diff --git a/articles/metric-details.html b/articles/metric-details.html index b26c21b75..70eecb39c 100644 --- a/articles/metric-details.html +++ b/articles/metric-details.html @@ -79,7 +79,7 @@

Nikos Bosse

-

2023-11-28

+

2023-11-29

Source: vignettes/metric-details.Rmd
metric-details.Rmd
diff --git a/articles/scoring-forecasts-directly.html b/articles/scoring-forecasts-directly.html index 6ad8d5472..c507f609d 100644 --- a/articles/scoring-forecasts-directly.html +++ b/articles/scoring-forecasts-directly.html @@ -79,7 +79,7 @@

Nikos Bosse

-

2023-11-28

+

2023-11-29

Source: vignettes/scoring-forecasts-directly.Rmd
scoring-forecasts-directly.Rmd
@@ -113,17 +113,17 @@

Bias observed <- rpois(30, lambda = 1:30) predicted <- replicate(200, rpois(n = 30, lambda = 1:30)) bias_sample(observed, predicted) -#> [1] -0.630 0.460 -0.765 -0.450 0.290 0.010 -0.875 -0.300 0.175 0.235 -#> [11] 0.905 0.175 0.990 -0.675 -0.305 0.005 -0.420 0.410 -0.430 0.265 -#> [21] 0.025 -0.640 0.835 0.770 0.705 -0.305 -0.865 -0.255 -0.460 0.515 +#> [1] -0.150 -0.640 -0.815 0.680 -0.085 0.815 -0.260 0.060 -0.265 -0.660 +#> [11] 0.650 -0.185 0.740 0.195 -0.420 -0.535 0.385 0.305 0.160 0.145 +#> [21] 0.000 0.115 -0.670 0.420 -0.300 0.590 -0.165 0.435 0.375 -0.935 ## continuous forecasts observed <- rnorm(30, mean = 1:30) predicted <- replicate(200, rnorm(30, mean = 1:30)) bias_sample(observed, predicted) -#> [1] -0.28 0.16 -0.15 0.23 0.19 -0.15 -0.18 -0.98 -0.02 -0.39 0.15 0.96 -#> [13] 0.53 -0.63 0.49 0.29 -0.50 -0.48 -0.03 0.88 -0.40 0.72 -0.56 -0.91 -#> [25] 0.52 0.64 0.89 -0.92 -0.17 0.75 +#> [1] -0.87 0.87 0.22 0.06 0.41 0.07 -0.28 0.34 -0.01 1.00 -0.65 0.61 +#> [13] 0.45 0.33 -0.15 -0.78 0.99 0.34 -0.50 -0.43 0.60 0.13 0.76 -0.05 +#> [25] 0.18 -0.37 -0.94 -0.06 -0.51 0.81

Sharpness @@ -138,9 +138,9 @@

Sharpness
 predicted <- replicate(200, rpois(n = 30, lambda = 1:30))
 mad_sample(predicted = predicted)
-#>  [1] 1.4826 1.4826 1.4826 2.9652 2.9652 2.9652 2.9652 2.9652 2.9652 2.9652
-#> [11] 2.9652 2.9652 4.4478 2.9652 3.7065 4.4478 4.4478 4.4478 4.4478 4.4478
-#> [21] 4.4478 4.4478 5.9304 4.4478 4.4478 4.4478 5.9304 5.1891 6.6717 5.9304

+#> [1] 1.4826 1.4826 1.4826 1.4826 1.4826 2.9652 2.9652 2.9652 2.9652 2.9652 +#> [11] 2.9652 4.4478 2.9652 4.4478 4.4478 4.4478 4.4478 4.4478 4.4478 4.4478 +#> [21] 4.4478 4.4478 4.4478 5.9304 4.4478 4.4478 5.9304 4.4478 4.4478 4.4478

Calibration @@ -197,10 +197,10 @@

Continuous Ranked Probability observed <- rpois(30, lambda = 1:30) predicted <- replicate(200, rpois(n = 30, lambda = 1:30)) crps_sample(observed, predicted) -#> [1] 0.438800 1.294275 1.368550 0.498550 1.071675 0.679050 1.097750 1.926825 -#> [9] 1.196475 1.258075 1.155800 2.689575 3.350275 3.186950 2.579975 3.959425 -#> [17] 1.214450 8.999100 1.964450 2.786325 2.034075 1.506925 2.042175 1.528450 -#> [25] 4.562200 2.169100 1.411075 4.042925 1.168175 6.392875

+#> [1] 0.598850 0.330325 0.805400 0.727300 1.006800 0.571475 0.783550 2.326300 +#> [9] 0.757300 1.194825 0.900300 0.847800 3.568025 1.216400 1.005575 1.568925 +#> [17] 2.105850 4.121650 2.763150 9.077400 1.695000 1.442325 2.822500 8.750775 +#> [25] 6.054200 1.739825 8.494275 1.889400 7.316350 3.049750

Dawid-Sebastiani Score (DSS) @@ -214,11 +214,11 @@

Dawid-Sebastiani Score (DSS)observed <- rpois(30, lambda = 1:30) predicted <- replicate(200, rpois(n = 30, lambda = 1:30)) dss_sample(observed, predicted) -#> [1] 0.8955598 3.1165801 2.4604536 3.7078737 1.6984349 3.4763279 1.8845007 -#> [8] 2.2095924 4.0164646 2.5680870 2.8765696 3.3235776 3.3424796 2.9959351 -#> [15] 5.7503039 3.8279437 3.2871708 3.5607386 8.0268345 3.0152728 4.2535215 -#> [22] 3.4993797 3.8071983 5.9717719 3.7610442 6.4643302 3.5688861 3.3817798 -#> [29] 5.1930587 3.4113845

+#> [1] 0.8444483 0.5596322 16.5535366 2.4938895 2.4357968 2.6493248 +#> [7] 2.0738007 3.3996446 2.5559438 2.4644238 2.6801078 3.8685390 +#> [13] 2.6291814 2.6575992 2.5963740 3.7304259 2.7806829 3.6480289 +#> [19] 3.1914089 3.7919271 3.1913286 3.0987188 3.1796194 3.5042409 +#> [25] 3.8410857 4.1611087 3.3980257 4.1634345 3.9703019 3.8689574

Log Score @@ -234,11 +234,11 @@

Log Scoreobserved <- rnorm(30, mean = 1:30) predicted <- replicate(200, rnorm(n = 30, mean = 1:30)) logs_sample(observed, predicted) -#> [1] 1.7878986 0.9714843 1.0628060 1.1303358 1.2058115 4.0448019 1.1785507 -#> [8] 1.2677081 1.5941806 1.9444053 1.2365641 1.2610836 1.3622606 2.2912402 -#> [15] 1.7214432 1.3677358 0.9553357 1.0404751 1.2626909 1.0067831 1.1552329 -#> [22] 1.5204914 1.0171378 1.0172942 1.5747560 2.4139952 1.0227707 1.1093289 -#> [29] 1.0303699 1.6105244

+#> [1] 1.3639180 1.1721622 0.9837552 1.1223984 1.2141230 1.1787707 0.9073846 +#> [8] 1.9390409 1.2006537 1.5294916 0.9598881 1.9307719 1.1043973 1.0898149 +#> [15] 1.0772859 1.1025971 0.9462241 0.8310544 3.3920656 0.9183323 1.1210145 +#> [22] 1.1412519 1.3032000 2.9945874 1.2448300 1.1788962 1.1406896 1.2570678 +#> [29] 1.2374300 1.0551493

Brier Score @@ -256,12 +256,11 @@

Brier Scorepredicted <- runif(n = 30, min = 0, max = 1) brier_score(observed, predicted) -#> [1] 9.260626e-01 4.016684e-01 8.564615e-01 1.227433e-01 3.504490e-01 -#> [6] 3.328103e-02 4.806220e-02 1.360908e-01 2.254118e-01 8.913470e-01 -#> [11] 6.473736e-01 5.786271e-01 1.258172e-01 9.912017e-01 6.127094e-01 -#> [16] 5.064337e-01 9.577694e-08 6.116021e-01 2.795541e-01 6.377789e-02 -#> [21] 2.370255e-01 7.755936e-01 1.224358e-02 2.511397e-01 9.343368e-01 -#> [26] 1.207449e-02 5.483555e-01 6.315328e-02 4.231578e-01 4.648896e-01

+#> [1] 0.112259247 0.564562618 0.222293787 0.021831261 0.790523726 0.440207593 +#> [7] 0.317289909 0.010112601 0.063597143 0.557275438 0.867665126 0.288636782 +#> [13] 0.723326701 0.043212427 0.003723609 0.676499784 0.383250903 0.483864329 +#> [19] 0.868022259 0.016770692 0.207787622 0.485208168 0.965358526 0.442452645 +#> [25] 0.065318462 0.104556347 0.861472188 0.334785513 0.042639655 0.795482392

Interval Score

@@ -301,11 +300,11 @@

Interval Score upper = upper, interval_range = interval_range ) -#> [1] 0.7386681 0.6890849 0.6481003 0.6577931 0.7021015 0.6525077 0.6405615 -#> [8] 0.7012594 0.6821138 0.6257076 0.7393191 0.8166930 0.7115758 0.5524686 -#> [15] 0.6240514 0.6240683 1.7273545 0.7715898 0.9254843 0.6871344 1.8055132 -#> [22] 0.8317843 0.6863535 0.6237477 0.7089224 0.7199854 0.6380707 0.7076533 -#> [29] 0.7479047 0.7054405

+#> [1] 0.6670564 0.7459922 0.5539554 1.2462373 0.6698641 0.7588718 0.6326608 +#> [8] 0.7020316 0.7733863 0.8357305 0.5821739 0.7204689 0.5707887 0.6594888 +#> [15] 0.7184740 0.7456016 1.8944255 0.6496035 0.5651959 0.6631289 0.6886410 +#> [22] 0.6441808 0.7138403 0.6287681 0.5477585 0.6669178 0.6891055 0.6213826 +#> [29] 0.6414575 0.9052694