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en:suppl_vars [2019/03/16 06:13]
David Zelený [Multiple testing issue and available corrections]
en:suppl_vars [2019/03/16 06:14]
David Zelený [Supplementary variables (unconstrained ordination)]
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 [[{|width: 7em; background-color:​ white; color: navy}suppl_vars_exercise|Exercise {{::​lock-icon.png?​nolink|}}]] [[{|width: 7em; background-color:​ white; color: navy}suppl_vars_exercise|Exercise {{::​lock-icon.png?​nolink|}}]]
  
-The ecological meaning of the axes in unconstrained ordination can be interpreted by relating the sample scores on these axes to external supplementary variables (usually measured or estimated environmental variables). This relationship can be done either by correlating the supplementary variable to the first two or few main axes using Pearson’s correlation coefficient or by regressing the supplementary on the sample scores of selected ordination axes using (weighted) multiple regression. The **correlation** method is more intuitive to understand, but the application is limited only to linear ordination methods, while the **(weighted) multiple regression** is less intuitive, but more general, applicable to both linear and unimodal ordination methods. The results are often used to project supplementary variables passively onto the ordination diagram while reporting the strength of the relationship with ordination axes (correlation coefficient in the case of correlation,​ r<​sup>​2</​sup>​ in the case of multiple regression) and possibly also the test of significance. There is a difference between linear and unimodal ordination method; while the linear method all samples have the same weight, in the unimodal method the sample weight (its importance in the analysis) is proportional to the sum of species abundances in this sample. This has to be reflected when the supplementary variables are related to ordination axes, and the weights need to be included in the calculation (that’s why weighted multiple regression is used).+The ecological meaning of the axes in unconstrained ordination can be interpreted by relating the sample scores on these axes to external supplementary variables (usually measured or estimated environmental variables). This relationship can be done either by correlating the supplementary variable to the first two or few main axes using Pearson’s correlation coefficient or by regressing the supplementary on the sample scores of selected ordination axes using (weighted) multiple regression. The **correlation** method is more intuitive to understand, but the application is limited only to linear ordination methods, while the **(weighted) multiple regression** is less intuitive, but more general, applicable to both linear and unimodal ordination methods. The results are often used to project supplementary variables passively onto the ordination diagram while reporting the strength of the relationship with ordination axes (correlation coefficient in the case of correlation,​ r<​sup>​2</​sup>​ in the case of multiple regression) and possibly also the test of significance. There is a difference between linear and unimodal ordination method; while in the linear method all samples have the same weight, in the unimodal method the sample weight (its importance in the analysis) is proportional to the sum of species abundances in this sample. This has to be reflected when the supplementary variables are related to ordination axes, and the weights need to be included in the calculation (that’s why weighted multiple regression is used).
  
 ==== Correlation of supplementary variable with selected ordination axes ==== ==== Correlation of supplementary variable with selected ordination axes ====
en/suppl_vars.txt · Last modified: 2019/03/16 06:20 by David Zelený