mahal: Flags outliers based on Mahalanobis distance matrix for all...

In specleanr: Detecting Environmental Outliers in Data Analysis Pipelines

Flags outliers based on Mahalanobis distance matrix for all records.

Description

Flags outliers based on Mahalanobis distance matrix for all records.

Usage

mahal(
  data,
  exclude = NULL,
  output = "outlier",
  mode = "soft",
  pdf = 0.95,
  tol = 1e-20,
  pc = FALSE,
  boot = FALSE,
  var,
  pcvar = NULL
)

Arguments

data	dataframe. Dataframe to check for outliers or extract the clean data.
exclude	vector or string Variables that should not be considered in the executing the Mahalanobis distance matrix. These can be coordinates such as latitude/longitude or any column that the user doesn't want to consider.
output	string Either clean for a data set with no outliers or outlier to output a data frame with outliers.
mode	string Either robust, if a robust mode is used which uses auto estimator to instead of mean. Default mode is soft.
pdf	numeric chisqure probability distribution value used for flagging outliers (Leys et al. 2018). Default is 0.95.
tol	numeric tolernce value when the inverse calculation are too small. Default 1e-20.
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

Either clean or outliers dataset

References

Leys C, Klein O, Dominicy Y, Ley C. 2018. Detecting multivariate outliers: Use a robust variant of the Mahalanobis distance. Journal of Experimental Social Psychology 74:150-156.

Examples

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'))

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

#outliers
outliers <- mahal(data = refdata[["Thymallus thymallus"]], exclude = c("x", "y"),
                        output='outlier')

specleanr documentation built on Nov. 26, 2025, 1:07 a.m.