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assess-approximation-D1.R
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### Comparing simulated and approximate CV(D) for two spatially-varying density surfaces
### Code is adapted from Efford and Boulanger (2019) MEE, for spatially-varying D.
runsim <- function (sp.ratio = seq(0.25,3.25,0.25), nx = 6, ny = 6, nrepeats = 1,
D = 10, lambda0 = 0.2, sigma = 20, nocc = 5, detector = 'proximity',
nrepl = 100, model.args = list(D ~ 1), seed = 123, ...) {
all_grids <- lapply(sp.ratio * sigma, make.grid, nx = nx, ny = ny, detector = detector, originxy = c(500,500))
all_masks <- lapply(all_grids, make.mask, spacing = sigma/2, type = 'trapbuffer', buffer = 4*sigma)
# add covariates
for(i in 1:length(all_masks)){
covariates(all_masks[[i]]) <- data.frame(D1 = D,
D2 = exp(0.5 * as.numeric(scale(sqrt(all_masks[[i]]$x * all_masks[[i]]$y)))),
D3 = exp(-0.5 * as.numeric(scale(sqrt((all_masks[[i]]$x-mean(all_masks[[i]]$x))^2 + (all_masks[[i]]$y-mean(all_masks[[i]]$y))^2)))))
}
sims <- list()
Dorig <- D
for(i in 1:length(all_masks)){
grids <- all_grids[[i]]
masks <- all_masks[[i]]
print(nrow(grids))
set.seed(seed)
# simulating directly from secr
if(grepl("D ~ 1", model.args)) { D <- Dorig }
if(grepl("D ~ D2", model.args)) { D <- covariates(masks)$D2 / sum(covariates(masks)$D2) * (Dorig * length(masks$x)) }
if(grepl("D ~ D3", model.args)) { D <- covariates(masks)$D3 / sum(covariates(masks)$D3) * (Dorig * length(masks$x)) }
mod_summary <- data.frame(j = as.integer(),
D = as.numeric(), se.D = as.numeric(),
E.N = as.numeric(), R.N = as.numeric(),
E.Nlcl = as.numeric(), R.Nlcl = as.numeric(),
E.Nucl = as.numeric(), R.Nucl = as.numeric(),
true.N = as.numeric(), n = as.numeric(),
detections = as.numeric(),
dets_visited = as.numeric())
cnt <- 1
for(j in 1:nrepl){ # run more when doing for real!
if(length(D) > 1) {
my_pop <- sim.popn(D = D, core = masks, buffer = 0, model2D = "IHP")
} else { my_pop <- sim.popn(D = D, core = masks, buffer = 0) }
ch <- sim.capthist(traps = grids, pop = my_pop,
noccasions = nocc,
detectpar = list(lambda0 = lambda0, sigma = sigma),
detectfn = "HHN")
# make starting values for secr.fit
startvals <- list(D = mean(D), lambda0 = lambda0, sigma = sigma)
mod <- try(secr.fit(capthist = ch, mask = masks, model = model.args,
detectfn = "HHN", start = startvals, trace = FALSE))
if(class(mod) != "try-error"){
rN <- try(region.N(mod))
if(class(rN) != "try-error"){
mod_summary <- rbind(mod_summary,
data.frame(j = j,
D = coef(mod)[,"beta"][1],
se.D = coef(mod)[,"SE.beta"][1],
E.N = region.N(mod)$estimate[1],
R.N = region.N(mod)$estimate[2],
E.Nlcl = region.N(mod)$lcl[1],
R.Nlcl = region.N(mod)$lcl[2],
E.Nucl = region.N(mod)$ucl[1],
R.Nucl = region.N(mod)$ucl[2],
true.N = nrow(my_pop),
n = summary(ch)[[4]][2,"Total"],
detections = summary(ch)[[4]][6,"Total"],
dets_visited = summary(ch)[[4]][7,"Total"]))
cnt <- cnt + 1
}
}
}
# can be fitting errors, remove any dodgy obs (this is subjective)
# mod_summary <- mod_summary %>% filter(E.N < 4 * true.N)
mod_summary$r <- mod_summary$detections - mod_summary$n
# add theoretical values
th <- Enrm(D = D, grids, masks, list(lambda0 = lambda0, sigma = sigma), noccasions = nocc)
mod_summary$th_En <- th[1]
mod_summary$th_Er <- th[2]
mod_summary$th_Em <- th[3]
mod_summary$th_cvD <- 1 / sqrt(min(th[1], th[2]))
# add input variables
mod_summary$nT <- nrow(grids)
mod_summary$sigma <- sigma
mod_summary$lambda0 <- lambda0
mod_summary$dt <- detector
mod_summary$sp.ratio <- summary(grids)$spacing / sigma
sims[[i]] <- mod_summary
}
do.call(rbind, sims)
}
########################################################################################
## setup
library(secrdesign)
spr <- seq(0.25, 3.25, 0.5)
nrepl <- 100
#############################
## Simulations
## Uniform D
sim66_D1 <- runsim(sp.ratio = spr, nx = 6, ny = 6, nrepl = nrepl, model.args = list(D ~ 1), seed = 123)
sim88_D1 <- runsim(sp.ratio = spr, nx = 8, ny = 8, nrepl = nrepl, model.args = list(D ~ 1), seed = 123)
sim1010_D1 <- runsim(sp.ratio = spr, nx = 10, ny = 10, nrepl = nrepl, model.args = list(D ~ 1), seed = 123)
sim1010.05_D1 <- runsim(sp.ratio = spr, nx = 10, ny = 10, nrepl = nrepl, model.args = list(D ~ 1), lambda0 = 0.05, seed = 123)
sim1010.1_D1 <- runsim(sp.ratio = spr, nx = 10, ny = 10, nrepl = nrepl, model.args = list(D ~ 1), lambda0 = 0.1, seed = 123)
## Spatially-varying D
sim66_D2 <- runsim(sp.ratio = spr, nx = 6, ny = 6, nrepl = nrepl, model.args = list(D ~ D2), seed = 123)
sim66_D3 <- runsim(sp.ratio = spr, nx = 6, ny = 6, nrepl = nrepl, model.args = list(D ~ D3), seed = 123)
sim88_D2 <- runsim(sp.ratio = spr, nx = 8, ny = 8, nrepl = nrepl, model.args = list(D ~ D2), seed = 123)
sim88_D3 <- runsim(sp.ratio = spr, nx = 8, ny = 8, nrepl = nrepl, model.args = list(D ~ D3), seed = 123)
sim1010_D2 <- runsim(sp.ratio = spr, nx = 10, ny = 10, nrepl = nrepl, model.args = list(D ~ D2), seed = 123)
sim1010_D3 <- runsim(sp.ratio = spr, nx = 10, ny = 10, nrepl = nrepl, model.args = list(D ~ D3), seed = 123)
sim1010.05_D2 <- runsim(sp.ratio = spr, nx = 10, ny = 10, nrepl = nrepl, model.args = list(D ~ D2), lambda0 = 0.05, seed = 123)
sim1010.05_D3 <- runsim(sp.ratio = spr, nx = 10, ny = 10, nrepl = nrepl, model.args = list(D ~ D3), lambda0 = 0.05, seed = 123)
sim1010.1_D2 <- runsim(sp.ratio = spr, nx = 10, ny = 10, nrepl = nrepl, model.args = list(D ~ D2), lambda0 = 0.1, seed = 123)
sim1010.1_D3 <- runsim(sp.ratio = spr, nx = 10, ny = 10, nrepl = nrepl, model.args = list(D ~ D3), lambda0 = 0.1, seed = 123)
simlist <- list(g66 = rbind(sim66_D1,sim66_D2,sim66_D3),
g88 = rbind(sim88_D1,sim88_D2,sim88_D3),
g1010 = rbind(sim1010_D1,sim1010_D2,sim1010_D3),
g1010.1 = rbind(sim1010.1_D1,sim1010.1_D2,sim1010.1_D3),
g1010.05 = rbind(sim1010.05_D1,sim1010.05_D2,sim1010.05_D3))
#save(simlist, file = "output/approxchecks/simlist-fullsim.Rdata")