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 48 -4.615121 0.08455446
#> 3 54 -4.615121 0.08574257
#> 4 49 -4.615121 0.08425743
#> 5 57 -4.615121 0.08900990
#> 6 49 -4.615121 0.08425743
#> 7 59 -4.615121 0.09217822
#> 8 49 -4.615121 0.08425743
#> 9 50 -4.615121 0.08415842
#> 10 47 -4.615121 0.08504950
#> 11 58 -4.615121 0.09049505
#> 12 49 -4.615121 0.08425743
#> 13 47 -4.615121 0.08504950
#> 14 49 -4.615121 0.08425743
#> 15 47 -4.615121 0.08504950
#> 16 44 -4.615121 0.08772277
#> 17 53 -4.615121 0.08504950
#> 18 52 -4.615121 0.08455446
#> 19 62 -4.615121 0.09841584
#> 20 45 -4.615121 0.08663366
#> 21 50 -4.615121 0.08415842
#> 22 46 -4.615121 0.08574257
#> 23 61 -4.615121 0.09613861
#> 24 43 -4.615121 0.08900990
#> 25 47 -4.615121 0.08504950
#> 26 55 -4.615121 0.08663366
#> 27 53 -4.615121 0.08504950
#> 28 47 -4.615121 0.08504950
#> 29 56 -4.615121 0.08772277
#> 30 50 -4.615121 0.08415842
#> 31 44 -4.615121 0.08772277
#> 32 55 -4.615121 0.08663366
#> 33 43 -4.615121 0.08900990
#> 34 46 -4.615121 0.08574257
#> 35 52 -4.615121 0.08455446
#> 36 46 -4.615121 0.08574257
#> 37 45 -4.615121 0.08663366
#> 38 52 -4.615121 0.08455446
#> 39 51 -4.615121 0.08425743
#> 40 41 -4.615121 0.09217822
#> 41 61 -4.615121 0.09613861
#> 42 49 -4.615121 0.08425743
#> 43 48 -4.615121 0.08455446
#> 44 58 -4.615121 0.09049505
#> 45 51 -4.615121 0.08425743
#> 46 56 -4.615121 0.08772277
#> 47 54 -4.615121 0.08574257
#> 48 54 -4.615121 0.08574257
#> 49 51 -4.615121 0.08425743
#> 50 50 -4.615121 0.08415842
#> 51 54 -4.615121 0.08574257
#> 52 43 -4.615121 0.08900990
#> 53 52 -4.615121 0.08455446
#> 54 55 -4.615121 0.08663366
#> 55 48 -4.615121 0.08455446
#> 56 51 -4.615121 0.08425743
#> 57 55 -4.615121 0.08663366
#> 58 49 -4.615121 0.08425743
#> 59 48 -4.615121 0.08455446
#> 60 47 -4.615121 0.08504950
#> 61 52 -4.615121 0.08455446
#> 62 58 -4.615121 0.09049505
#> 63 50 -4.615121 0.08415842
#> 64 53 -4.615121 0.08504950
#> 65 46 -4.615121 0.08574257
#> 66 39 -4.615121 0.09613861
#> 67 43 -4.615121 0.08900990
#> 68 55 -4.615121 0.08663366
#> 69 55 -4.615121 0.08663366
#> 70 53 -4.615121 0.08504950
#> 71 49 -4.615121 0.08425743
#> 72 45 -4.615121 0.08663366
#> 73 53 -4.615121 0.08504950
#> 74 52 -4.615121 0.08455446
#> 75 55 -4.615121 0.08663366
#> 76 66 -4.615121 0.10950495
#> 77 48 -4.615121 0.08455446
#> 78 56 -4.615121 0.08772277
#> 79 49 -4.615121 0.08425743
#> 80 55 -4.615121 0.08663366
#> 81 49 -4.615121 0.08425743
#> 82 43 -4.615121 0.08900990
#> 83 48 -4.615121 0.08455446
#> 84 45 -4.615121 0.08663366
#> 85 44 -4.615121 0.08772277
#> 86 47 -4.615121 0.08504950
#> 87 53 -4.615121 0.08504950
#> 88 51 -4.615121 0.08425743
#> 89 48 -4.615121 0.08455446
#> 90 44 -4.615121 0.08772277
#> 91 54 -4.615121 0.08574257
#> 92 52 -4.615121 0.08455446
#> 93 49 -4.615121 0.08425743
#> 94 51 -4.615121 0.08425743
#> 95 54 -4.615121 0.08574257
#> 96 52 -4.615121 0.08455446
#> 97 47 -4.615121 0.08504950
#> 98 50 -4.615121 0.08415842
#> 99 58 -4.615121 0.09049505
#> 100 53 -4.615121 0.08504950