Performance of a uniform forecast
Usage
add_uniform_pred(
test,
max_score,
discrete = TRUE,
include_samples = FALSE,
n_samples = NULL
)Arguments
- test
Testing dataframe. The only requirements is that it contains a column "Score".
- max_score
Maximum value that the score can take
- discrete
Whether to estimate a discrete or continuous historical forecast
- include_samples
Whether to return samples from the historical forecast in the output
- n_samples
If include_samples=TRUE, how many samples to return. When NULL, the samples
0:max_scoreare returned.
Value
Dataframe test appended by the columns "lpd", "RPS" (or CRPS if discrete=FALSE) and optionally "Samples"
Examples
max_score <- 100
test <- data.frame(Score = rbinom(1e2, max_score, 0.5))
add_uniform_pred(test, max_score)
#> Score lpd RPS
#> 1 44 -4.615121 0.08772277
#> 2 52 -4.615121 0.08455446
#> 3 53 -4.615121 0.08504950
#> 4 49 -4.615121 0.08425743
#> 5 50 -4.615121 0.08415842
#> 6 61 -4.615121 0.09613861
#> 7 38 -4.615121 0.09841584
#> 8 49 -4.615121 0.08425743
#> 9 53 -4.615121 0.08504950
#> 10 54 -4.615121 0.08574257
#> 11 61 -4.615121 0.09613861
#> 12 49 -4.615121 0.08425743
#> 13 55 -4.615121 0.08663366
#> 14 47 -4.615121 0.08504950
#> 15 52 -4.615121 0.08455446
#> 16 54 -4.615121 0.08574257
#> 17 50 -4.615121 0.08415842
#> 18 57 -4.615121 0.08900990
#> 19 47 -4.615121 0.08504950
#> 20 48 -4.615121 0.08455446
#> 21 52 -4.615121 0.08455446
#> 22 52 -4.615121 0.08455446
#> 23 55 -4.615121 0.08663366
#> 24 49 -4.615121 0.08425743
#> 25 52 -4.615121 0.08455446
#> 26 51 -4.615121 0.08425743
#> 27 52 -4.615121 0.08455446
#> 28 47 -4.615121 0.08504950
#> 29 50 -4.615121 0.08415842
#> 30 56 -4.615121 0.08772277
#> 31 55 -4.615121 0.08663366
#> 32 47 -4.615121 0.08504950
#> 33 51 -4.615121 0.08425743
#> 34 48 -4.615121 0.08455446
#> 35 52 -4.615121 0.08455446
#> 36 53 -4.615121 0.08504950
#> 37 57 -4.615121 0.08900990
#> 38 51 -4.615121 0.08425743
#> 39 52 -4.615121 0.08455446
#> 40 51 -4.615121 0.08425743
#> 41 54 -4.615121 0.08574257
#> 42 47 -4.615121 0.08504950
#> 43 47 -4.615121 0.08504950
#> 44 48 -4.615121 0.08455446
#> 45 45 -4.615121 0.08663366
#> 46 42 -4.615121 0.09049505
#> 47 49 -4.615121 0.08425743
#> 48 44 -4.615121 0.08772277
#> 49 46 -4.615121 0.08574257
#> 50 46 -4.615121 0.08574257
#> 51 55 -4.615121 0.08663366
#> 52 51 -4.615121 0.08425743
#> 53 54 -4.615121 0.08574257
#> 54 47 -4.615121 0.08504950
#> 55 57 -4.615121 0.08900990
#> 56 38 -4.615121 0.09841584
#> 57 44 -4.615121 0.08772277
#> 58 52 -4.615121 0.08455446
#> 59 51 -4.615121 0.08425743
#> 60 49 -4.615121 0.08425743
#> 61 56 -4.615121 0.08772277
#> 62 45 -4.615121 0.08663366
#> 63 49 -4.615121 0.08425743
#> 64 42 -4.615121 0.09049505
#> 65 52 -4.615121 0.08455446
#> 66 39 -4.615121 0.09613861
#> 67 51 -4.615121 0.08425743
#> 68 56 -4.615121 0.08772277
#> 69 48 -4.615121 0.08455446
#> 70 55 -4.615121 0.08663366
#> 71 58 -4.615121 0.09049505
#> 72 48 -4.615121 0.08455446
#> 73 51 -4.615121 0.08425743
#> 74 39 -4.615121 0.09613861
#> 75 59 -4.615121 0.09217822
#> 76 50 -4.615121 0.08415842
#> 77 54 -4.615121 0.08574257
#> 78 53 -4.615121 0.08504950
#> 79 51 -4.615121 0.08425743
#> 80 42 -4.615121 0.09049505
#> 81 46 -4.615121 0.08574257
#> 82 51 -4.615121 0.08425743
#> 83 45 -4.615121 0.08663366
#> 84 49 -4.615121 0.08425743
#> 85 47 -4.615121 0.08504950
#> 86 46 -4.615121 0.08574257
#> 87 50 -4.615121 0.08415842
#> 88 49 -4.615121 0.08425743
#> 89 39 -4.615121 0.09613861
#> 90 49 -4.615121 0.08425743
#> 91 52 -4.615121 0.08455446
#> 92 60 -4.615121 0.09405941
#> 93 47 -4.615121 0.08504950
#> 94 46 -4.615121 0.08574257
#> 95 54 -4.615121 0.08574257
#> 96 48 -4.615121 0.08455446
#> 97 47 -4.615121 0.08504950
#> 98 43 -4.615121 0.08900990
#> 99 47 -4.615121 0.08504950
#> 100 49 -4.615121 0.08425743