EnsRoca: Area Under the ROC Curve

In easyVerification: Ensemble Forecast Verification for Large Data Sets

Area Under the ROC Curve

Description

Computes the area under the ROC curve given the observations. EnsRoca computes the Area Under the Curve (AUC). For ease of interpretation, EnsRocss converts the AUC to the range from -1 to 1 with zero indicating a forecast with no discrimination.

Usage

EnsRoca(ens, obs, use.easy = FALSE)

EnsRocss(ens, obs, use.easy = FALSE)

Arguments

ens	n x j matrix of n probability forecasts for j categories
obs	n x j matrix of occurence of n verifying observations in j categories
use.easy	logical, should implementation of standard errors as implemented in easyVerifcation be used (see below)?

Standard Error

If used with SpecsVerification >= 0.5, the standard errors as implemented in the function SpecsVerification::Auc are used.

If use.easy = TRUE or when used with an older version of SpecsVerification, the standard error \sigma of the ROC area skill score is given by the following formula after Broecker (2012).

\sigma^2 = \frac{1}{3} \left(\frac{1}{N_0} + \frac{1}{N_1} + \frac{1}{N_0 N_1} \right)

Where \sigma is the standard error, N_1 the number of events, and N_0 the number of non-events in category i.

References

Br\"ocker, J. (2012). Probability forecasts. Forecast Verification: A Practitioner's Guide in Atmospheric Science, Second Edition, 119-139.

See Also

veriApply, EnsRocss

Examples

tm <- toymodel()

## compute ROC area for tercile forecasts using veriApply
veriApply("EnsRoca", fcst = tm$fcst, obs = tm$obs, prob = 1:2 / 3)

easyVerification documentation built on Aug. 15, 2023, 9:06 a.m.