Compute the quality metrics of the given CRP matrix
Arguments
- crps
A 2D or 3D CRP matrix
- with_entropy
If
TRUE, wrap the results of the metrics with the Shannon entropy. By defaultFALSE.
Examples
crps <- rbits(c(5, 50))
metrics(crps)
#> $reliability
#> [1] NA
#>
#> $devices
#> [1] 1 2 3 4 5
#>
#> $challenges
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
#> [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
#>
#> $uniformity
#> [1] 0.56 0.42 0.44 0.42 0.44
#>
#> $bitaliasing
#> [1] 0.2 0.4 0.8 0.2 0.2 0.6 0.2 0.6 0.6 0.8 0.4 0.4 0.4 0.4 0.6 0.4 0.2 0.6 0.6
#> [20] 0.8 0.4 0.4 0.6 0.4 0.4 0.4 0.6 0.6 0.2 0.4 0.6 0.8 0.6 0.4 0.4 0.2 0.2 0.4
#> [39] 0.8 0.2 0.2 0.8 0.2 0.6 0.4 0.4 0.6 0.6 0.0 0.6
#>
#> $uniqueness
#> [1] 0.54 0.48 0.42 0.44 0.54 0.52 0.54 0.46 0.44 0.42
#>
#> attr(,"class")
#> [1] "pufmetrics"
crps <- rbits(c(5, 50, 3))
metrics(crps)
#> $reliability
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,] 1.0 1.0 0.0 1.0 0.0 1.0 1 0.0 0.5 1.0 0.0 0.0 1.0 0.5
#> [2,] 1.0 0.0 0.5 0.5 1.0 0.5 0 0.0 1.0 0.5 0.0 1.0 1.0 0.0
#> [3,] 0.5 0.5 0.5 1.0 0.0 0.0 1 0.5 0.0 0.5 0.5 0.5 0.0 1.0
#> [4,] 0.5 1.0 0.5 1.0 0.5 0.5 1 0.5 0.5 0.0 0.0 0.0 0.5 1.0
#> [5,] 0.5 0.0 0.5 0.5 0.5 1.0 0 0.0 0.0 1.0 0.5 0.5 0.5 0.5
#> [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
#> [1,] 1.0 0.5 1.0 0.5 0.5 0.0 0.5 0.5 0.5 0.5 0.0 0.0
#> [2,] 0.5 0.5 0.5 1.0 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.0
#> [3,] 0.5 1.0 0.5 0.5 0.5 0.5 0.5 1.0 0.5 0.5 0.0 0.5
#> [4,] 0.0 0.5 0.5 1.0 0.5 0.5 0.5 0.5 0.5 1.0 0.5 0.0
#> [5,] 1.0 0.5 0.5 0.5 0.5 0.5 0.5 0.5 1.0 0.5 0.5 0.5
#> [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38]
#> [1,] 0.5 1.0 0 0.0 1.0 0.5 0.5 0.5 0.5 0.0 0.5 0.0
#> [2,] 0.0 0.5 1 0.5 0.5 0.5 0.0 0.5 0.5 1.0 0.0 0.5
#> [3,] 1.0 0.0 0 1.0 0.5 0.0 0.5 1.0 1.0 0.0 1.0 0.0
#> [4,] 1.0 0.0 1 0.5 0.5 0.0 1.0 0.5 0.0 0.0 1.0 0.5
#> [5,] 1.0 1.0 1 0.0 1.0 0.0 0.5 0.0 0.0 0.5 0.5 0.5
#> [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50]
#> [1,] 0.0 0.0 0.5 1.0 0.0 0.0 0.0 1.0 0.0 0.5 1.0 0.5
#> [2,] 0.5 0.0 1.0 0.5 0.0 0.5 1.0 0.5 1.0 0.5 0.5 0.0
#> [3,] 1.0 0.0 0.5 0.0 0.0 0.5 0.5 0.5 0.5 1.0 0.5 0.0
#> [4,] 1.0 0.5 0.5 0.5 0.5 1.0 0.5 0.5 0.0 0.5 0.5 0.5
#> [5,] 0.0 0.5 0.0 0.5 0.5 0.0 0.5 0.5 0.5 0.0 1.0 0.0
#>
#> $devices
#> [1] 1 2 3 4 5
#>
#> $challenges
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
#> [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
#>
#> $samples
#> [1] 1 2 3
#>
#> $uniformity
#> $uniformity[[1]]
#> [1] 0.58 0.52 0.50 0.52 0.48
#>
#> $uniformity[[2]]
#> [1] 0.52 0.64 0.48 0.58 0.48
#>
#> $uniformity[[3]]
#> [1] 0.48 0.54 0.56 0.54 0.64
#>
#>
#> $bitaliasing
#> $bitaliasing[[1]]
#> [1] 0.2 0.4 0.8 0.6 0.4 0.8 1.0 0.2 0.8 0.2 0.4 0.6 0.6 0.4 0.8 0.6 0.4 0.2 0.8
#> [20] 0.6 0.4 0.2 0.4 0.0 0.4 0.4 0.2 0.8 0.8 0.4 0.2 1.0 0.6 0.4 0.4 0.6 1.0 0.4
#> [39] 0.4 0.2 0.2 1.0 0.6 0.8 0.6 0.2 0.6 0.8 0.4 0.8
#>
#> $bitaliasing[[2]]
#> [1] 0.8 0.4 0.4 0.4 0.2 0.8 0.4 0.2 0.8 0.8 0.2 0.4 0.6 0.2 0.0 0.6 0.6 0.6 0.0
#> [20] 0.4 0.4 0.4 0.8 0.4 0.8 0.6 0.8 0.8 0.6 0.6 0.8 1.0 0.8 0.4 0.6 0.8 0.6 0.4
#> [39] 0.8 0.0 0.2 0.4 0.6 0.6 0.4 0.8 0.8 0.6 0.6 0.8
#>
#> $bitaliasing[[3]]
#> [1] 0.2 0.6 0.8 0.4 0.6 0.4 0.4 0.2 0.4 0.4 0.6 0.8 0.2 0.6 0.4 0.2 0.2 0.8 1.0
#> [20] 0.8 0.6 0.4 0.4 0.8 0.6 0.6 1.0 0.6 0.6 0.2 0.6 0.6 0.6 0.2 0.6 1.0 0.2 0.2
#> [39] 1.0 0.4 0.8 0.6 0.6 0.6 1.0 0.4 0.8 0.4 0.4 0.8
#>
#>
#> $uniqueness
#> $uniqueness[[1]]
#> [1] 0.54 0.64 0.50 0.58 0.58 0.48 0.56 0.42 0.58 0.52
#>
#> $uniqueness[[2]]
#> [1] 0.48 0.60 0.46 0.56 0.52 0.46 0.44 0.58 0.68 0.50
#>
#> $uniqueness[[3]]
#> [1] 0.62 0.52 0.54 0.48 0.54 0.52 0.54 0.50 0.44 0.58
#>
#>
#> attr(,"class")
#> [1] "pufmetrics"