Computes z-scores to flag environmental outliers.

Usage

zscore(
  data,
  var,
  output = "outlier",
  type = "mild",
  mode = "soft",
  pc = FALSE,
  pcvar = NULL,
  boot = FALSE
)

Arguments

data: Dataframe or vector to check for outliers.
var: Variable considered in flagging suspicious outliers.
output: Either clean: for data frame with no suspicious outliers or outlier: to return dataframe with only outliers.
type: Either mild if zscore cut off is 2.5 or extreme if zscore is >3.
mode: Either robust, if a robust mode is used which uses median instead of mean and median absolute deviation from median.
pc: Whether principal component analysis will be computed. Default FALSE
pcvar: Principal component analysis to e used for outlier detection after PCA. Default PC1
boot: Whether bootstrapping will be computed. Default FALSE

Value

Data frame with or with no outliers.

Details

The method uses mean as an estimator of location and standard deviation for scale (Rousseeuw & Hubert 2011), which both have zero breakdown point, and their influence function is unbounded (robustness of an estimator to outliers) (Seo 2006; Rousseeuw & Hubert 2011). Because both parameters are not robust to outliers, it leads to outlier masking and swamping (Rousseeuw & Hubert 2011). Records are flagged as outliers if their Z-score exceeds 2.5 (Rousseeuw & Hubert 2011).

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)

 zout <- zscore(data = refdata[['Salmo trutta']], var = 'bio6', output='outlier')

} # }