hampel: Flag suspicious outliers based on the Hampel filter method..

In specleanr: Detecting Environmental Outliers in Data Analysis Pipelines

Flag suspicious outliers based on the Hampel filter method..

Description

Flag suspicious outliers based on the Hampel filter method..

Usage

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

Arguments

data	Data frame to check for outliers
var	Environmental parameter considered in flagging suspicious outliers
output	Either clean: for dataframe with no suspicious outliers or outlier: to retrun dataframe with only outliers
x	A constant to create a fence or boundary to detect outliers.
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

Details

The Hampel filter method is a robust decision-based filter that considers the median and MAD. Outliers lies beyond

[x-* lmbda*MAD; x+ lmbda*MAD]

and lmbda of 3 was considered (Pearson et al. 2016).

Value

Data frame with or with no outliers.

References

Pearson Ronald, Neuvo Y, Astola J, Gabbouj M. 2016. The Class of Generalized Hampel Filters. 2546-2550 2015 23rd European Signal Processing Conference (EUSIPCO).

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)

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

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