Computes interquartile range to flag environmental outliers

Computes interquartile range to flag environmental outliers

Usage

interquartile(
  data,
  var,
  output,
  x = 1.5,
  pc = FALSE,
  pcvar = NULL,
  boot = FALSE
)

Arguments

data

Dataframe to check for outliers

var

Variable 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

Interquartile range (IQR) uses quantiles that are resistant to outliers compared to mean and standard deviation (Seo 2006). Records were considered as mild outliers if they fell outside the lower and upper bounding fences [Q1 (lower quantile) -1.5*IQR (Interquartile range); Q3 (upper quantile) +1.5*IQR] respectively (Rousseeuw & Hubert 2011). Extreme outliers were also considered if they fell outside \[Q1-3*IQR, Q3+3*IQR\] (García-Roselló et al. 2014). However, using the interquartile range assumes uniform lower and upper bounding fences, which is not robust to highly skewed data (Hubert & Vandervieren 2008).

References

Rousseeuw PJ, Hubert M. 2011. Robust statistics for outlier detection. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery 1:73-79.

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)

 iqrout <- interquartile(data = refdata[['Salmo trutta']], var = 'bio6', output='outlier')
} # }