extract_clean_data: Extract final clean data using either absolute or best method...

In specleanr: Detecting Environmental Outliers in Data Analysis Pipelines

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

Description

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

Usage

extract_clean_data(
  refdata,
  outliers,
  mode = "abs",
  var_col = NULL,
  threshold = NULL,
  warn = FALSE,
  verbose = FALSE,
  autothreshold = FALSE,
  pabs = 0.1,
  loess = FALSE,
  outlier_to_NA = FALSE,
  cutoff = 0.6
)

Arguments

refdata	dataframe. The reference data for the species used in outlier detection.
outliers	string. Output from the outlier detection process.
mode	character. Either abs to use absolute outliers to filter data or best to outliers from best method.
var_col	string. A parameter to be used if the data is a data frame and the user must indicate the column wih 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.
autothreshold	vector. Identifies the threshold with mean number of absolute outliers.The search is limited within 0.51 to 1 since thresholds less than are deemed inappropriate for identifying absolute outliers. The autothreshold is used when threshold is set to NULL.
pabs	numeric. Percentage of outliers allowed to be extracted from the data. If best is used to extract outliers and the pabs is exceeded, the absolute outliers are removed instead. This because some records in the best methods are repeated and they will likely to remove true values as outliers.
loess	logical. Set to TRUE to use loess threshold optimization to extract clean data.
outlier_to_NA	logical If TRUE a clean dataset will have outliers replaced with NAs. This parameter is experimented to ouput dataframe when multiple variables of concerns are considered during outlier detection. ###param multiple TRUE for multiple species and FALSE for single species considered during outlier detection.
cutoff	numeric. Ranging from 0.5 to 0.8 indicating the cutoff to initiate the LOESS model to optimize the identification of absolute outliers.

Value

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

See Also

search_threshold

Examples

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'))


danube <- system.file('extdata/danube.shp.zip', package='specleanr')

db <- sf::st_read(danube, quiet=TRUE)


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

rdata <- pred_extract(data = matchdata,
                      raster= worldclim ,
                      lat = 'decimalLatitude',
                      lon= 'decimalLongitude',
                      colsp = 'species',
                      bbox = db,
                      minpts = 10,
                      list=TRUE,
                      merge=FALSE)


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(refdata = rdata, outliers = out_df,
                                 mode = 'abs', threshold = 0.6,
                                 autothreshold = FALSE)

bestmout_bm <- extract_clean_data(refdata = rdata, outliers = out_df,
                                  mode = 'best', threshold = 0.6,
                                 autothreshold = FALSE)

specleanr documentation built on Nov. 26, 2025, 1:07 a.m.