Adjust the boxplots bounding fences using medcouple to flag suspicious outliers.

Usage

adjustboxplots(
  data,
  var,
  output = "outlier",
  a = -4,
  b = 3,
  coef = 1.5,
  pc = FALSE,
  pcvar = NULL,
  boot = FALSE
)

Arguments

data: dataframe. Dataframe to check for outliers.
var: string. Environmental predictor considered in flagging suspicious outliers.
output: string Either clean: for dataframe with no suspicious outliers or outlier: to return dataframe with only outliers.
a: numeric. Constant for adjusted boxplots.
b: numeric. Constant for adjusted boxplots.
coef: numeric. Constant for adjusted boxplots.
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

dataframe. Dataframe with or with no outliers.

References

Hubert M, Vandervieren E. 2008. An adjusted boxplot for skewed distributions. Computational Statistics and Data Analysis 52:5186-5201.

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)

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

} # }