Performance of (population) historical forecast
Usage
add_historical_pred(
test,
train,
max_score,
discrete = TRUE,
add_uniform = TRUE,
include_samples = FALSE,
n_samples = NULL
)Arguments
- test
Testing dataframe. The only requirements is that it contains a column "Score".
- train
Training 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
- add_uniform
Whether to include samples from uniform distribution when computing a discrete historical forecast. This ensures that all states are visited.
- 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 function return the training set.
Value
Dataframe test appended by the columns "lpd", "RPS" (or CRPS if discrete=FALSE) and optionally "Samples"
Details
The continuous historical forecast is calculated by considering the training set as samples from the predictive distribution.
Examples
max_score <- 100
train <- data.frame(Score = rbinom(1e2, max_score, 0.2))
test <- data.frame(Score = rbinom(1e2, max_score, 0.5))
add_historical_pred(test, train, max_score)
#> Score lpd RPS
#> 1 52 -5.303305 0.14980075
#> 2 42 -5.303305 0.10303458
#> 3 50 -5.303305 0.13965149
#> 4 40 -5.303305 0.09487537
#> 5 53 -5.303305 0.15502463
#> 6 55 -5.303305 0.16577090
#> 7 49 -5.303305 0.13472612
#> 8 45 -5.303305 0.11601965
#> 9 55 -5.303305 0.16577090
#> 10 45 -5.303305 0.11601965
#> 11 45 -5.303305 0.11601965
#> 12 50 -5.303305 0.13965149
#> 13 48 -5.303305 0.12990025
#> 14 54 -5.303305 0.16034801
#> 15 41 -5.303305 0.09890523
#> 16 48 -5.303305 0.12990025
#> 17 46 -5.303305 0.12054702
#> 18 39 -5.303305 0.09094503
#> 19 46 -5.303305 0.12054702
#> 20 46 -5.303305 0.12054702
#> 21 53 -5.303305 0.15502463
#> 22 47 -5.303305 0.12517388
#> 23 46 -5.303305 0.12054702
#> 24 53 -5.303305 0.15502463
#> 25 52 -5.303305 0.14980075
#> 26 55 -5.303305 0.16577090
#> 27 50 -5.303305 0.13965149
#> 28 69 -5.303305 0.25213906
#> 29 50 -5.303305 0.13965149
#> 30 43 -5.303305 0.10726343
#> 31 54 -5.303305 0.16034801
#> 32 53 -5.303305 0.15502463
#> 33 44 -5.303305 0.11159179
#> 34 53 -5.303305 0.15502463
#> 35 45 -5.303305 0.11601965
#> 36 42 -5.303305 0.10303458
#> 37 44 -5.303305 0.11159179
#> 38 54 -5.303305 0.16034801
#> 39 58 -5.303305 0.18263657
#> 40 45 -5.303305 0.11601965
#> 41 53 -5.303305 0.15502463
#> 42 38 -5.303305 0.08711418
#> 43 52 -5.303305 0.14980075
#> 44 57 -5.303305 0.17691518
#> 45 52 -5.303305 0.14980075
#> 46 53 -5.303305 0.15502463
#> 47 53 -5.303305 0.15502463
#> 48 59 -5.303305 0.18845746
#> 49 45 -5.303305 0.11601965
#> 50 50 -5.303305 0.13965149
#> 51 53 -5.303305 0.15502463
#> 52 51 -5.303305 0.14467637
#> 53 47 -5.303305 0.12517388
#> 54 41 -5.303305 0.09890523
#> 55 50 -5.303305 0.13965149
#> 56 47 -5.303305 0.12517388
#> 57 50 -5.303305 0.13965149
#> 58 47 -5.303305 0.12517388
#> 59 45 -5.303305 0.11601965
#> 60 41 -5.303305 0.09890523
#> 61 54 -5.303305 0.16034801
#> 62 49 -5.303305 0.13472612
#> 63 47 -5.303305 0.12517388
#> 64 49 -5.303305 0.13472612
#> 65 60 -5.303305 0.19437786
#> 66 45 -5.303305 0.11601965
#> 67 45 -5.303305 0.11601965
#> 68 44 -5.303305 0.11159179
#> 69 40 -5.303305 0.09487537
#> 70 50 -5.303305 0.13965149
#> 71 41 -5.303305 0.09890523
#> 72 48 -5.303305 0.12990025
#> 73 48 -5.303305 0.12990025
#> 74 45 -5.303305 0.11601965
#> 75 53 -5.303305 0.15502463
#> 76 42 -5.303305 0.10303458
#> 77 48 -5.303305 0.12990025
#> 78 46 -5.303305 0.12054702
#> 79 60 -5.303305 0.19437786
#> 80 51 -5.303305 0.14467637
#> 81 55 -5.303305 0.16577090
#> 82 49 -5.303305 0.13472612
#> 83 53 -5.303305 0.15502463
#> 84 52 -5.303305 0.14980075
#> 85 61 -5.303305 0.20039776
#> 86 53 -5.303305 0.15502463
#> 87 48 -5.303305 0.12990025
#> 88 47 -5.303305 0.12517388
#> 89 55 -5.303305 0.16577090
#> 90 35 -5.303305 0.07621866
#> 91 56 -5.303305 0.17129328
#> 92 42 -5.303305 0.10303458
#> 93 58 -5.303305 0.18263657
#> 94 44 -5.303305 0.11159179
#> 95 53 -5.303305 0.15502463
#> 96 34 -5.303305 0.07278582
#> 97 50 -5.303305 0.13965149
#> 98 49 -5.303305 0.13472612
#> 99 49 -5.303305 0.13472612
#> 100 52 -5.303305 0.14980075