Variance estimation via parametric moving block bootstrap

This function is deprecated, use dsm_var_movblk.

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).