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en:ordination [2020/04/23 08:41]
David Zelený [Summary: Three alternative approaches for ordination]
en:ordination [2020/04/23 08:44] (current)
David Zelený [Summary: Three alternative approaches for ordination]
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 ==== Summary: Three alternative approaches for ordination ==== ==== Summary: Three alternative approaches for ordination ====
  
-The schemas below show the three alternative approaches you can use for the ordination of community ecology data, for either unconstrained or constrained ordination (<imgref three-alt-ord-approach>​). It does not make much sense to combine individual approaches within the same type of ordination. For example, you can decide to analyze your data by either ​1) PCA/CA methods (depending on whether community composition data are homogeneous or heterogeneous),​ or 2) transformation-based PCA (i.e. by pre-transforming your species composition data, e.g. by Hellinger standardization,​ and then using PCA; here doesn'​t matter whether community composition data are homogeneous or heterogeneous),​ or 3) by distance-based approaches like PCoA or NMDS. But it often does not make much sense to combine these approaches. For example, if you decide for approach ​1) above (PCA or CA), but in the case that you select PCA (since you concluded, e.g. by using preanalysis by DCA, that your data are reasonably homogeneous and you thus don't have to face double-zero problem), you also apply Hellinger standardization on your community composition data  - in this case, you, in the end, opt for approach ​above, and you didn't need to check the heterogeneity of compositional data at all. +The schemas below show the three alternative approaches you can use for the ordination of community ecology data, for either unconstrained or constrained ordination (<imgref three-alt-ord-approach>​). It does not make much sense to combine individual approaches within the same type of ordination. For example, you can decide to analyze your data by either ​a) PCA/CA methods (depending on whether community composition data are homogeneous or heterogeneous),​ or b) transformation-based PCA (i.e. by pre-transforming your species composition data, e.g. by Hellinger standardization,​ and then using PCA; here doesn'​t matter whether community composition data are homogeneous or heterogeneous),​ or c) by distance-based approaches like PCoA or NMDS. But it often does not make much sense to combine these approaches. For example, if you decide for approach ​a) above (PCA or CA), but in the case that you select PCA (since you concluded, e.g. by using preanalysis by DCA, that your data are reasonably homogeneous and you thus don't have to face double-zero problem), you also apply Hellinger standardization on your community composition data  - in this case, you, in the end, opt for approach ​b) above, and you didn't need to check the heterogeneity of compositional data at all. 
  
 <​imgcaption three-alt-ord-approach left|Three alternative approaches to unconstrained (above) and constrained (below) ordination analysis. From Legendre & Legendre (2012), slightly modified by D. Zelený.>​{{:​obrazky:​alternative_approaches_ordination_leg_leg_unconstrained.jpg?​direct|}}{{:​obrazky:​alternative_approaches_ordination_leg_leg_constrained.jpg?​direct|}}</​imgcaption>​ <​imgcaption three-alt-ord-approach left|Three alternative approaches to unconstrained (above) and constrained (below) ordination analysis. From Legendre & Legendre (2012), slightly modified by D. Zelený.>​{{:​obrazky:​alternative_approaches_ordination_leg_leg_unconstrained.jpg?​direct|}}{{:​obrazky:​alternative_approaches_ordination_leg_leg_constrained.jpg?​direct|}}</​imgcaption>​
en/ordination.txt · Last modified: 2020/04/23 08:44 by David Zelený