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Global-Local Outlier Score from Hierarchies

Source: R/outliermethods.R
xglosh.Rd

Global-Local Outlier Score from Hierarchies

Usage

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

Arguments

data

Data frame of species records with environmental data.

k

The size of the neighborhood (Hahsler et al 2022).

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.

References

  1. Campello, Ricardo JGB, Davoud Moulavi, Arthur Zimek, and Joerg Sander. Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Transactions on Knowledge Discovery from Data (TKDD) 10, no. 1 (2015). doi:10.1145/2733381

  2. Hahsler M, Piekenbrock M (2022). dbscan: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Related Algorithms. R package version 1.1-11, <https://CRAN.R-project.org/package=dbscan>

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)

gloshout <- xglosh(data = refdata[['Salmo trutta']], exclude = c("x", "y"),
                output='outlier', metric ='manhattan', k = 3,
                 mode = "soft")
                 } # }