scoring: Proper scoring rules

• CRAN: http://cran.r-project.org/web/packages/scoring/index.html

> library(scoring)
> data("WeatherProbs")
> data("WorldEvents")

バージョン: 0.5.1

WeatherProbs

> data("WeatherProbs")
> WeatherProbs %>% dplyr::glimpse()

Observations: 8,976
Variables: 11
$ stn   (int) 69002, 69007, 69008, 69012, 69013, 69014, 69016, 69017, ...
$ made  (date) 2009-04-01, 2009-04-01, 2009-04-01, 2009-04-01, 2009-04...
$ valid (date) 2009-04-09, 2009-04-09, 2009-04-09, 2009-04-09, 2009-04...
$ tblw  (dbl) 0.4341, 0.4243, 0.4443, 0.2936, 0.3200, 0.4242, 0.4249, ...
$ tnrm  (dbl) 0.3333, 0.3333, 0.3333, 0.4146, 0.3866, 0.3333, 0.3333, ...
$ tabv  (dbl) 0.2326, 0.2424, 0.2224, 0.2918, 0.2934, 0.2425, 0.2418, ...
$ tcat  (fctr) 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2...
$ pblw  (dbl) 0.3140, 0.3141, 0.3141, 0.3059, 0.3276, 0.3146, 0.3146, ...
$ pnrm  (dbl) 0.3835, 0.3759, 0.3841, 0.3333, 0.3333, 0.3755, 0.3755, ...
$ pabv  (dbl) 0.3025, 0.3100, 0.3018, 0.3608, 0.3391, 0.3099, 0.3099, ...
$ pcat  (fctr) 2, 2, 2, 3, 3, 2, 2, 3, 2, 2, 1, 1, 2, 1, 1, 2, 2, 3, 2...

WorldEvents

> data("WorldEvents")
> WorldEvents %>% dplyr::glimpse()

Observations: 21
Variables: 4
$ forecaster (int) 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, ...
$ item       (int) 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, ...
$ answer     (int) 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, ...
$ forecast   (dbl) 0.51, 0.81, 0.41, 0.04, 0.15, 0.80, 0.51, 0.70, 0.7...

brierscore

Arguments

• object
• data
• group
• decomp
• bounds
• reverse

> brier1 <- brierscore(answer ~ forecast, data = WorldEvents)
> brier2 <- brierscore(answer ~ forecast, data = WorldEvents, group = "forecaster", decomp = TRUE)
> brier3 <- calcscore(answer ~ forecast, data = WorldEvents, param = 2, fam = "pow")
> all.equal(brier1, brier3)

[1] TRUE

calcscore

> scores <- calcscore(answer ~ forecast, 
+                     fam = "beta",
+                     param = c(1,1), 
+                     data = WorldEvents,
+                     bounds = c(0, 1))
> scores

 [1] 0.2601 0.6561 0.3481 0.0016 0.0225 0.0400 0.2601 0.4900 0.0625 0.0000
[11] 1.0000 0.0000 0.0400 0.6400 0.0400 0.0000 0.6400 0.0049 0.0361 0.2601
[21] 0.5625

sphscore

> sph1 <- sphscore(answer ~ forecast, data = WorldEvents)
> sph1

 [1] 0.3071739058 0.7716306565 0.4293433841 0.0008669269 0.0152164412
 [6] 0.0298574999 0.3071739058 0.6060807014 0.0513167019 0.0000000000
[11] 1.0000000000 0.0000000000 0.0298574999 0.7574643750 0.0298574999
[16] 0.0000000000 0.7574643750 0.0028207177 0.0264254302 0.3071739058
[21] 0.6837722340