data(linetran)
(dbreaksLine <- c(0, 5, 10, 15, 20))
lengths <- linetran$Length
ltUMF <- with(linetran, {
unmarkedFrameDS(y = cbind(dc1, dc2, dc3, dc4),
siteCovs = data.frame(Length, area, habitat), dist.breaks = dbreaksLine,
tlength = lengths*1000, survey = "line", unitsIn = "m")
})
# Fit a model
(fm <- distsamp(~area ~habitat, ltUMF))
# Function returning three fit-statistics.
fitstats <- function(fm, na.rm=TRUE) {
observed <- getY(fm@data)
expected <- fitted(fm)
resids <- residuals(fm)
sse <- sum(resids^2, na.rm=na.rm)
chisq <- sum((observed - expected)^2 / expected, na.rm=na.rm)
freeTuke <- sum((sqrt(observed) - sqrt(expected))^2, na.rm=na.rm)
out <- c(SSE=sse, Chisq=chisq, freemanTukey=freeTuke)
return(out)
}
(pb <- parboot(fm, fitstats, nsim=25, report=1))
plot(pb, main="")
# Finite-sample inference for a derived parameter.
# Population size in sampled area
Nhat <- function(fm) {
sum(bup(ranef(fm, K=50)))
}
set.seed(345)
(pb.N <- parboot(fm, Nhat, nsim=25, report=5))
# Compare to empirical Bayes confidence intervals
colSums(confint(ranef(fm, K=50)))
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