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:
A number indicating which of four zones the record comes from.
A factor indicating which species was observed: boar (wild pig), clouded leopard, golden cat, macaque, muntjac, sambar deer, tapir, or tiger.
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") 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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