logboxplot: Log boxplot based for outlier detection.

In specleanr: Detecting Environmental Outliers in Data Analysis Pipelines

Log boxplot based for outlier detection.

Description

Log boxplot based for outlier detection.

Usage

logboxplot(data, var, output, x = 1.5, 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 in a vector format.
output	Either clean: for clean data output without outliers; outliers: for outlier data frame or vectors.
x	The constant for creating lower and upper fences. Extreme is 3, but default is 1.5.
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 loxplot for outlier detection Barbato et al. (2011) modifies the the interquartile range method to detect outlier but considering the sample sizes while indicating the fences (lower and upper fences).

lowerfence = [Q1 -1.5*IQR[1+0.1 * log(n/10)]

upperfence = [Q3 +1.5*IQR[1+0.1 *log(n/10)]

Where; Q1 is the lower quantile and Q3 is the upper quantile. The method consider the sample size in setting the fences, to address the weakness of the interquartile range method (Tukey, 1977). However. similar to IQR method for flagging outlier, log boxplot modification is affected by data skewness and which can be address using distboxplot, seqfences, mixediqr and semiIQR.

Value

Dataframe with our without outliers depending on the output.

clean

Data without outliers.

outlier

Data with outliers.

References

Barbato G, Barini EM, Genta G, Levi R. 2011. Features and performance of some outlier detection methods. Journal of Applied Statistics 38:2133-2149

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

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