Example data | R Documentation |

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

```
data(kerinci)
```

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

Ridout, M.S. and Linkie, M. (2009) Estimating overlap of daily activity patterns from camera trap data. *Journal of Agricultural, Biological and Environmental Statistics*, 14, 322-337.

https://www.kent.ac.uk/smsas/personal/msr/overlap.html

```
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', ]
```

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