Dear friends, the data are ordered real numbers between 0 and 1. something of that sort: observations site4 0.3 site1 0.4 site5 0.42 . . . siten 0.999 after being sorted. I would like the models to order the sites in the same order, even if the values are not right. That is ideally: model1 site4 2.4 site1 2.8 site5 4.0 . . . siten 32 As you see, the values are different, but the order is still the same, that is if I sort the values, I still get the same sorting order. 1) I do not understand how to use AIC, ROC or Spearmann in this situation!? Could you pleas explain? 2) I had thought about using Kendal or Wilcoxon. What do you think? Regards
On Tuesday 03 November 2009 14:49:32 Aitor Gast?n wrote:
You can compute Spearman's rank correlation coefficient using cor() between predicted and observed values for each model and test differences. If observed data are binary and the predictions probabilities, you may use a discrimination statistic like Somers' Dxy Rank Correlation or receiver operating characteristic curve area using somers2() in Hmisc package. Hope this helps, Aitor ----- Original Message ----- From: "Corrado" <ct529 at york.ac.uk> To: <r-sig-ecology at r-project.org> Sent: Tuesday, November 03, 2009 1:36 PM Subject: [R-sig-eco] Testing "order" on predicted data
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, 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
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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