## Line transect examples
data(linetran)
ltUMF <- with(linetran, {
unmarkedFrameDS(y = cbind(dc1, dc2, dc3, dc4),
siteCovs = data.frame(Length, area, habitat),
dist.breaks = c(0, 5, 10, 15, 20),
tlength = linetran$Length * 1000, survey = "line", unitsIn = "m")
})
ltUMF
summary(ltUMF)
hist(ltUMF)
# Half-normal detection function. Density output (log scale). No covariates.
(fm1 <- distsamp(~ 1 ~ 1, ltUMF))
# Some methods to use on fitted model
summary(fm1)
backTransform(fm1, type="state") # animals / ha
exp(coef(fm1, type="state", altNames=TRUE)) # same
backTransform(fm1, type="det") # half-normal SD
hist(fm1, xlab="Distance (m)") # Only works when there are no det covars
# Empirical Bayes estimates of posterior distribution for N_i
plot(ranef(fm1, K=50))
# Effective strip half-width
(eshw <- integrate(gxhn, 0, 20, sigma=10.9)$value)
# Detection probability
eshw / 20 # 20 is strip-width
# Halfnormal. Covariates affecting both density and and detection.
(fm2 <- distsamp(~area + habitat ~ habitat, ltUMF))
# Hazard-rate detection function.
(fm3 <- distsamp(~ 1 ~ 1, ltUMF, keyfun="hazard"))
# Plot detection function.
fmhz.shape <- exp(coef(fm3, type="det"))
fmhz.scale <- exp(coef(fm3, type="scale"))
plot(function(x) gxhaz(x, shape=fmhz.shape, scale=fmhz.scale), 0, 25,
xlab="Distance (m)", ylab="Detection probability")
## Point transect examples
# Analysis of the Island Scrub-jay data.
# See Sillett et al. (In press)
data(issj)
str(issj)
jayumf <- unmarkedFrameDS(y=as.matrix(issj[,1:3]),
siteCovs=data.frame(scale(issj[,c("elevation","forest","chaparral")])),
dist.breaks=c(0,100,200,300), unitsIn="m", survey="point")
(fm1jay <- distsamp(~chaparral ~chaparral, jayumf))
if (FALSE) {
data(pointtran)
ptUMF <- with(pointtran, {
unmarkedFrameDS(y = cbind(dc1, dc2, dc3, dc4, dc5),
siteCovs = data.frame(area, habitat),
dist.breaks = seq(0, 25, by=5), survey = "point", unitsIn = "m")
})
# Half-normal.
(fmp1 <- distsamp(~ 1 ~ 1, ptUMF))
hist(fmp1, ylim=c(0, 0.07), xlab="Distance (m)")
# effective radius
sig <- exp(coef(fmp1, type="det"))
ea <- 2*pi * integrate(grhn, 0, 25, sigma=sig)$value # effective area
sqrt(ea / pi) # effective radius
# detection probability
ea / (pi*25^2)
}
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