### Introduction

### Theory, Examples & Exercises

en:varpart_r

Section: Ordination analysis

(library`varpart`

`vegan`

) - variation partitioning (using RDA, CCA or db-RDA) among up to four matrices of environmental variables. First argument (`Y`

) is dependent variable, usually the matrix of species composition (the function calculates RDA, or, if`chisquare = TRUE`

, CCA), but could be also only a single variable (in that case it calculates linear regression) or distance matrix (applying db-RDA using the function`capscale`

). Further arguments (up to four) are (groups of) explanatory variables. The function uses either formula interface (with ~, see examples) or matrices. The interpretation should be based on adjusted R^{2}, although raw R^{2}is also reported (for CCA, adjusted R2 is calculated by permutation method of Peres-Neto et al. 2006 and may slightly vary between re-analyses of the same data; the argument`permutations`

specifies the number of permutations used to calculate adjusted R^{2}in CCA).(library`plot.varpart`

`vegan`

) - draws Venn's diagram with fractions of explained variation. In default setting it doesn't show negative values of explained variation (argument`cutoff = 0`

). The function can use arguments of`showvarparts`

below, e.g. to add the labels for individual (groups of) variables (`Xnames`

), or colors of the fractions (`bg`

). Consult`?plot.varpart`

for more details.(library`showvarparts`

`vegan`

) - draws schema of Venn's diagram with codes of individual fractions.

en/varpart_r.txt · Last modified: 2019/04/06 18:03 by David Zelený