bootdht: Bootstrap uncertainty estimation for distance sampling models

In Distance: Distance Sampling Detection Function and Abundance Estimation

Description

Performs a bootstrap for simple distance sampling models using the same data structures as dht.

Usage

bootdht(
  model,
  flatfile,
  resample_strata = FALSE,
  resample_obs = FALSE,
  resample_transects = TRUE,
  nboot = 100,
  summary_fun = bootdht_Nhat_summarize,
  convert.units = 1,
  select_adjustments = FALSE,
  sample_fraction = 1,
  progress_bar = "base"
)

Arguments

model	a model fitted by ds or a list of models
flatfile	Data provided in the flatfile format. See flatfile for details.
resample_strata	should resampling happen at the stratum (Region.Label) level? (Default FALSE)
resample_obs	should resampling happen at the observation (object) level? (Default FALSE)
resample_transects	should resampling happen at the transect (Sample.Label) level? (Default TRUE)
nboot	number of bootstrap replicates
summary_fun	function that is used to obtain summary statistics from the bootstrap, see Summary Functions below. By default bootdht_Nhat_summarize is used, which just extracts abundance estimates.
convert.units	conversion between units for abundance estimation, see "Units", below. (Defaults to 1, implying all of the units are "correct" already.) This takes precedence over any unit conversion stored in model.
select_adjustments	select the number of adjustments in each bootstrap, when FALSE the exact detection function specified in model is fitted to each replicate. Setting this option to TRUE can significantly increase the runtime for the bootstrap. Note that for this to work model must have been fitted with adjustment!=NULL.
sample_fraction	what proportion of the transects was covered (e.g., 0.5 for one-sided line transects).
progress_bar	which progress bar should be used? Default "base" uses txtProgressBar, "none" suppresses output, "progress" uses the progress package, if installed.

Summary Functions

The function summary_fun allows the user to specify what summary statistics should be recorded from each bootstrap. The function should take two arguments, ests and fit. The former is the output from dht2, giving tables of estimates. The latter is the fitted detection function object. The function is called once fitting and estimation has been performed and should return a data.frame. Those data.frames are then concatenated using rbind. One can make these functions return any information within those objects, for example abundance or density estimates or the AIC for each model. See Examples below.

Model selection

Model selection can be performed on a per-replicate basis within the bootstrap. This has three variations:

1. when select_adjustments is TRUE then adjustment terms are selected by AIC within each bootstrap replicate (provided that model had the order and adjustment options set to non-NULL.

2. if model is a list of fitted detection functions, each of these is fitted to each replicate and results generated from the one with the lowest AIC.

3. when select_adjustments is TRUE and model is a list of fitted detection functions, each model fitted to each replicate and number of adjustments is selected via AIC.

The last of these options can be very time consuming!

See Also

summary.dht_bootstrap for how to summarize the results, bootdht_Nhat_summarize for an example summary function.

Examples

## Not run: 
# fit a model to the minke data
data(minke)
mod1 <- ds(minke)

# summary function to save the abundance estimate
Nhat_summarize <- function(ests, fit) {
  return(data.frame(Nhat=ests$individuals$N$Estimate))
}

# perform 5 bootstraps
bootout <- bootdht(mod1, flatfile=minke, summary_fun=Nhat_summarize, nboot=5)

# obtain basic summary information
summary(bootout)

## End(Not run)

Distance documentation built on Jan. 13, 2021, 10:43 p.m.