Agreement coefficients and related methods

These functions calculate the agreement coefficient and mean product difference (MPD), as well as their systematic and unsystematic components, from Ji and Gallo (2006). Agreement coefficients provides a useful measurement of agreement between two data sets which is bounded, symmetrical, and can be decomposed into systematic and unsystematic components; however, it assumes a linear relationship between the two data sets and treats both "truth" and "estimate" as being of equal quality, and as such may not be a useful metric in all scenarios.

Usage

ww_agreement_coefficient(data, ...)

# S3 method for class 'data.frame'
ww_agreement_coefficient(data, truth, estimate, na_rm = TRUE, ...)

ww_agreement_coefficient_vec(truth, estimate, na_rm = TRUE, ...)

ww_systematic_agreement_coefficient(data, ...)

# S3 method for class 'data.frame'
ww_systematic_agreement_coefficient(data, truth, estimate, na_rm = TRUE, ...)

ww_systematic_agreement_coefficient_vec(truth, estimate, na_rm = TRUE, ...)

ww_unsystematic_agreement_coefficient(data, ...)

# S3 method for class 'data.frame'
ww_unsystematic_agreement_coefficient(data, truth, estimate, na_rm = TRUE, ...)

ww_unsystematic_agreement_coefficient_vec(truth, estimate, na_rm = TRUE, ...)

ww_unsystematic_mpd(data, ...)

# S3 method for class 'data.frame'
ww_unsystematic_mpd(data, truth, estimate, na_rm = TRUE, ...)

ww_unsystematic_mpd_vec(truth, estimate, na_rm = TRUE, ...)

ww_systematic_mpd(data, ...)

# S3 method for class 'data.frame'
ww_systematic_mpd(data, truth, estimate, na_rm = TRUE, ...)

ww_systematic_mpd_vec(truth, estimate, na_rm = TRUE, ...)

ww_unsystematic_rmpd(data, ...)

# S3 method for class 'data.frame'
ww_unsystematic_rmpd(data, truth, estimate, na_rm = TRUE, ...)

ww_unsystematic_rmpd_vec(truth, estimate, na_rm = TRUE, ...)

ww_systematic_rmpd(data, ...)

# S3 method for class 'data.frame'
ww_systematic_rmpd(data, truth, estimate, na_rm = TRUE, ...)

ww_systematic_rmpd_vec(truth, estimate, na_rm = TRUE, ...)

Arguments

data

A data.frame containing the columns specified by the truth and estimate arguments.

...

Not currently used.

truth

The column identifier for the true results (that is numeric). This should be an unquoted column name although this argument is passed by expression and supports quasiquotation (you can unquote column names). For _vec() functions, a numeric vector.

estimate

The column identifier for the predicted results (that is also numeric). As with truth this can be specified different ways but the primary method is to use an unquoted variable name. For _vec() functions, a numeric vector.

na_rm

A logical value indicating whether NA values should be stripped before the computation proceeds.

Value

A tibble with columns .metric, .estimator, and .estimate and 1 row of values. For grouped data frames, the number of rows returned will be the same as the number of groups. For _vec() functions, a single value (or NA).

Details

Agreement coefficient values range from 0 to 1, with 1 indicating perfect agreement. truth and estimate must be the same length. This function is not explicitly spatial and as such can be applied to data with any number of dimensions and any coordinate reference system.

References

Ji, L. and Gallo, K. 2006. "An Agreement Coefficient for Image Comparison." Photogrammetric Engineering & Remote Sensing 72(7), pp 823–833, doi: 10.14358/PERS.72.7.823.

See also

Other agreement metrics: ww_willmott_d()

Other yardstick metrics: ww_global_geary_c(), ww_global_moran_i(), ww_local_geary_c(), ww_local_getis_ord_g(), ww_local_moran_i(), ww_willmott_d()

Examples

# Calculated values match Ji and Gallo 2006:
x <- c(6, 8, 9, 10, 11, 14)
y <- c(2, 3, 5, 5, 6, 8)

ww_agreement_coefficient_vec(x, y)
#> [1] 0.4745867
ww_systematic_agreement_coefficient_vec(x, y)
#> [1] 0.4784812
ww_unsystematic_agreement_coefficient_vec(x, y)
#> [1] 0.9961054
ww_systematic_mpd_vec(x, y)
#> [1] 23.65667
ww_unsystematic_mpd_vec(x, y)
#> [1] 0.1766615
ww_systematic_rmpd_vec(x, y)
#> [1] 4.863812
ww_unsystematic_rmpd_vec(x, y)
#> [1] 0.4203112

example_df <- data.frame(x = x, y = y)
ww_agreement_coefficient(example_df, x, y)
#> # A tibble: 1 × 3
#>   .metric               .estimator .estimate
#>   <chr>                 <chr>          <dbl>
#> 1 agreement_coefficient standard       0.475
ww_systematic_agreement_coefficient(example_df, x, y)
#> # A tibble: 1 × 3
#>   .metric                          .estimator .estimate
#>   <chr>                            <chr>          <dbl>
#> 1 systematic_agreement_coefficient standard       0.478
ww_unsystematic_agreement_coefficient(example_df, x, y)
#> # A tibble: 1 × 3
#>   .metric                            .estimator .estimate
#>   <chr>                              <chr>          <dbl>
#> 1 unsystematic_agreement_coefficient standard       0.996
ww_systematic_mpd(example_df, x, y)
#> # A tibble: 1 × 3
#>   .metric        .estimator .estimate
#>   <chr>          <chr>          <dbl>
#> 1 systematic_mpd standard        23.7
ww_unsystematic_mpd(example_df, x, y)
#> # A tibble: 1 × 3
#>   .metric          .estimator .estimate
#>   <chr>            <chr>          <dbl>
#> 1 unsystematic_mpd standard       0.177
ww_systematic_rmpd(example_df, x, y)
#> # A tibble: 1 × 3
#>   .metric         .estimator .estimate
#>   <chr>           <chr>          <dbl>
#> 1 systematic_rmpd standard        4.86
ww_unsystematic_rmpd(example_df, x, y)
#> # A tibble: 1 × 3
#>   .metric           .estimator .estimate
#>   <chr>             <chr>          <dbl>
#> 1 unsystematic_rmpd standard       0.420