptured == 1) %in%
which(captured=="origin" & recaptured == 1)
linkerTarget <- which(recaptured == 1) %in%
which(captured=="target" & recaptured == 1)
if (any(captured=="origin" & recaptured == 1)) {
geoBias2 <- array(rep(geoBias, sum(captured=="origin" & recaptured == 1)),
c(2, sum(captured=="origin" & recaptured == 1)))
targetPointsObs[linkerOrigin, ] <-
t(array(apply(sf::st_coordinates(targetPointsTrue)[captured=="origin" & recaptured == 1, , drop = FALSE], 1,
MASS::mvrnorm, n=1, Sigma=geoVCov),
c(2, sum(captured=="origin" & recaptured == 1))) + geoBias2)
originPointsObs[linkerOrigin, ] <-
sf::st_coordinates(originPointsTrue)[captured=="origin" & recaptured == 1, ,
drop = FALSE]
}
if (any(captured == "target" & recaptured == 1)) {
geoBias2 <- array(rep(geoBiasOrigin, sum(captured == "target" & recaptured == 1)),
c(2, sum(captured == "target" & recaptured == 1)))
originPointsObs[linkerTarget, ] <-
t(array(apply(sf::st_coordinates(originPointsTrue)[captured == "target" & recaptured == 1, , drop = FALSE], 1,
MASS::mvrnorm, n=1, Sigma=geoVCovOrigin),
c(2, sum(captured == "target" & recaptured == 1))) + geoBias2)
targetPointsObs[linkerTarget, ] <-
sf::st_coordinates(targetPointsTrue)[captured=="target" & recaptured == 1, ,
drop = FALSE]
}
targetPointsObs <- sf::st_as_sf(data.frame(targetPointsObs),
coords = c("x","y"),
crs = sf::st_crs(targetSites))
originPointsObs <- sf::st_as_sf(data.frame(originPointsObs),
coords = c("x","y"),
crs = sf::st_crs(originSites))
oaTest <- suppressMessages(unclass(sf::st_intersects(x = originPointsObs,
y = originSites,
sparse = TRUE)))
taTest <- suppressMessages(unclass(sf::st_intersects(x = targetPointsObs,
y = targetSites,
sparse = TRUE)))
}
return(list(originAssignment = originAssignment,
targetAssignment = targetAssignment,
originPointsTrue = originPointsTrue,
targetPointsTrue = targetPointsTrue,
originPointsObs = originPointsObs,
targetPointsObs = targetPointsObs,
lived = lived,
recaptured = recaptured,
input = list(psi = psi, originRelAbund = originRelAbund,
originSites = originSites,
targetSites = targetSites,
captured = captured,
geoBias = geoBias, geoVCov = geoVCov,
geoBiasOrigin = geoBiasOrigin,
geoVCovOrigin = geoVCovOrigin,
S = S, p = p)))
}
#' Simulate telemetry/GPS data
#'
#' @param psi Transition probabilities between B origin and W target sites.
#' A matrix with B rows and W columns where rows sum to 1.
#' @param sampleSize If captured is "origin", either a vector of
#' length B with the number of simulated animals to release with geolocators
#' at each of the B origin sites or a single integer with the total number of
#' simulated animals to release with GPS at origin sites (in which
#' case, the origin sites will be sampled according to the relative
#' abundance). If captured is "target", either a vector of length W with the
#' number of simulated animals to release with GPS at each of the W target
#' sites or a single integer with the total number of simulated animals to
#' release at target sites (in which case, the target sites will be
#' sampled according to their relative abundance).
#' @param originRelAbund Relative abundances at B origin sites. Numeric vector
#' of length B that sums to 1. Optional unless providing target data and/or
#' sample size of length 1.
#' @param originSites A polygon spatial layer (sf - MULTIPOLYGON) defining the
#' geographic representation of sites in the origin season. If left NULL, the
#' simulation won't provide origin points.
#' @param targetSites A polygon spatial layer (sf - MULTIPOLYGON) defining the
#' geographic representation of sites in the target season. If left NULL, the
#' simulation won't provide target points.
#' @param captured Either "origin" (the default) or "target".
#' @param S Survival probabilities of released animals. Probably only
#' relevant for simulating archival tags. Either a matrix with B rows and W
#' columns (if survival depends on both origin site and target site), a vector
#' of length W (if survival depends only on target site), or a single number
#' (if survival is the same for all animals). Default 1 (all tagged animals
#' survive a year).
#' @param p Recapture probabilities of released animals. Only relevant for
#' simulating archival tags. Either a vector of length B (if captured on origin
#' and recapture depends on origin site), a vector of length W (if captured on
#' target and recapture depends on target site), or a single number (if
#' recapture is the same for all animals). Default 1 (all animals that survive
#' are recaptured).
#' @param requireEveryOrigin If TRUE, the function will throw an error if it
#' looks like at least one origin site has no animals released in or
#' migrating to it, or if it can, keep simulating until representation is
#' met. This helps estTransition not throw an error. Default FALSE.
#' @return \code{simTelemetryData} returns a list with the elements:
#' \describe{
#' \item{\code{originAssignment}}{Vector with true origin site of each animal}
#' \item{\code{targetAssignment}}{Vector with true target site of each animal}
#' \item{\code{originPointsTrue}}{True origin location of each animal, type sf,
#' same projection as originSites}
#' \item{\code{targetPointsTrue}}{True target location of each animal, type sf,
#' same projection as targetSites}
#' \item{\code{originPointsObs}}{Observed origin location of each animal that
#' survived and was recaptured, type sf, same projection as originSites. Same
#' as originPointsTrue when S and p==1}
#' \item{\code{targetPointsObs}}{Observed target location of each animal that
#' survived and was recaptured, type sf, same projection as targetSites. Same
#' as targetPointsTrue when S and p==1}
#' \item{\code{lived}}{0/1 vector for each animal, indicating which survived}
#' \item{\code{recaptured}}{0/1 vector for each animal, indicating which were
#' recaptured}
#' \item{\code{input}}{List containing the inputs to function}
#' }
#' @export
#'
# @examples
simTelemetryData <- function(psi, sampleSize, originRelAbund = NULL,
originSites = NULL, targetSites = NULL,
captured = "origin",
S = 1, p = 1,
requireEveryOrigin = FALSE) {
nOriginSites <- nrow(psi)
nTargetSites <- ncol(psi)
if (!is.null(originRelAbund)) {
rev <- reversePsiRelAbund(psi, originRelAbund)
gamma <- rev$gamma
targetRelAbund <- rev$targetRelAbund
}
else if (captured != "origin" || length(sampleSize) < 2)
stop("Need to enter in originRelAbund for target data or for single sample size")
if (length(dim(S))<2) {
S <- matrix(S, nOriginSites, nTargetSites, byrow = TRUE)
}
if (length(p==1)){
if (captured=="origin")
p <- rep(p, nOriginSites)
else
p <- rep(p, nTargetSites)
}
nAnimals <- sum(sampleSize)
targetAssignment <- rep(NA, nAnimals)
originAssignment <- rep(NA, nAnimals)
lived <- rep(1, nAnimals)
recaptured <- rep(1, nAnimals)
if (length(sampleSize)>1 && captured=="origin"){
originAssignment <- rep(1:nOriginSites, sampleSize)
if (requireEveryOrigin){
if (any(sampleSize < 1))
stop("Some origin site or sites have no samples ", sampleSize)
}
}
else if (length(sampleSize)>1 && captured=="origin"){
targetAssignment <- rep(1:nTargetSites, sampleSize)
}
runWell <- FALSE
while (!runWell){
for (a in 1:nAnimals) {
if (captured=="origin") {
if (is.na(originAssignment[a]))
originAssignment[a] <- sample.int(nOriginSites, 1, prob = originRelAbund)
targetAssignment[a] <- sample.int(nTargetSites, 1, prob = psi[originAssignment[a], ])
lived[a] <- rbinom(1, 1, S[originAssignment[a], targetAssignment[a]])
recaptured[a] <- rbinom(1, 1, lived[a] * p[originAssignment[a]])
}
else {
if (is.na(targetAssignment[a]))
targetAssignment[a] <- sample.int(nTargetSites, 1, prob = targetRelAbund)
originAssignment[a] <- sample.int(nOriginSites, 1, prob = gamma[targetAssignment[a], ])
lived[a] <- rbinom(1, 1, S[originAssignment[a], targetAssignment[a]])
recaptured[a] <- rbinom(1, 1, lived[a] * p[targetAssignment[a]])
}
}
oa1 <- sort(unique(originAssignment))
runWell <- !requireEveryOrigin || (captured=="target") ||
isTRUE(all.equal(oa1, 1:nOriginSites))
}
if (!is.null(originSites))
originPointsTrue <- randomPoints(originSites, originAssignment)
else
originPointsTrue <- NULL
if (!is.null(originSites))
targetPointsTrue <- randomPoints(targetSites, targetAssignment)
else
targetPointsTrue <- NULL
return(list(originAssignment = originAssignment,
targetAssignment = targetAssignment,
originPointsTrue = originPointsTrue,
targetPointsTrue = targetPointsTrue,
originPointsObs = originPointsTrue[recaptured==1, ],
targetPointsObs = targetPointsTrue[recaptured==1, ],
lived = lived,
recaptured = recaptured,
input = list(psi = psi, originRelAbund = originRelAbund,
originSites = originSites,
targetSites = targetSites,
captured = captured,
S = S, p = p)))
}
#' Simulate Dirichlet-based probability table data
#'
#' @param psi Transition probabilities between B origin sites and W target
#' sites. B by W matrix
#' @param originRelAbund Vector of relative abundances at B origin sites
#' @param sampleSize Either the total number of data points to simulate or a
#' vector with the number at each target or origin site. If only the total is
#' provided, sampling will be done in proportion to abundance
#' @param shapes If captured == "target", a B by B matrix, each row of which is
#' the shape parameters for the Dirichlet distribution of an animal whose true
#' origin assignment is that row's. If captured == "origin", a W by W matrix,
#' each row of which is the shape parameters for the Dirichlet distribution of
#' an animal whose true target assignment is that row's.
#' @param captured Either "target" (the default) or "origin", indicating which
#' side animal data were collected on
#' @param requireEveryOrigin If TRUE, the function will throw an error if it
#' looks like at least one origin site has no animals released in or
#' migrating to it, or if it can, keep simulating until representation is
#' met. This helps estTransition or estMC not throw an error. Default FALSE
#'
#' @return \code{simProbData} returns a list with the elements:
#' \describe{
#' \item{\code{originAssignment}}{Vector with true origin site of each animal}
#' \item{\code{targetAssignment}}{Vector with true target site of each animal}
#' \item{\code{genProbs}}{Table of assignment site probabilities for each
#' animal}
#' \item{\code{input}}{List containing the inputs to function}
#' }
#' @export
#'
# @examples
simProbData <- function(psi,
originRelAbund,
sampleSize,
shapes,
captured = "target",
requireEveryOrigin = FALSE) {
nOriginSites <- nrow(psi)
nTargetSites <- ncol(psi)
rev <- reversePsiRelAbund(psi, originRelAbund)
gamma <- rev$gamma
targetRelAbund <- rev$targetRelAbund
nAnimals <- sum(sampleSize)
if (length(captured)>1)
captured <- captured[1]
targetAssignment <- rep(NA, nAnimals)
originAssignment <- rep(NA, nAnimals)
if (captured=="origin" && length(sampleSize)>1){
originAssignment <- rep(1:nOriginSites, sampleSize)
if (requireEveryOrigin){
if (any(sampleSize < 1))
stop("Some origin site or sites have no samples ", sampleSize)
}
}
if (captured=="target" && length(sampleSize)>1){
targetAssignment <- rep(1:nTargetSites, sampleSize)
}
runWell <- FALSE
while (!runWell){
for (a in 1:nAnimals) {
if (captured=="origin") {
if (is.na(originAssignment[a]))
originAssignment[a] <- sample.int(nOriginSites, 1, prob = originRelAbund)
targetAssignment[a] <- sample.int(nTargetSites, 1, prob = psi[originAssignment[a], ])
}
else {
if (is.na(targetAssignment[a]))
targetAssignment[a] <- sample.int(nTargetSites, 1, prob = targetRelAbund)
originAssignment[a] <- sample.int(nOriginSites, 1, prob = gamma[targetAssignment[a], ])
}
}
runWell <- !requireEveryOrigin || captured=="target" ||
all.equal(unique(originAssignment), 1:nOriginSites)
}
if (captured == "target") {
genProbs <- array(NA, c(nAnimals, nOriginSites))
for (a in 1:nAnimals) {
genProbs[a, ] <- VGAM::rdiric(1, shapes[originAssignment[a], ])
}
}
else {
genProbs <- array(NA, c(nAnimals, nTargetSites))
for (a in 1:nAnimals) {
genProbs[a, ] <- VGAM::rdiric(1, shapes[targetAssignment[a], ])
}
}
return(list(originAssignment = originAssignment,
targetAssignment = targetAssignment,
genProbs = genProbs,
input = list(psi = psi,
originRelAbund = originRelAbund,
captured = captured,
shapes = shapes)))
}
#' Simulate capture-mark-reencounter (CMR) migratory movement data
#'
#' @param psi Transition probabilities between B origin sites and W target
#' sites. B by W matrix
#' @param banded A vector of the number of released animals from each origin
#' site (including those never reencountered in a target site). Length B
#' @param r A vector (length W) of reencounter probabilities at each target site
#'
#' @return \code{simCMRData} returns a list with the elements:
#' \describe{
#' \item{\code{reencountered}}{B by W matrix with numbers reencountered at
#' each target site, by origin site}
#' \item{\code{migrated}}{B by W matrix with numbers migrated to
#' each target site, by origin site. Assumes survival to arrival is 1}
#' \item{\code{input}}{List containing the inputs to function}
#' }
#' @export
#'
#' @example inst/examples/simCMRExamples2.R
simCMRData <- function(psi, banded, r) {
moved <- reencountered <- array(0, dim(psi), dimnames = dimnames(psi))
nOriginSites <- nrow(psi)
nTargetSites <- nrow(psi)
for (i in 1:nOriginSites) {
moved[i, ] <- stats::rmultinom(1, banded[i], psi[i, ])
for (j in 1:nTargetSites) {
reencountered[i,j] <- stats::rbinom(1, moved[i,j], r[j])
}
}
return(list(reencountered = reencountered,
migrated = moved,
input = list(psi = psi, banded = banded, r = r)))
}
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