specleanr (version 1.0.0)

multiabsolute: Identifies absolute outliers for multiple species.

Description

Identifies absolute outliers for multiple species.

Usage

multiabsolute(
  x,
  threshold = NULL,
  props = FALSE,
  warn = FALSE,
  autothreshold = FALSE
)

Value

vector or absolute outliers, best outlier detection method or data frame of absolute outliers and their proportions

Arguments

x

datacleaner class for each methods used to identify outliers in multidetect function.

threshold

numeric. Maximum value to denote an absolute outlier. The threshold ranges from 0, which indicates a point has not been flagged by any outlier detection method as an outlier, to 1, which means the record is an absolute or true outlier since all methods have identified it. At both extremes, many records are classified at low threshold values, which may be due to individual method weakness or strength and data distribution. Also, at higher threshold values, the true outliers are retained. For example, if ten methods are considered and 9 methods flag a record as an outlier, If a cutoff of 1 is used, then that particular record is retained. Therefore, the default cutoff is 0.6, but autothreshold can be used to select the appropriate threshold.

props

dataframe. To output the proportional absoluteness for each outlier.

warn

logical. If TRUE, warning on whether absolute outliers obtained at a low threshold is indicated. Default TRUE.

autothreshold

vector. 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.

See Also

ocindex

Examples

# \donttest{

data(efidata)

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"))

extdf <- 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 = extdf, output='outlier', var = 'bio6',
                          exclude = c('x','y'), multiple = TRUE,
                          methods = c('mixediqr', "iqr", "mahal", "iqr", "logboxplot"))

totabs_counts <- multiabsolute(x = outliersdf, threshold = 0.2)

# }