Trace:

en:varpart_r

This shows you the differences between two versions of the page.

Both sides previous revision Previous revision | Last revision Both sides next revision | ||

en:varpart_r [2019/02/26 23:37] David Zelený |
en:varpart_r [2019/04/06 18:01] David Zelený [R functions] |
||
---|---|---|---|

Line 7: | Line 7: | ||

[[{|width: 7em; background-color: white; color: navy}varpart_exercise|Exercise {{::lock-icon.png?nolink|}}]] | [[{|width: 7em; background-color: white; color: navy}varpart_exercise|Exercise {{::lock-icon.png?nolink|}}]] | ||

==== R functions ==== | ==== R functions ==== | ||

- | * **''varpart''** (library ''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''). The further arguments (up to four) are (groups of) explanatory variables. The function uses either formula interface (with ~, see [[en:varpart_examples|examples]]) or matrices. The interpretation should be based on adjusted R<sup>2</sup>, although raw R<sup>2</sup> 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). | + | * **''varpart''** (library ''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 [[en:varpart_examples|examples]]) or matrices. The interpretation should be based on adjusted R<sup>2</sup>, although raw R<sup>2</sup> 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). |

* **''plot.varpart''** (library ''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. | * **''plot.varpart''** (library ''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. | ||

* **''showvarparts''** (library ''vegan'') - draws schema of Venn's diagram with codes of individual fractions. | * **''showvarparts''** (library ''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