Extract final clean data using either absolute or best method generated outliers.

Usage

classify_data(
  refdata,
  outliers,
  var_col = NULL,
  threshold = 0.1,
  warn = FALSE,
  verbose = TRUE,
  classify = "med",
  EIF = FALSE
)

Arguments

refdata: dataframe. The reference data for the species used in outlier detection.
outliers: string. Output from the outlier detection process.
var_col: string. A parameter to be used if the data is a data frame and the user must indicate the column with species names.
threshold: numeric. Value to consider whether the outlier is an absolute outlier or not.
warn: logical. If FALSE, warning on whether absolute outliers obtained at a low threshold is indicated. Default TRUE.
verbose: logical. Produces messages or not. Default FALSE.
classify: string. Categorize data base on the correlation coefficient manner based on Akoglu 2018. For more information check in the details section.
EIF: logical To calculate the empirical influence function for each value.

Value

Either a list or dataframe of cleaned records for multiple species.

Details

Outlier cluster weights were based on statistical classification of coefficients mostly for correlation based on Akoglu 2018. They are classified based on three naming standards, namely Dancey & Reidy (Physchology), Quinni piac University (Politics) and Chan YH medicine. All classifications have been used in the function and each affects the data clusters. The default is Chan YH (medicine).

References

Akoglu, H. 2018. User’s guide to correlation coefficients. - Turk J Emerg Med 18: 91–93.

Examples


if (FALSE) { # \dontrun{

data(jdsdata)
data(efidata)
matchdata <- match_datasets(datasets = list(jds = jdsdata, efi = efidata),
                            lats = 'lat',
                            lons = 'lon',
                            species = c('speciesname','scientificName'),
                            country= c('JDS4_site_ID'),
                            date=c('sampling_date', 'Date'))

datacheck <- check_names(matchdata, var_col= 'species', pct = 90, merge =TRUE)


db <- sf::st_read(system.file('extdata/danube/basinfinal.shp', package='specleanr'), quiet=TRUE)


worldclim <- terra::rast(system.file('extdata/worldclim.tiff', package='specleanr'))

rdata <- pred_extract(data = datacheck,
                      raster= worldclim ,
                      lat = 'decimalLatitude',
                      lon= 'decimalLongitude',
                      var_col = 'speciescheck',
                      bbox = db,
                      multiple = TRUE,
                      minpts = 10,
                      list=TRUE,
                      merge=F)


out_df <- multidetect(data = rdata, multiple = TRUE,
                      var = 'bio6',
                      output = 'outlier',
                      exclude = c('x','y'),
                      methods = c('zscore', 'adjbox','iqr', 'semiqr','hampel'))

#extracting use the absolute method for one species

extractabs <- extract_clean_data_label(refdata = rdata, outliers = out_df)
} # }