Calculate correlation between metrics — get

Calculate the correlation between different metrics for a data.frame of scores as produced by score().

Usage

get_correlations(scores, metrics = get_metrics.scores(scores), ...)

Arguments

scores: An object of class scores (a data.table with scores and an additional attribute metrics as produced by score()).
metrics: A character vector with the metrics to show. If set to NULL (default), all metrics present in scores will be shown.
...: Additional arguments to pass down to cor().

Value

An object of class scores (a data.table with an additional attribute metrics holding the names of the scores) with correlations between different metrics

Examples

library(magrittr) # pipe operator

scores <- example_quantile %>%
 as_forecast_quantile() %>%
 score()
#> ℹ Some rows containing NA values may be removed. This is fine if not
#>   unexpected.

get_correlations(scores)
#>           wis overprediction underprediction  dispersion        bias
#>         <num>          <num>           <num>       <num>       <num>
#> 1:  1.0000000     0.94297565      0.28377361  0.45566303  0.10545891
#> 2:  0.9429757     1.00000000     -0.03310356  0.32493799  0.21532161
#> 3:  0.2837736    -0.03310356      1.00000000  0.14580143 -0.35123801
#> 4:  0.4556630     0.32493799      0.14580143  1.00000000  0.11118365
#> 5:  0.1054589     0.21532161     -0.35123801  0.11118365  1.00000000
#> 6: -0.2076649    -0.14556039     -0.21392764 -0.09400664  0.01338140
#> 7: -0.4075613    -0.31824017     -0.35756699 -0.08614678  0.09802725
#> 8:  0.9886108     0.90326672      0.33589892  0.53809741  0.09578751
#>    interval_coverage_50 interval_coverage_90   ae_median               metric
#>                   <num>                <num>       <num>               <char>
#> 1:          -0.20766492          -0.40756133  0.98861080                  wis
#> 2:          -0.14556039          -0.31824017  0.90326672       overprediction
#> 3:          -0.21392764          -0.35756699  0.33589892      underprediction
#> 4:          -0.09400664          -0.08614678  0.53809741           dispersion
#> 5:           0.01338140           0.09802725  0.09578751                 bias
#> 6:           1.00000000           0.37245118 -0.24559356 interval_coverage_50
#> 7:           0.37245118           1.00000000 -0.41079097 interval_coverage_90
#> 8:          -0.24559356          -0.41079097  1.00000000            ae_median