remBoot: Random Encounter Model bootstrapping for error terms

In arcaravaggi/remBoot: Random Encounter Modelling for Density Estimation and Variance

Description

This function allows the automated calculation of variance for animal densities estimates derived from Random Encounter Modelling.

Usage

remBoot(.df, tm, v, nboots, error_stat)

Arguments

.df	(data frame)
tm	(numeric) The total number of hours all cameras were left in-situ at a focal site
v	(numeric) The distance travelled by the focal species in 24 hours, in kilometres
nboots	(numeric) The number of bootstrap iterations
error_stat	(text) Variance, given as confidence intervals (ci) or standard deviation (sd)

Details

The function assumes that the first 4 columns of the dataset contain: 1) An identifying number for each survey location (e.g. 1, 2, 3) 2) The number of individuals of the focal species observed in each capture 3) The radial distance to the detected animal in each capture, given in metres 4) The angle of detection based on the location of the detected animal in each capture, given in radians

For an example of how to use remBoot, click (here).

Examples

## Define tm, v and nboots and pass the values to the function:

data(hDat)  

tm <- 1800
v <- 0.89
nboots <- 1000

output <- remBoot(hDat, tm, v, nboots, error_stat = c("sd"))

## Alternatively, pass the values to the function, directly:

output <- remBoot(hDat, tm = 1800, v = 0.89, nboots = 1000, error_stat = c("ci"))

arcaravaggi/remBoot documentation built on May 10, 2019, 12:49 p.m.