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

# \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)#
# }