# data_kerinci: Times of 'capture' of large mammals In overlap: Estimates of Coefficient of Overlapping for Animal Activity Patterns

## Description

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

## Usage

 `1` ```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

## Source

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.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36``` ```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', ] ```

### Example output

```'data.frame':	1098 obs. of  3 variables:
\$ Zone: int  1 1 1 1 1 1 1 1 1 1 ...
\$ Sps : Factor w/ 8 levels "boar","clouded",..: 8 8 8 8 8 8 8 8 8 8 ...
\$ Time: num  0.175 0.787 0.247 0.591 0.5 0.564 0.41 0.538 0.633 0.758 ...
[1] 0.003 0.990
[1] 52
[1] 42
Dhat1     Dhat4     Dhat5
0.4849314 0.4751341 0.4752747
Dhat1
0.4849314
[1] 0.526295
lower     upper
0.3551243 0.6147385
lower     upper
0.3586399 0.6143508
```

overlap documentation built on May 17, 2021, 9:09 a.m.