k-nearest neighbors for outlier detection

Usage

xknn(
  data,
  output,
  exclude = NULL,
  metric = "manhattan",
  mode = "soft",
  pc = FALSE,
  boot = FALSE,
  var,
  pcvar = NULL
)

Arguments

data: Data frame of species records with environmental data.
output: Either clean: for data frame with no suspicious outliers or outlier: to return dataframe with only outliers.
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.
metric: The different metric distances to compute the distances among the environmental predictors. See dist function and how te different distances are applied. The different measures are allowed including "euclidean", "maximum", "manhattan", "canberra", "binary".
mode: This includes soft when the outliers are removed using mean to compute the z-scores or robust when median absolute deviation.
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)

lofout <- xknn(data = refdata[['Salmo trutta']], exclude = c("x", "y"),
                output='outlier', metric ='manhattan',
                 mode = "soft")
} # }