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en:rda_cca_examples

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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