specleanr (version 1.0.0)

onesvm: Identify outliers using One Class Support Vector Machines

Description

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
)

Value

Dataframe with or with no outliers.

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

Examples

# \donttest{
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)

nedata <- onesvm(data = refdata[["Thymallus thymallus"]], exclude = c("x", "y"),  output='outlier')

# }