# boot_area: Cumulative analysis of collective areas by bootstrapping In SDLfilter: Filtering and Assessing the Sample Size of Tracking Data

## Description

Function to calculate collective areas (merged x% Utilisation Distributions or UDs) of n individuals by bootstrapping.

## Usage

 ```1 2 3 4 5 6 7``` ```boot_area( data, cell.size = NA, R = 1000, percent = 50, quantiles = c(0.25, 0.5, 0.75) ) ```

## Arguments

 `data` A matrix or list of RasterLayer objects. Each row of the matrix or each RasterLayer object contains a utilisation distribution (or other statistics that sums to 1 - e.g. proportion of time spent). The grid size and geographical extent must be consistent across each row of the matrix or each RasterLayer object. The function assumes that each column of the matrix is associated with a unique geographical location or that each RasterLayer has exactly the same geographical extent and resolution. `cell.size` A numeric value specifying the grid cell size of the input data in metres. `R` An integer specifying the number of iterations. A larger R is required when the sample size is large. R = sample size x 200 is often sufficient (e.g. R = 2000 for a sample size 10). `percent` An integer specifying the percent volume of each UD to be considered in the analysis. `quantiles` A vector or a number to specify the quantiles to be calculated in the summary of the results.

## Details

This function calculates collective areas (e.g. 50% UDs) of 1 to n individuals by bootstrapping.

## Value

A list containing two data frames - raw results and summary (mean, sd, sem and quantiles at each sample size).

## Author(s)

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```## Not run: #1 Utilisation distributions of flatback turtles (n = 29). data(ud_matrix) #2 Calculate collective areas from 6000 random permutation area <- boot_area(ud_matrix, R = 6000, percent = 50) #3 Find the minimum sample size required to estimate the general distribution. a <- asymptote(area) #4 Plot the mean collective area and rational function fit relative to the sample sizes. ggplot(data = area\$summary)+ geom_point(aes(x = N, y = mu/1e+6), alpha = 0.5) + geom_path(data = a\$results, aes(x = x, y = ys/1e+6)) + labs(x = "N", y = expression(Area~(km^2))) ## End(Not run) ```