Provides a variety of statistics for a data summarized in a 2 by 2 contingency table.
table.stats(obs, pred, fudge = 0.01, silent = FALSE)
Contingency table
Threat score a.k.a. Critical success index (CSI)
Standard Error for TS
Hit Rate aka probability of detection
Standard Error for POD
Miss rate
False Alarm RATE
Standard Error for F
False Alarm RATIO
Standard Error for FAR
Heidke Skill Score
Standard Error for HSS
Peirce Skill Score
Standard Error for PSS
Kuiper's Skill Score
Percent correct - events along the diagonal.
Standard Error for PC
Bias
Equitable Threat Score
Standard Error for ETS
Odds Ratio
Log Odds Ratio
Standard Error for Log Odds Ratio
Degrees of freedom for log.theta
Odds ratio skill score, aka Yules's Q
Standard Error for Odds ratio skill score
Extreme Dependency Score
Standard Error for EDS
Symmetric Extreme Dependency Score
Standard Error for Symmetric Extreme Dependency Score
Extreme Dependency Index
Standard Error for EDI
Symmetric EDI
Standard Error for SEDI
Either a vector of contingency table counts, a vector of binary observations, or a 2 by 2 matrix in the form of a contingency table. (See note below.)
Either null or a vector of binary forecasts.
A numeric fudge factor to be added to each cell of the contingency table in order to avoid division by zero.
Should warning statements be surpressed.
Matt Pocernich
Jolliffe, I.T. and D.B. Stephenson (2003). Forecast verification: a practitioner's guide in atmospheric science. John Wiley and Sons. See chapter 3 concerning categorical events.
Stephenson, D.B. (2000). "Use of `Odds Ratio for Diagnosing Forecast Skill." Weather and Forecasting 15 221-232.
Hogan, R.J., O'Connor E.J. and Illingworth, 2009. "Verification of cloud-fraction forecasts." Q.J.R. Meteorol. Soc. 135, 1494-1511.
verify and multi.cont
DAT<- matrix(c(28, 23, 72, 2680 ), ncol = 2) ## Finley
table.stats(DAT)
Run the code above in your browser using DataLab