##### SCR transient model simulation
library(openpopscr)
# set number of threads for parallel processing
RcppParallel::setThreadOptions(numThreads = 1)
# setup simulations -------------------------------------------------------
set.seed(17417)
nsims <- 100
ests <- vector(mode = "list", length = nsims)
# set truth
true_par <- list(D = 1000, lambda0 = 0.5, sigma = 20, sd = 10)
# make detectors array
detectors <- make.grid(nx = 7, ny = 7, spacing = 20, detector = "count")
# make mesh
mesh <- make.mask(detectors, buffer = 100, nx = 64, ny = 64, type = "trapbuffer")
# set number of occasions to simulate
n_occasions <- 5
# create formulae
form <- list(lambda0 ~ 1,
sigma ~ 1,
sd ~ 1,
D ~ 1)
# simulation --------------------------------------------------------------
progbar <- utils::txtProgressBar(min = 0, max = nsims, style = 3)
for (sim in 1:nsims) {
## progress bar
Sys.sleep(0.1)
utils::setTxtProgressBar(progbar, sim)
# simulate ScrData
scrdat <- simulate_scr(true_par, n_occasions, detectors, mesh, move = TRUE, print = FALSE)
# get starting values for numerical optimiser
start <- list(lambda0 = 0.5, sigma = 20, sd = 10, D = 1000)
# create the model object
obj <- ScrTransientModel$new(form, scrdat, start, print = FALSE)
# fit model
obj$fit()
# store results
ests[[sim]] <- obj$estimates()$par
}
# extract results
mu <- exp(sapply(ests, FUN = function(x){x[,1]}))
lcl <- exp(sapply(ests, FUN = function(x){x[,3]}))
ucl <- exp(sapply(ests, FUN = function(x){x[,4]}))
# distributions
summary(t(mu))
# confidence interval coverage
sum(lcl[1,] < true_par$lambda0 & ucl[1,] > true_par$lambda0)
sum(lcl[2,] < true_par$sigma & ucl[2,] > true_par$sigma)
sum(lcl[3,] < true_par$sd & ucl[3,] > true_par$sd)
sum(lcl[4,] < true_par$D & ucl[4,] > true_par$D)
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