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
.
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x |
a numeric vector of time-of-capture data in radians, ie. on [0, 2π] 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 |
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. |
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.
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.
Mike Meredith, including code by Martin Ridout.
Ridout & Linkie (2009) Estimating overlap of daily activity patterns from camera trap data. Journal of Agricultural, Biological, and Environmental Statistics 14:322-337
bootCI
.
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Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
All documentation is copyright its authors; we didn't write any of that.