#### Basic mixture SCR example
library(openpopscr)
# set number of threads for parallel processing
RcppParallel::setThreadOptions(numThreads = 1)
# simulate data -----------------------------------------------------------
set.seed(53919)
## Set parameters
D <- 1000
lambda0 <- c(1.0, 1.0)
sigma <- c(20, 40)
# make detectors array
detectors <- make.grid(nx = 7, ny = 7, spacing = 20, detector = "count")
rownames(detectors) <- 1:nrow(detectors)
# make mesh
mesh <- make.mask(detectors, buffer = 100, nx = 64, ny = 64, type = "trapbuffer")
# set number of occasions to simulate
K <- 10
## Simulate activity centres
A <- nrow(mesh) * attr(mesh, "area") / 100
N <- rpois(1, D*A)
pt <- sample(1:nrow(mesh), size = N, replace = TRUE)
x <- mesh[pt, 1]
y <- mesh[pt, 2]
## Simulate mixture
nstates <- 2
delta <- c(0.7, 0.3)
mix <- sample(1:2, size = N, replace = TRUE, prob = delta)
## Simulate survey
cap <- data.frame(session = numeric(),
ID = numeric(),
occasion = numeric(),
trap = numeric())
seen <- rep(FALSE, N)
id <- rep(0, N)
obsmix <- NULL
for (k in 1:K) {
for (i in 1:N) {
d2 <- (x[i] - detectors[,1])^2 + (y[i] - detectors[,2])^2
er <- lambda0[mix[i]] * exp(-d2 / (2 * sigma[mix[i]]^2))
c <- rpois(length(er), er)
if (any(c > 0)) {
if (!seen[i]) {
id[i] <- max(id) + 1
seen[i] <- TRUE
obsmix <- c(obsmix, mix[i])
}
dets <- which(c > 0)
for (r in 1:length(dets)) {
nc <- c[dets[r]]
rec <- data.frame(session = rep(1, nc),
ID = rep(id[i], nc),
occasion = rep(k, nc),
trap = rep(dets[r], nc))
cap <- rbind(cap, rec)
}
}
}
}
if (max(cap$occasion) != K) cap <- rbind(cap, data.frame(session = 1, ID = "NONE", occasion = K, trap = 1))
ch <- make.capthist(cap, detectors)
# create data object
scrdat <- ScrData$new(ch, mesh = mesh)
# openpopscr fit ----------------------------------------------------------
## create state model
statemod <- StateModel$new(data = scrdat,
names = c("A", "B"),
structure = matrix(c(".", "0",
"0", "."), nr = 2, nc = 2, byrow = T),
start = list(delta = c(0.5, 0.5), tpm = diag(2)))
# create formulae
form <- list(lambda0 ~ 1,
sigma ~ state,
D ~ 1)
# get starting values for numerical optimiser
start <- get_start_values(scrdat)
# create the model object
obj <- ScrModel$new(form, scrdat, start, statemod = statemod)
# compute initial likelihood to see if start is reasonable
obj$calc_llk()
# fit model
obj$fit()
# see model results
obj
# get parameters on natural scale
obj$get_par("lambda0", k = 1, j = 1)
obj$get_par("sigma", k = 1, j = 1, s = 1)
obj$get_par("sigma", k = 1, j = 1, s = 2)
obj$get_par("D")
obj$state()$delta()
# predict state
pr <- obj$pr_state()
predstate <- matrix(0, nr = scrdat$n(), nc = 2)
for (i in 1:scrdat$n()) {
predstate[i,] <- colSums(pr[[i]][,,10])
}
print(cbind(round(predstate, 2), obsmix))
predmix <- rep(0, scrdat$n())
for (i in 1:scrdat$n()) predmix[i] <- which.max(predstate[i,])
sum(predmix == obsmix) / scrdat$n()
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