specleanr (version 1.0.0)

multibestmethod: 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
)

Value

best method for outlier detection for each species

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.

Examples

# \donttest{


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)#
# }