Wilcoxon: Difference between revisions
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imported>Scott (Created page with "===Purpose=== Pairwise Wilcoxon signed rank test for evaluating residuals from two models. ===Synopsis=== :prob = wilcoxon(err_1,err_2) ===Description=== Pairwise comparis...") |
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===Purpose=== | ===Purpose=== | ||
Pairwise Wilcoxon signed rank test for evaluating residuals from two models. | Pairwise Wilcoxon signed rank test for evaluating residuals from two models. | ||
===Synopsis=== | ===Synopsis=== | ||
:prob = wilcoxon(err_1,err_2) | :prob = wilcoxon(err_1,err_2) | ||
===Description=== | ===Description=== | ||
Pairwise comparison between two sets of model residuals using the sign and magnitude of residuals from two models. Output is the probability that model 2 (the model producing the second set of residuals) is better than model 1 (the model that produces the first set of residuals). | |||
Adapted from: ''Edward V. Thomas, "Non-parametric statistical methods for multivariate calibration model selection and comparison", J. Chemometrics 2003; 17: 653659. Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cem.833'' | |||
====Inputs==== | ====Inputs==== | ||
* '''err_1''' = Prediction errors from model #1 | * '''err_1''' = Prediction errors from model #1 | ||
| Line 19: | Line 14: | ||
====Outputs==== | ====Outputs==== | ||
* '''prob''' = Prob{# of times model#2 wins <=k} Probability that model#2 is better than model#1. | * '''prob''' = Prob{# of times model#2 wins <=k} Probability that model#2 is better than model#1. | ||
===See Also=== | ===See Also=== | ||
[[crossval]], [[randomttest]], [[ | [[crossval]], [[randomttest]], [[signtest]] | ||
Latest revision as of 15:07, 27 September 2011
Purpose
Pairwise Wilcoxon signed rank test for evaluating residuals from two models.
Synopsis
- prob = wilcoxon(err_1,err_2)
Description
Pairwise comparison between two sets of model residuals using the sign and magnitude of residuals from two models. Output is the probability that model 2 (the model producing the second set of residuals) is better than model 1 (the model that produces the first set of residuals).
Adapted from: Edward V. Thomas, "Non-parametric statistical methods for multivariate calibration model selection and comparison", J. Chemometrics 2003; 17: 653659. Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cem.833
Inputs
- err_1 = Prediction errors from model #1
- err_2 = Prediction errors from model #2
Outputs
- prob = Prob{# of times model#2 wins <=k} Probability that model#2 is better than model#1.