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
)

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.

Value

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

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, colsp= 'species', pct = 90, merge =TRUE)


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 = datacheck,
                      raster= worldclim ,
                      lat = 'decimalLatitude',
                      lon= 'decimalLongitude',
                      colsp = 'speciescheck',
                      bbox = db,
                      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(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)
} # }

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

Usage

Arguments

Value

See also

Examples