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Identifies outliers using Reverse Jackknifing method based on Chapman et al., (2005).

Source: R/outliermethods.R
jknife.Rd

Identifies outliers using Reverse Jackknifing method based on Chapman et al., (2005).

Usage

jknife(
  data,
  var,
  output = "outlier",
  mode = "soft",
  pc = FALSE,
  pcvar = NULL,
  boot = FALSE
)

Arguments

data

Dataframe to check for outliers

var

Variable considered in flagging suspicious outliers.

output

Either clean: for data frame with no suspicious outliers or outlier: to return data frame with only outliers

mode

Either robust, if a robust mode is used which uses median instead of mean and median absolute deviation from median or mad instead of standard deviation.

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

Data frame with or with no outliers.

Details

Reverse jackknifing was specifically developed to detect error climate profiles (Chapman 1991, 1999). The method has been applied in detecting outliers in environmental data (García-Roselló et al. 2014; Robertson et al. 2016) and incorporated in the DIVAS-GIS software (Hijmans et al. 2001).

References

  1. Chapman AD. 1991. Quality control and validation of environmental resource data in Data Quality and Standards. Pages 1-23. Canberra. Available from https://www.researchgate.net/publication/332537824.

  2. Chapman AD. 1999. Quality Control and Validation of Point-Sourced Environmental Resource Data. eds. . Chelsea,. Pages 409-418 in Lowell K, Jaton A, editors. Spatial accuracy assessment: Land information uncertainty in natural resources, 1st edition. MI: Ann Arbor Press., Chelsea.

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)

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

} # }