Editing Linear discriminant analysis (section)

==Effect size==
Some suggest the use of eigenvalues as [[effect size]] measures, however, this is generally not supported.<ref name="green"/> Instead, the [[canonical correlation]] is the preferred measure of effect size. It is similar to the eigenvalue, but is the square root of the ratio of ''SS''<sub>between</sub> and ''SS''<sub>total</sub>. It is the correlation between groups and the function.<ref name="green"/> 
	Another popular measure of effect size is the percent of variance{{clarify|date=April 2012}} for each function.  This is calculated by: (''λ<sub>x</sub>/Σλ<sub>i</sub>'') X 100 where ''λ<sub>x</sub>'' is the eigenvalue for the function and Σ''λ<sub>i</sub>'' is the sum of all eigenvalues. This tells us how strong the prediction is for that particular function compared to the others.<ref name="green"/> 
	Percent correctly classified can also be analyzed as an effect size. The kappa value can describe this while correcting for chance agreement.<ref name="green"/>{{clarify|date=April 2012|reason=Kappa normalizes across all categorizes rather than biased by a significantly good or poorly performing classes|text=Kappa normalizes across all categorizes rather than biased by a significantly good or poorly performing classes.|pre-text=|post-text=}}<ref>{{Cite journal|last=Israel|first=Steven A.|date=June 2006|title=Performance Metrics: How and When|journal=Geocarto International|volume=21|issue=2|pages=23–32|doi=10.1080/10106040608542380|bibcode=2006GeoIn..21...23I |s2cid=122376081|issn=1010-6049}}</ref>