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 |

`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

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Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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