mixediqr: Mixed Interquartile range and semiInterquartile range 'Walker...

In specleanr: Detecting Environmental Outliers in Data Analysis Pipelines

Mixed Interquartile range and semiInterquartile range Walker et al., 2018

Description

Mixed Interquartile range and semiInterquartile range Walker et al., 2018

Usage

mixediqr(data, var, output, x = 3, pc = FALSE, pcvar = NULL, boot = FALSE)

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.
x	A constant for flagging outliers Walker et al., 2018).
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

Either clean our outliers

References

Walker ML, Dovoedo YH, Chakraborti S, Hilton CW. 2018. An Improved Boxplot for Univariate Data. American Statistician 72:348-353. American Statistical Association.

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)

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

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