Flags outliers using kmeans clustering method

Flags outliers using kmeans clustering method

Usage

xkmeans(
  data,
  k,
  exclude = NULL,
  output,
  mode = "soft",
  method = "silhouette",
  seed = 1135,
  verbose = FALSE,
  pc = FALSE,
  boot = FALSE,
  var,
  pcvar = NULL
)

Arguments

data

Dataframe to check for outliers

k

The number of clusters to be used for optimization. It should be greater than 1. For many species k should be be greater 10 to ably cater for each species search for optimal k using the different optimization methods in kmethod

exclude

Exclude variables that should not be considered in the fitting the one class model, for example x and y columns or latitude/longitude or any column that the user doesn't want to consider.

output

Either clean: for a data set with no outliers or outlier: to output a data frame with outliers.

mode

Either robust, if a robust mode is used which uses median instead of mean and median absolute deviation from median.

method

The method to be used for the kmeans clustering. Default is silhouette. Elbow method can be used but user input is required, and therefore multiple outlier detection method is not possible.

seed

An integer to fix the maintain the iterations by during the kmeans method optimisation.

verbose

To indicate messages and the default is FALSE.

pc

Whether principal component analysis will be computed. Default FALSE

boot

Whether bootstrapping will be computed. Default FALSE

var

The variable of concern, which is vital for univariate outlier detection methods

pcvar

Principal component analysis to e used for outlier detection after PCA. Default PC1

Value

Dataframe with or with no outliers.

Examples

if (FALSE) { # \dontrun{

data("efidata")

gbd <- check_names(data = efidata, colsp='scientificName', pct=90, merge=TRUE)

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'))

refdata <- pred_extract(data = gbd, raster= wcd ,
                        lat = 'decimalLatitude',
                        lon= 'decimalLongitude',
                        colsp = 'speciescheck',
                        bbox = db,
                        minpts = 10)

kmeansout <- xkmeans(data = refdata[['Salmo trutta']],
                      output='outlier', exclude = c('x', 'y'), mode = 'soft', k=3)
} # }