Identify outliers using One Class Support Vector Machines

Usage

onesvm(
  data,
  kernel = "radial",
  tune = FALSE,
  exclude = NULL,
  output,
  tpar = list(gamma = 1^(-1:1), epislon = seq(0, 1, 0.1), cost = 2^2:4, nu = seq(0.05, 1,
    0.1)),
  boot = FALSE,
  pc = FALSE,
  var,
  pcvar = NULL
)

Arguments

data: Dataframe of environmental variables extracted from where the species was recorded present or absent.
kernel: Either radial, linear
tune: To performed a tuned version of one-class svm. High computation requirements needed.
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 doesnot want to consider.
output: Either clean: for a dataset with no outliers or outlier: to output a dataframe with outliers.
tpar: A list of parameters to be varied during tunning from the normal model.
boot: Whether bootstrapping will be computed. Default FALSE
pc: Whether principal component analysis will be computed. Default FALSE
var: The variable of concern, which is vital for univariate outlier detection methods
pcvar: Principal component analysis to e used for outlier detection after PCA. Default PC1

Value

Dataframe with or with no outliers.

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)

nedata <- onesvm(data = refdata[['Salmo trutta']], exclude = c("x", "y"),  output='outlier')
} # }