Skip to contents

Variance estimation via parametric moving block bootstrap

Source: R/dsm_var_movblk.R
dsm_var_movblk.Rd

Estimate the variance in abundance over an area using a moving block bootstrap. Two procedures are implemented, one incorporating detection function uncertainty, one not.

Usage

dsm_var_movblk(
  dsm.object,
  pred.data,
  n.boot,
  block.size,
  off.set,
  ds.uncertainty = FALSE,
  samp.unit.name = "Transect.Label",
  progress.file = NULL,
  bs.file = NULL,
  bar = TRUE
)

Arguments

dsm.object

object returned from dsm.

pred.data

either: a single prediction grid or list of prediction grids. Each grid should be a data.frame with the same columns as the original data.

n.boot

number of bootstrap resamples.

block.size

number of segments in each block.

off.set

a a vector or list of vectors with as many elements as there are in pred.data. Each vector is as long as the number of rows in the corresponding element of pred.data. These give the area associated with each prediction cell. If a single number is supplied it will be replicated for the length of pred.data.

ds.uncertainty

incorporate uncertainty in the detection function? See Details, below. Note that this feature is EXPERIMENTAL at the moment.

samp.unit.name

name sampling unit to resample (default 'Transect.Label').

progress.file

path to a file to be used (usually by Distance) to generate a progress bar (default NULL – no file written).

bs.file

path to a file to store each bootstrap round. This stores all of the bootstrap results rather than just the summaries, enabling outliers to be detected and removed. (Default NULL).

bar

should a progress bar be printed to screen? (Default TRUE).

Details

Setting ds.uncertainty=TRUE will incorporate detection function uncertainty directly into the bootstrap. This is done by generating observations from the fitted detection function and then re-fitting a new detection function (of the same form), then calculating a new effective strip width. Rejection sampling is used to generate the observations (except in the half-normal case) so the procedure can be rather slow. Note that this is currently not supported with covariates in the detection function.

Setting ds.uncertainty=FALSE will incorporate detection function uncertainty using the delta method. This assumes that the detection function and the spatial model are INDEPENDENT. This is probably not reasonable.

Examples

if (FALSE) { # \dontrun{
library(Distance)
library(dsm)

# load the Gulf of Mexico dolphin data (see ?mexdolphins)
data(mexdolphins)

# fit a detection function and look at the summary
hr.model <- ds(distdata, truncation=6000,
               key = "hr", adjustment = NULL)
summary(hr.model)

# fit a simple smooth of x and y
mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata)
summary(mod1)

# calculate the variance by 500 moving block bootstraps
mod1.movblk <- dsm_var_movblk(mod1, preddata, n.boot = 500,
   block.size = 3, samp.unit.name = "Transect.Label",
   off.set = preddata$area,
   bar = TRUE, bs.file = "mexico-bs.csv", ds.uncertainty = TRUE)
} # }