Klecka's tau
Klecka's tau (τ) is a statistic which is used to test whether a given classification analysis improves one's classification to groups over a random allocation to the various groups under consideration.[1] The maximum value of τ is 1.0 indicating no errors in the prediction. A value of zero indicates no improvement over a random assignment.
The distribution of τ is not presently known and it is used as a descriptive rather than as an analytic statistic.
Rationale for use
Klecka's τ was developed for use with
Mathematical formulation
τ is defined as[citation needed]
where ncorr is the number of cases correctly classified, ni is the number of cases in the ith group, N is the total number of cases, T is the number of groups and pi is the probability of a case being allocated to that group by chance (pi = 1 / T ).
Uses
In addition to its use in discriminant analysis
References
- ^ Klecka, WR (1980) Discriminant analysis. Sage Publications, Beverly Hills
- ^ Murphy AMC (2002) The calcaneus: sex assessment of prehistoric New Zealand Polynesian skeletal remains. Forensic Sci Int 129(3) 205–208
- ^ Murphy AMC (1986) Determination of sex by discriminant function analysis of New Zealand Polynesian pectoral girdles: forensic science applications. J Anat 149, 249-268
- ^ Taylor JV, Dibennardo R (1984) Discriminant function analysis of the central portion of the innominate. Am J Phys Anthropol 64 (3) 315–320
- ^ Stromberg MR (1986) Systematics and conservation of the swift fox, Vulpes velox, in North America. Biolog Conservation 35(2) 97–110
- ^ Closea ME & Davies‐Colley RJ (1990) Baseflow water chemistry in New Zealand rivers 2. Influence of environmental factors. NZ J Marine Freshwater Res 24(3) 343-356
- ^ Khemani RS, Shapiro DM (1993) An empirical analysis of Canadian merger policy. J Indust Econ 41 (2) 161-177
- ^ Dattaloa P (1995) A comparison of discriminant analysis and logistic regression. J Social Service Res 19 (3-4): 121-144
- ^ Jiang S, Liu D (2011) On chance-adjusted measures for accuracy assessment in remote sensing image classification. ASPRS 2011 Annual Conference Milwaukee, Wisconsin