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

Value

Data frame with or with no outliers.

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).

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


# \donttest{
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')
# }