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

Usage

correlation(scores, metrics = NULL)

Arguments

scores

A data.table of scores 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

Value

A data.table with correlations for the different metrics

Examples

scores <- score(example_quantile)
#> The following messages were produced when checking inputs:
#> 1.  144 values for `prediction` are NA in the data provided and the corresponding rows were removed. This may indicate a problem if unexpected.
correlation(scores)
#> Warning: There is a column called 'quantile' in the scores. Usually, you should call 'summarise_scores()' to summarise over quantiles and obtain one score per forecast before calculating correlations. You can ignore this warning if you know what you're doing.
#>    interval_score dispersion underprediction overprediction coverage
#> 1:           1.00       0.41            0.31           0.93    -0.23
#> 2:           0.41       1.00            0.10           0.27    -0.05
#> 3:           0.31       0.10            1.00          -0.03    -0.23
#> 4:           0.93       0.27           -0.03           1.00    -0.16
#> 5:          -0.23      -0.05           -0.23          -0.16     1.00
#> 6:          -0.23      -0.08           -0.20          -0.17     0.78
#> 7:           0.10       0.10           -0.32           0.21     0.03
#> 8:           0.97       0.47            0.30           0.89    -0.21
#>    coverage_deviation  bias ae_median             metric
#> 1:              -0.23  0.10      0.97     interval_score
#> 2:              -0.08  0.10      0.47         dispersion
#> 3:              -0.20 -0.32      0.30    underprediction
#> 4:              -0.17  0.21      0.89     overprediction
#> 5:               0.78  0.03     -0.21           coverage
#> 6:               1.00  0.04     -0.26 coverage_deviation
#> 7:               0.04  1.00      0.10               bias
#> 8:              -0.26  0.10      1.00          ae_median