multibestmethod: Identify best method for outlier removal for multiple species...

In specleanr: Detecting Environmental Outliers in Data Analysis Pipelines

Identify best method for outlier removal for multiple species using majority votes.

Description

Identify best method for outlier removal for multiple species using majority votes.

Usage

multibestmethod(
  x,
  threshold = NULL,
  warn = FALSE,
  verbose = FALSE,
  autothreshold = FALSE
)

Arguments

x	Output from the outlier detection.
threshold	value to consider whether the outlier is an absolute outlier or not.
warn	If TRUE, warning on whether absolute outliers obtained at a low threshold is indicated. Default FALSE.
verbose	Produce messages on the process or not. Default FALSE.
autothreshold	Identifies the threshold with mean number of absolute outliers.The search is limited within 0.51 to 1 since thresholds less than are deemed inappropriate for identifying absolute outliers. The autothreshold is used when threshold is set to NULL.

Value

best method for outlier detection for each species

Examples

danube <- system.file('extdata/danube.shp.zip', package='specleanr')

db <- sf::st_read(danube, quiet=TRUE)

wcd <- terra::rast(system.file('extdata/worldclim.tiff', package = "specleanr"))

preddata <- pred_extract(data = efidata, raster = wcd,
                      lat = 'decimalLatitude', lon = 'decimalLongitude',
                     colsp = 'scientificName',
                     list = TRUE,verbose = FALSE,
                     minpts = 6,merge = FALSE)#'basin removed

 #outlier detection

outliersdf <- multidetect(data = preddata, multiple = TRUE,
                      var = 'bio6',
                      output = 'outlier',
                      exclude = c('x','y'),
                      methods = c('zscore', 'adjbox','iqr', 'semiqr','hampel', 'kmeans',
                                  'logboxplot', 'lof','iforest', 'mahal', 'seqfences'))

multbm <- multibestmethod(x = outliersdf, threshold = 0.2)#

specleanr documentation built on Nov. 26, 2025, 1:07 a.m.