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en:rda_cca_examples [2019/02/06 11:29]
David Zelený
en:rda_cca_examples [2019/03/07 21:16]
David Zelený
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-====== Ordination analysis ======+Section: [[en:​ordination]]
 ===== RDA, tb-RDA, CCA & db-RDA (constrained ordination) ===== ===== RDA, tb-RDA, CCA & db-RDA (constrained ordination) =====
  
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 The two variables explain 8.9% of the variance (the row ''​Constrained''​ and column ''​Proportion''​ in the table above, can be calculated also as the sum of eigenvalues for the constrained axes divided by total variance (inertia): (0.04023+0.02227) /​0.70476=0.08869. The first constrained axis (RDA1) explains 0.04023/​0.70476=5.7% of the variance, while the second (RDA2) explains 0.02227/​0.70476=3.2%. Note that the first unconstrained axis (PC1) represents 0.07321/​0.70476=10.4% of total variance, which is more than both explanatory variables together; the first two unconstrained explain (0.07321+0.04857)/​0.70476=17.3%. This means that the dataset may be structured by some strong environmental variable(s) different from pH and soil depth (we will check this below). The two variables explain 8.9% of the variance (the row ''​Constrained''​ and column ''​Proportion''​ in the table above, can be calculated also as the sum of eigenvalues for the constrained axes divided by total variance (inertia): (0.04023+0.02227) /​0.70476=0.08869. The first constrained axis (RDA1) explains 0.04023/​0.70476=5.7% of the variance, while the second (RDA2) explains 0.02227/​0.70476=3.2%. Note that the first unconstrained axis (PC1) represents 0.07321/​0.70476=10.4% of total variance, which is more than both explanatory variables together; the first two unconstrained explain (0.07321+0.04857)/​0.70476=17.3%. This means that the dataset may be structured by some strong environmental variable(s) different from pH and soil depth (we will check this below).
 +
 +The relationship between the variation represented by individual (constrained and unconstrained) ordination axes can be displayed using the barplot on eigenvalues:​
 +<code rsplus>
 +constrained_eig <- tbRDA$CCA$eig/​tbRDA$CA$tot.chi*100
 +unconstrained_eig <- tbRDA$CA$eig/​tbRDA$CA$tot.chi*100
 +barplot (c(constrained_eig,​ unconstrained_eig),​ col = c(rep ('​red',​ length (constrained_eig)),​ rep ('​black',​ length (unconstrained_eig))),​ las = 2, ylab = '% variation'​)
 +</​code>​
 +
 +(note that all information about the eigenvalues and total inertia is in the object calculated by ''​vegan'''​s ordination function (''​rda''​ in this case, stored in the list ''​tbRDA''​),​ you just need to search a bit inside to find it - consider using the function ''​str''​ to check the structure of tbRDA first).
 +
 +{{:​obrazky:​tb_rda_vltava_barplot_eig.png?​direct|}}
 +
  
 Let's see the ordination diagram: Let's see the ordination diagram:
en/rda_cca_examples.txt · Last modified: 2019/03/07 21:16 by David Zelený