Identify outliers using isolation forest model.

Usage

isoforest(
  data,
  size,
  cutoff = 0.5,
  output,
  exclude = NULL,
  pc = FALSE,
  boot = FALSE,
  pcvar = NULL,
  var
)

Arguments

data: Dataframe of environmental variables extracted from where the species was recorded present or absent.
size: Proportion of data to be used in training isolation forest n´model. It ranges form 0.1 (fewer data selected ) to 1 to all data used in training isolation model.
cutoff: Cut to select where the record was an outlier or not.
output: Either clean: for a data set with no outliers or outlier: to output a dataframe with outliers. Default is 0.5.
exclude: Exclude variables that should not be considered in the fitting the one class model, for example x and y columns or latitude/longitude or any column that the user doesn't want to consider.
pc: Whether principal component analysis will be computed. Default FALSE
boot: Whether bootstrapping will be computed. Default FALSE
pcvar: Principal component analysis to e used for outlier detection after PCA. Default PC1
var: The variable of concern, which is vital for univariate outlier detection methods

Value

Dataframe with or with no outliers.

References

Liu FeiT, Ting KaiM, Zhou Z-H. 2008. Isolation Forest. Pages 413–422 In 2008 Eighth IEEE International Conference on Data Mining. Available from https://ieeexplore.ieee.org/abstract/document/4781136 (accessed November 18, 2023).

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)

iosd <- isoforest(data = refdata[['Salmo trutta']], size = 0.7,  output='outlier',
                  exclude = c("x", "y"))
} # }