distboxplot: Distribution boxplot

In specleanr: Detecting Environmental Outliers in Data Analysis Pipelines

Distribution boxplot

Description

Distribution boxplot

Usage

distboxplot(
  data,
  var,
  output,
  p1 = 0.025,
  p2 = 0.975,
  boot = FALSE,
  pc = FALSE,
  pcvar = NULL
)

Arguments

data	Dataframe or vector where to check outliers.
var	Variable to be used for outlier detection if data is not a vector file.
output	Either clean: for clean data output without outliers; outliers: for outlier data frame or vectors.
p1, p2	Different pvalues for outlier detection Schwertman et al. 2004).
boot	Whether bootstrapping will be computed. Default FALSE
pc	Whether principal component analysis will be computed. Default FALSE
pcvar	Principal component analysis to e used for outlier detection after PCA. Default PC1

Value

Either clean or outliers.

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)

 bxout <- distboxplot(data = refdata[["Thymallus thymallus"]], var = 'bio6', output='outlier')

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