table.stats: Verification statistics for a 2 by 2 Contingency Table

In verification: Weather Forecast Verification Utilities

Description

Provides a variety of statistics for a data summarized in a 2 by 2 contingency table.

Usage

       	table.stats(obs, pred, fudge = 0.01, silent = FALSE)

Arguments

obs	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.)
pred	Either null or a vector of binary forecasts.
fudge	A numeric fudge factor to be added to each cell of the contingency table in order to avoid division by zero.
silent	Should warning statements be surpressed.

Value

tab.out	Contingency table
TS	Threat score a.k.a. Critical success index (CSI)
TS.se	Standard Error for TS
POD	Hit Rate aka probability of detection
POD.se	Standard Error for POD
M	Miss rate
F	False Alarm RATE
F.se	Standard Error for F
FAR	False Alarm RATIO
FAR.se	Standard Error for FAR
HSS	Heidke Skill Score
HSS.se	Standard Error for HSS
PSS	Peirce Skill Score
PSS.se	Standard Error for PSS
KSS	Kuiper's Skill Score
PC	Percent correct - events along the diagonal.
PC.se	Standard Error for PC
BIAS	Bias
ETS	Equitable Threat Score
ETS.se	Standard Error for ETS
theta	Odds Ratio
log.theta	Log Odds Ratio
LOR.se	Standard Error for Log Odds Ratio
n.h	Degrees of freedom for log.theta
orss	Odds ratio skill score, aka Yules's Q
ORSS.se	Standard Error for Odds ratio skill score
eds	Extreme Dependency Score
esd.se	Standard Error for EDS
seds	Symmetric Extreme Dependency Score
seds.se	Standard Error for Symmetric Extreme Dependency Score
EDI	Extreme Dependency Index
EDI.se	Standard Error for EDI
SEDI	Symmetric EDI
SEDI.se	Standard Error for SEDI

Note

Initially, table.stats was an internal function used by verify for binary events and multi.cont for categorical events. But occassionally, it is nice to use it directly.

Author(s)

Matt Pocernich

References

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.

See Also

verify and multi.cont

Examples

DAT<- matrix(c(28, 23, 72, 2680 ), ncol = 2) ## Finley
table.stats(DAT)

Example output

Loading required package: fields
Loading required package: spam
Loading required package: dotCall64
Loading required package: grid
Spam version 2.2-2 (2019-03-07) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction 
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.

Attaching package: 'spam'

The following objects are masked from 'package:base':

    backsolve, forwardsolve

Loading required package: maps
See https://github.com/NCAR/Fields for
 an extensive vignette, other supplements and source code 
Loading required package: boot
Loading required package: CircStats
Loading required package: MASS
Loading required package: dtw
Loading required package: proxy

Attaching package: 'proxy'

The following object is masked from 'package:spam':

    as.matrix

The following objects are masked from 'package:stats':

    as.dist, dist

The following object is masked from 'package:base':

    as.matrix

Loaded dtw v1.20-1. See ?dtw for help, citation("dtw") for use in publication.

[1] " Assume data entered as c(n11, n01, n10, n00) Obs*Forecast"
[1] " Assume contingency table has observed values in columns, forecasts in rows"
$tab
     [,1] [,2]
[1,]   28   72
[2,]   23 2680

$TS
[1] 0.2276238

$TS.se
[1] 0.03278027

$POD
[1] 0.548912

$POD.se
[1] 0.06967137

$M
[1] 0.450892

$F
[1] 0.0261627

$F.se
[1] 0.003042702

$FAR
[1] 0.719928

$FAR.se
[1] 0.03469411

$HSS
[1] 0.3553249

$HSS.se
[1] 0.04613537

$PSS
[1] 0.5229453

$PSS.se
[1] 0.06973778

$KSS
[1] 0.5228568

$PC
[1] 0.9661043

$PC.se
[1] 0.003245228

$BIAS
[1] 1.960784

$ETS
[1] 0.2160456

$ETS.se
[1] 0.03411173

$theta
[1] 45.31401

$log.theta
[1] 3.813616

$LOR.se
[1] 0.3057034

$n.h
[1] 10.70039

$orss
[1] 0.9568165

$orss.se
[1] 0.0129163

$eds
[1] 0.7396484

$eds.se
[1] 0.04793713

$seds
[1] 0.5934675

$seds.se
[1] 0.04390902

$EDI
[1] 0.7172833

$EDI.se
[1] 0.06166522

$SEDI
[1] 0.7527249

$SEDI.se
[1] 0.06043495

verification documentation built on May 2, 2019, 1:24 a.m.