Functions to generate bootstrap estimates of overlap

Description

The strategy implemented involves three steps: 1. Create a matrix of bootstrap samples for each data set, using resample. 2. Use bootEst to calculate estimates for each bootstrap sample and create a matrix of bootstrap estimates. 3. Process the bootstrap estimates, eg. to produce confidence intervals with bootCI.

Usage

1
2
3
resample(x, nb, smooth = TRUE, kmax = 3, adjust = 1, n.grid = 512)

bootEst(Amat, Bmat, kmax = 3, adjust=c(0.8, 1, 4), n.grid = 128)

Arguments

x

a numeric vector of time-of-capture data in radians, ie. on [0, ] scale

nb

the number of bootstrap samples required

smooth

if TRUE, smoothed bootstrap samples are produced.

Amat, Bmat

matrices of resampled data for each species produced by resample; see Value below.

kmax

maximum value of k for optimal bandwidth estimation.

adjust

bandwidth adjustment: see Details.

n.grid

number of points at which to estimate density for comparison between species; smaller values give lower precision but run faster in bootstraps.

Details

bandwidth adjustment:

  • for resample, a single value.

  • for bootEst, either a single value used for all 3 overlap estimates, or a vector of 3 different values; a NA value in adjust means that the corresponding estimate will not be calculated.

Value

The function resample returns a numeric matrix with each column corresponding to a bootstrap sample. Times are in radians. It may return a matrix of NAs if smooth = TRUE and bandwidth estimation fails.

Function bootEst returns a numeric matrix with three columns, one for each estimator of overlap. If argument adjust contains NAs, the corresponding columns in the output will be NAs. If bandwidth estimation fails for a bootstrap sample, the corresponding row will contain NAs.

Author(s)

Mike Meredith, including code by Martin Ridout.

References

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

See Also

bootCI.

Examples

1
2
3
4
5
6
7
data(simulatedData)
tigSim <- resample(tigerObs, 99)
dim(tigSim)

pigSim <- resample(pigObs, 99)
boots <- bootEst(tigSim, pigSim)
colMeans(boots)