Testing "order" on predicted data
Dear Sarah, I do not understand the question. I am not looking for any relationships between data, only rank order correspondence, which means the nearer is the rank order equivalence the better it is. I have tried to explain in my 2 emails, probably failing. The number of variables is normally one, as in my second email. I considered Kendal and Wilcoxon (and also Friedman), but I am not sure which one is better (that is better at comparing rank orders). Another example, to simplify the question: if you have ten judges evaluating the quality of 10 products by ranking from the best (1) to the worst (10) and you want to discover which couple of judges did provide the most similar ranking for the products, which test would you use? Best,
On Tuesday 03 November 2009 15:36:59 Sarah Goslee wrote:
You really don't give enough information - what's "better"? Are you looking for linear relationships? Single variables or many? Without knowing anything else, I think you might try looking at Spearman (rank) correlations. Sarah On Tue, Nov 3, 2009 at 7:36 AM, Corrado <ct529 at york.ac.uk> wrote:
Dear all, I have a strange situation: 1) I have some data that are associated with "sites" 2) I have two models that predict the data on the "sites" 3) I would like to understand which of the models predicts the order of the data better. In other words, I am not interested in the models predicting the values exactly, but only in predicting values that are in the same order (smaller to bigger). What is the best test? PS: Does that make sense? Best, -- Corrado Topi
Corrado Topi Global Climate Change & Biodiversity Indicators Area 18,Department of Biology University of York, York, YO10 5YW, UK Phone: + 44 (0) 1904 328645, E-mail: ct529 at york.ac.uk