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


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)

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


} # }