weib_percentile_ci {phenesse}

Calculating the CIs of a percentile estimate of a seasonal abundance distribution using the non-parametric bootstrapping.

Description

weib_percentile_ci uses non-parametric bootstrapping from the boot package to estimate 95

Usage

weib_percentile_ci(
  observations,
  iterations,
  percentile,
  bootstraps,
  type = "perc",
  conf = 0.95,
  parallelize = "no",
  ncpus = getOption("boot.ncpus", 1L),
  cl = NULL
)

Arguments

observations

is a vector of dates/time of observations given as numeric values

iterations

is the number of iterations you want to run to create empirical bootstrapping to estimate bias of original CDF. The bias is used to calculate a bias corrected estimate of the percentile bound.

percentile

is the percentile of the cumulative distribution function of interest

bootstraps

is the number of bootstraps you want to run to create the CIs

type

A vector of character strings representing the type of intervals required to calculate the CI. Defaults to "perc". See ??boot.ci for more information.

conf

The confidence level wanted. Defaults to 95% CI.

parallelize

The type of parallel operation to be used (if any). If missing, the default is that no parallelization will occur. Parallelization options are "multicore" and "snow"

ncpus

An integer that represents the number of processes to be used in parallel operation.

cl

An optional parallel or snow cluster for use if parallel = "snow". If not supplied, a cluster on the local machine is created for the duration of the boot call.

Value

The Weibull-corrected estimate of the percentile of interest and CIs.

Examples

# Gather sightings of iNaturalist observations for four species:
# Danaus plexippus, Speyeria cybele, Rudbeckia hirta, and Asclepias syriaca

# Estimate when the first 50 percent of individuals of the butterfly species
# Speyeria cybele are in flight. Note, only 10 iterations are beingg used
# in this example to keep computation time low, but more iterations should
# be used to get a more stable result. See vignette for parallelization
# options.


data(inat_examples)
s_cybele <- subset(inat_examples, scientific_name == "Speyeria cybele")
weib_percentile_ci(observations = s_cybele$doy, iterations = 10,
                   percentile = 0.5, bootstraps = 100)