Computes semi-interquantile range to flag suspicious outliers

Usage

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

Arguments

data: Dataframe 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

Dataframe with or with no outliers.

Details

SemiInterquantile Ranges introduced adjusts for whiskers on either side to flag suspicious outliers [Q1 – 3(Q2 (median) − Q1); Q3 + 3(Q3 − Q2)] ((Kimber 1990)). However, SIQR introduced the same constant values for bounding fences for the lower and upper quartiles (Rousseeuw & Hubert 2011), which leads to outlier swamping and masking.

References

Kimber AC. 1990. Exploratory Data Analysis for Possibly Censored Data From Skewed Distributions. Page Source: Journal of the Royal Statistical Society. Series C (Applied Statistics).

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)

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

} # }