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context("IDS fitting function")
skip_on_cran()
test_that("IDS can fit models with covariates", {
set.seed(123)
formulas <- list(lam=~elev, ds=~1, phi=~1)
# Based on values from real data
design <- list(Mds=1000, J=6, Mpc=300)
# Based on values from real data
coefs <- list(lam = c(intercept=3, elev=-0.5),
ds = c(intercept=-2.5),
phi = c(intercept=-1.3))
# Survey durations, loosely based on real data
durs <- list(ds = rep(5, design$Mds), pc=runif(design$Mpc, 3, 30))
sim_umf <- simulate("IDS", # name of model we are simulating for
nsim=1, # number of replicates
formulas=formulas,
coefs=coefs,
design=design,
# arguments used by unmarkedFrameDS
dist.breaks = seq(0, 0.30, length.out=7),
unitsIn="km",
# arguments used by IDS
# could also have e.g. keyfun here
durationDS=durs$ds, durationPC=durs$pc, durationOC=durs$oc,
maxDistPC=0.5, maxDistOC=0.5,
unitsOut="kmsq")
set.seed(123)
mod_sim <- IDS(lambdaformula = ~elev, detformulaDS = ~1,
dataDS=sim_umf$ds, dataPC=sim_umf$pc,
availformula = ~1, durationDS=durs$ds, durationPC=durs$pc,
maxDistPC=0.5, maxDistOC=0.5,
unitsOut="kmsq")
expect_equivalent(coef(mod_sim), c(3.0271179,-0.4858101,-2.5050244,-1.3729505), tol=1e-5)
# Predict
pr <- predict(mod_sim, type = 'lam')
expect_equal(nrow(pr), 1000) # predicts only for the distance sampled sites
pr <- predict(mod_sim, type = 'lam', newdata=sim_umf$pc)
pr <- predict(mod_sim, type = 'ds')
expect_equal(nrow(pr), 1000)
expect_equal(pr$Predicted[1], exp(-2.5), tol=0.05)
pr <- predict(mod_sim, type = 'pc')
expect_equal(nrow(pr), 300)
expect_equal(pr$Predicted[1], exp(-2.5), tol=0.05)
pr <- predict(mod_sim, type = 'phi')
expect_equal(pr$ds$Predicted[1], exp(-1.37), tol=0.05)
# residuals
r <- residuals(mod_sim)
expect_equal(lapply(r, dim), list(ds=c(1000,6), pc = c(300,1)))
# parboot
pb <- parboot(mod_sim, nsim=2)
pdf(NULL)
plot(mod_sim)
hist(mod_sim)
dev.off()
expect_error(nonparboot(mod_sim))
expect_error(ranef(mod_sim))
# Separate detection models
mod_sep <- IDS(lambdaformula = ~elev, detformulaDS = ~1, detformulaPC = ~1,
dataDS=sim_umf$ds[1:100,], dataPC=sim_umf$pc[1:100,],
availformula = ~1, durationDS=durs$ds[1:100], durationPC=durs$pc[1:100],
maxDistPC=0.5, maxDistOC=0.5,
unitsOut="kmsq")
expect_equal(length(coef(mod_sim)), 4)
expect_equal(length(coef(mod_sep)), 5)
})
test_that("IDS can fit models with occupancy data", {
set.seed(123)
formulas <- list(lam=~elev, ds=~1, oc=~1)
# Based on values from real data
design <- list(Mds=100, J=6, Mpc=100, Moc=100)
# Based on values from real data
coefs <- list(lam = c(intercept=3, elev=-0.5),
ds = c(intercept=-2.5),
oc = c(intercept = -2))
sim_umf <- simulate("IDS", # name of model we are simulating for
nsim=1, # number of replicates
formulas=formulas,
coefs=coefs,
design=design,
# arguments used by unmarkedFrameDS
dist.breaks = seq(0, 0.30, length.out=7),
unitsIn="km",
# arguments used by IDS
# could also have e.g. keyfun here
maxDistPC=0.5, maxDistOC=0.5,
unitsOut="kmsq")
mod_oc <- IDS(lambdaformula = ~elev, detformulaDS = ~1, detformulaOC = ~1,
dataDS=sim_umf$ds, dataPC=sim_umf$pc, dataOC=sim_umf$oc,
maxDistPC=0.5, maxDistOC=0.5,
unitsOut="kmsq")
expect_equivalent(coef(mod_oc), c(3.0557091, -0.4200719, -2.5384331, -2.0610341),
tol=1e-5)
pr <- predict(mod_oc, type='oc')
expect_equal(pr$Predicted[1], 0.1273222, tol=1e-5)
res <- residuals(mod_oc)
expect_equal(lapply(res, dim), list(ds=c(100,6), pc = c(100,1), oc=c(100,1)))
pb <- parboot(mod_oc, nsim=1)
# Don't estimate availability if OC data
expect_error(
mod_oc <- IDS(lambdaformula = ~elev, detformulaDS = ~1, detformulaOC = ~1,
availformula = ~1,
dataDS=sim_umf$ds, dataPC=sim_umf$pc, dataOC=sim_umf$oc,
maxDistPC=0.5, maxDistOC=0.5,
durationDS=durs$ds, durationPC=durs$pc, durationOC=durs$pc,
unitsOut="kmsq")
)
# Just occupancy data
mod_oc <- IDS(lambdaformula = ~elev, detformulaDS = ~1, detformulaOC = ~1,
dataDS=sim_umf$ds, dataOC=sim_umf$oc,
maxDistOC=0.5,
unitsOut="kmsq")
expect_equal(names(mod_oc), c("lam","ds","oc"))
})
test_that("IDS handles missing values", {
set.seed(123)
design <- list(Mds=100, J=6, Mpc=100, Moc=100)
formulas <- list(lam=~elev, ds=~1, phi=~1)
# Based on values from real data
coefs <- list(lam = c(intercept=3, elev=-0.5),
ds = c(intercept=-2.5),
phi = c(intercept=-1.3))
# Survey durations, loosely based on real data
durs <- list(ds = rep(5, design$Mds), pc=runif(design$Mpc, 3, 30))
sim_umf <- simulate("IDS", # name of model we are simulating for
nsim=1, # number of replicates
formulas=formulas,
coefs=coefs,
design=design,
# arguments used by unmarkedFrameDS
dist.breaks = seq(0, 0.30, length.out=7),
unitsIn="km",
# arguments used by IDS
# could also have e.g. keyfun here
maxDistPC=0.5, maxDistOC=0.5,
unitsOut="kmsq")
sim_umf$pc@y[1,1] <- NA
sim_umf$pc@y[2,] <- NA
sim_umf$oc@y[1,1] <- NA
sim_umf$oc@y[2,] <- NA
expect_warning(
mod_sim <- IDS(lambdaformula = ~elev, detformulaDS = ~1, detformulaOC = ~1,
dataDS=sim_umf$ds, dataPC=sim_umf$pc, dataOC=sim_umf$oc,
maxDistPC=0.5, maxDistOC=0.5,
unitsOut="kmsq")
)
expect_equivalent(coef(mod_sim), c(2.9354934,-0.4759405,-2.5314594,-2.3259133),
tol=1e-5)
sim_umf$ds@y[1,1] <- NA
sim_umf$ds@y[2,] <- NA
expect_error(
expect_warning(
mod_sim <- IDS(lambdaformula = ~elev, detformulaDS = ~1, detformulaOC = ~1,
dataDS=sim_umf$ds, dataPC=sim_umf$pc, dataOC=sim_umf$oc,
maxDistPC=0.5, maxDistOC=0.5,
unitsOut="kmsq")
))
})
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