varipart | R Documentation |

The function partitions the variation of a response table (usually community data) with respect to two
explanatory tables. The function performs the variation partitioning based on
redundancy analysis (RDA, if `dudiY`

is obtained by `dudi.pca`

) or canonical correspondance analysis (CCA, if `dudiY`

is obtained by `dudi.coa`

)
and computes
unadjusted and adjusted R-squared.
The significance of R-squared are evaluated by a randomization procedure
where the rows of the explanatory tables are permuted.

varipart(Y, X, W = NULL, nrepet = 999, type = c("simulated", "parametric"), scale = FALSE, ...) ## S3 method for class 'varipart' print(x, ...)

`Y` |
a vector, matrix or data frame or an object of class |

`X, W` |
dataframes or matrices of explanatory (co)variables (numeric and/or factor
variables). By default, no covariables are considered ( |

`nrepet` |
an integer indicating the number of permutations . |

`type` |
a character specifying the algorithm which should be used to adjust R-squared (either |

`scale` |
If |

`...` |
further arguments passed to |

`x` |
an object of class |

Two types of algorithm are provided to adjust R-squared. The "simulated" procedure estimates the unadjusted R-squared expected under the null hypothesis H0 and uses it to adjust the observed R-squared as follows: R2.adj = 1 - (1 - R2) / (1 - E(R2|H0)) with R2.adj the adjusted R-squared and R2 the unadjusted R-squared. The "parametric" procedure performs the Ezequiel's adjustement on the unadjusted R-squared as: R2.adj = 1 - (1 - R2) / (1 - p / (n - 1)) where n is the number of sites, and p the number of predictors.

It returns an object of class `varipart`

. It is a `list`

with:

`test`

the significance test of fractions [ab], [bc], and [abc] based on randomization procedure. An object of class

`krandtest`

`R2`

unadjusted estimations of fractions [a], [b], [c], and [d]

`R2.adj`

adjusted estimations of fractions [a], [b], [c], and [d]

`call`

the matched call

Stephane Dray stephane.dray@univ-lyon1.fr and Sylvie Clappe sylvie.clappe@univ-lyon1.fr

Borcard, D., P. Legendre, and P. Drapeau. 1992. Partialling out the spatial component of ecological variation. Ecology 73:1045.

Peres-Neto, P. R., P. Legendre, S. Dray, and D. Borcard. 2006. Variation partitioning of species data matrices: estimation and comparison of fractions. Ecology 87:2614-2625.

`pcaiv`

data(mafragh) # PCA on response table Y Y <- mafragh$flo dudiY <- dudi.pca(Y, scannf = FALSE, scale = FALSE) # Variation partitioning based on RDA # without covariables vprda <- varipart(dudiY, mafragh$env) vprda # Variation partitioning based on RDA # with covariables and parametric estimation vprda <- varipart(dudiY, mafragh$env, mafragh$xy, type = "parametric") vprda names(vprda)

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