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

Value

Dataframe with our without outliers depending on the output.

clean: Data without outliers.
outlier: Data with outliers.

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.

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


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)

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

} # }