boot_area: Cumulative analysis of collective areas by bootstrapping

Description Usage Arguments Details Value Author(s) References Examples

View source: R/boot_area.R

Description

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

Usage

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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)

Takahiro Shimada

References

Shimada T, Thums M, Hamann M, Limpus CJ, Hays GC, FitzSimmons N, Wildermann NE, Duarte CD, Meekan MG (2021) Optimising sample sizes for animal distribution analysis using tracking data. Methods in Ecology and Evolution 12(2):288-297 doi: 10.1111/2041-210X.13506

Examples

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## 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)

SDLfilter documentation built on July 20, 2021, 9:07 a.m.