inst/simulations/08_js_basic.R

In r-glennie/openpopscr: Open Population Spatial Capture-Recapture

#### Basic JS simulation
library(openpopscr)
# set number of threads for parallel processing 
RcppParallel::setThreadOptions(numThreads = 30)

# setup simulations -------------------------------------------------------

set.seed(184821)
nsims <- 100 
ests <- dens <- vector(mode = "list", length = nsims)

# set truth 
true_par <- list(D = 1000, lambda0 = 1, sigma = 30, phi = 0.5, beta = 0.3)

# 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, 
             beta ~ 1, 
             phi ~ 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_js_openscr(true_par, n_occasions, detectors, mesh, print = FALSE)
  
  # get starting values for numerical optimiser  
  start <- get_start_values(scrdat, model = "JsModel")
  
  # create the model object 
  obj <- JsModel$new(form, scrdat, start, print = FALSE)
  
  # fit model
  obj$fit()
  
  # store parameter results
  ests[[sim]] <- obj$estimates()$par
  
  # store density results 
  dens[[sim]] <- obj$estimates()$D

}

## PARAMETERS
# extract results 
mu <- sapply(ests, FUN = function(x){x[,1]})
lcl <- sapply(ests, FUN = function(x){x[,3]})
ucl <- sapply(ests, FUN = function(x){x[,4]})
# distributions 
summary(t(mu))
# confidence interval coverage 
sum(lcl[1,] < log(true_par$lambda0) & ucl[1,] > log(true_par$lambda0))
sum(lcl[2,] < log(true_par$sigma) & ucl[2,] > log(true_par$sigma))
sum(lcl[3,] < qlogis(true_par$phi) & ucl[3,] > qlogis(true_par$phi))
b <- (1 - true_par$beta) / (n_occasions - 1)
b <- log(b/true_par$beta)
sum(lcl[4,] < b & ucl[4,] > b)
sum(lcl[5,] < log(true_par$D) & ucl[5,] > log(true_par$D))

## DENSITY 
trueD <- rep(0, n_occasions)
trueD[1] <- true_par$D * true_par$beta
b <- (1 - true_par$beta) / (n_occasions - 1) 
for (i in 2:n_occasions) trueD[i] <- trueD[i - 1] * true_par$phi + b * true_par$D
ci <- rep(0, n_occasions)
for (i in 1:n_occasions) {
  lcls <- sapply(dens, FUN = function(x){x[i, 3]})
  ucls <- sapply(dens, FUN = function(x){x[i, 4]})
  ci[i] <- sum(lcls < trueD[i] & trueD[i] < ucls)
}