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Create reference data object for LIA endmember distance and probability estimation functions. Data preprocessing and cleaning ensure the reliability and accuracy of provenance analysis. This step results in the removal of all NA values from the selected isotope tables and groups. The step should also exclude groups with a low number samples (in the case of Shnyr et al. (2026), this was 5).

Usage

as_pbiso_ref_data(x, ...)

# S3 method for class 'ASTR'
as_pbiso_ref_data(x, group, min_groupsize = 5L, ...)

Arguments

x

ASTR object containing 206Pb/204Pb, 207Pb/204Pb, 208Pb/204Pb isotope ratios.

...

Additional parameters

group

Name of the column containing isotope groups as character string.

min_groupsize

Integer value for the minimum number of samples in a group, default value is 5L.

Value

ASTR_Pbiso_ref_data object with isotope groupings and isotope columns of 206Pb/204Pb, 207Pb/204Pb, 208Pb/204Pb.

Examples

as_pbiso_ref_data(GlobaLID_ASTR,
                  names(GlobaLID_ASTR)[[2]],
                  min_groupsize = 5)
#> ASTR table
#> Analytical columns: 207Pb/204Pb, 208Pb/204Pb
#> NULL 
#> # A data frame: 5,784 × 4
#>    `Political province/region` `206Pb/204Pb` `207Pb/204Pb` `208Pb/204Pb`
#>    <chr>                               <dbl>         <dbl>         <dbl>
#>  1 Catamarca                            18.7          15.6          38.7
#>  2 Catamarca                            18.8          15.6          38.8
#>  3 Catamarca                            18.8          15.7          38.8
#>  4 Catamarca                            18.8          15.7          38.9
#>  5 Catamarca                            18.7          15.6          38.8
#>  6 Catamarca                            18.7          15.6          38.8
#>  7 Catamarca                            18.7          15.6          38.7
#>  8 Catamarca                            18.7          15.6          38.7
#>  9 Catamarca                            18.7          15.6          38.7
#> 10 Catamarca                            18.8          15.6          38.8
#> # ℹ 5,774 more rows