**Introduction**

**Theory, Examples & Exercises**

*Unconstrained ordination**Constrained ordination*

**Data, Links & References**

**Other stuff**

Author: David Zelený

**Introduction**

**Theory, Examples & Exercises**

*Unconstrained ordination**Constrained ordination*

**Data, Links & References**

**Other stuff**

Author: David Zelený

en:rda

Constrained linear ordination method, which combines multiple regression with principal component analysis (PCA). The number of canonical axes corresponds to the number of explanatory variables, or more precisely to the number of degrees of freedoms (factor of k classes requires *k - 1* coding dummy variables, hence it brings *k - 1* degrees of freedom). Each canonical axis is linear combination of *all* explanatory variables (Borcard et al. 2011).

RDA can be calculated by function `rda`

from `vegan`

- you can use two different syntax:

- matrix syntax -
`RDA = rda (Y, X, W)`

, where`Y`

is the response matrix (species composition),`X`

is the explanatory matrix (environmental factors) and`W`

is the matrix of covariables - formula syntax -
`RDA = rda (Y ~ var1 + factorA + var2*var3 + Condition (var4), data = XW)`

- as explanatory are used: quantitative variable`var1`

, categorical variable`factorA`

, interaction term between`var2`

and`var3`

, whereas`var4`

is used as covariable and hence partialled out.

en/rda.txt · Last modified: 2017/02/16 14:35 by David Zelený