boot_sd: Required for remsD bootstrapping

In timcdlucas/RandEM: Population density estimates via Random Encounter Models in R

Description

Resamples camera trap data to calculate variance for Random Encounter Model density estimates. Note that tm and v must be the same across all sites. If this is not the case, split your data before bootstrapping and run the function on each sub-group. Called by remsD. Calls bsD.

Usage

boot_sd(x, tm, v, nboots)

Arguments

x	bsD resampled data
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

Examples

## Define the number of bootstrapping iterations and apply boot_sd to the data:
tm <- 3600
v <- 0.85
nboots <- 1000

grpDat <- split_dat(hDat)
remsD <- lapply(grpDat, boot_sd, tm, v, nboots)
remsSD <- lapply(remsD, sd)
remsSD

timcdlucas/RandEM documentation built on May 29, 2019, 3:07 a.m.