bootEst: Functions to generate bootstrap estimates of overlap

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/bootEst.R

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

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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,
      type=c("all", "Dhat1", "Dhat4", "Dhat5"), cores=1)

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.

type

the name of the estimator to use, or "all" to produce all three estimates. See overlapEst for recommendations on which to use.

cores

the number of cores to use for parallel processing. If NA, all but one of the available cores will used. Parallel processing may take longer than serial processing if the bootstrap runs quickly.

Details

bandwidth adjustment:

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 with type = "all" returns a numeric matrix with three columns, one for each estimator of overlap, otherwise a vector of bootstrap estimates. If bandwidth estimation fails for a bootstrap sample, the corresponding value will be NA.

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

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data(simulatedData)
tigSim <- resample(tigerObs, 99)
dim(tigSim)

pigSim <- resample(pigObs, 99)
boots <- bootEst(tigSim, pigSim)
colMeans(boots)
# or just do Dhat4
boots <- bootEst(tigSim, pigSim, type="Dhat4")
mean(boots)
# parallel processing takes longer for this example
boots <- bootEst(tigSim, pigSim, type="Dhat4", cores=2)
mean(boots)

overlap documentation built on May 29, 2017, 8:05 p.m.