overlap (version 0.3.9)

Example data: Times of 'capture' of large mammals

Description

Times of capture of large mammals in camera traps in Kerinci Seblat National Park, Indonesia.

Usage

data(kerinci)

Format

A data frame with 1098 rows and three columns:

Zone

A number indicating which of four zones the record comes from.

Sps

A factor indicating which species was observed: boar (wild pig), clouded leopard, golden cat, macaque, muntjac, sambar deer, tapir, or tiger.

Time

The time of the observation on a scale of 0 to 1, where 0 and 1 both correspond to midnight

Examples

data(kerinci)
str(kerinci)
# Time is in days, ie. 0 to 1:
range(kerinci$Time)
# Convert to radians:
timeRad <- kerinci$Time * 2*pi

# Extract data for tiger and tapir for Zone3:
spsA <- timeRad[kerinci$Zone == 3 & kerinci$Sps == 'tiger']
spsB <- timeRad[kerinci$Zone == 3 & kerinci$Sps == 'tapir']

# Plot the data:
overlapPlot(spsA, spsB)  # Tapir are mainly nocturnal
overlapPlot(spsA, spsB, xcenter="midnight")
legend('topleft', c("Tiger", "Tapir"), lty=c(1, 2), col=c("black", "blue"), bty='n')

# Check sample sizes:
length(spsA)
length(spsB)
# If the smaller sample is less than 50, Dhat1 gives the best estimates, together with
# confidence intervals from a smoothed bootstrap with norm0 or basic0 confidence interval.

# Calculate estimates of overlap:
( Dhats <- overlapEst(spsA, spsB) )  # or just get Dhat1
( Dhat1 <- overlapEst(spsA, spsB, type="Dhat1") )

# Do 999 smoothed bootstrap values:
bs <- bootstrap(spsA, spsB, 999, type="Dhat1", cores=1)
mean(bs)
hist(bs)
abline(v=Dhat1, col='red', lwd=2)
abline(v=mean(bs), col='blue', lwd=2, lty=3)

# Get confidence intervals:
bootCI(Dhat1, bs)['norm0', ]
bootCI(Dhat1, bs)['basic0', ]