ade4: varipart – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

varipart

Partition of the variation of a response multivariate table by 2 explanatory tables

Description

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.

Usage

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

Arguments

`Y`	a vector, matrix or data frame or an object of class `dudi`. If not a `dudi` object, the data are trated by a principal component analysis (`dudi.pca`).
`X, W`	dataframes or matrices of explanatory (co)variables (numeric and/or factor variables). By default, no covariables are considered (`W` is `NULL`) and this case corresponds to simple caonical ordination.
`nrepet`	an integer indicating the number of permutations .
`type`	a character specifying the algorithm which should be used to adjust R-squared (either `"simulated"` or \ code"parametric").
`scale`	If `Y` is not a dudi, a `logical` indicating if variables should be scaled
`...`	further arguments passed to `as.krandtest` or `as.randtest` (if no covariables are considered) for function `varipart`.
`x`	an object of class `varipart`

Details

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.

Value

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

Author(s)

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

References

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.

Examples

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)

ade4

Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences

v1.7-16

GPL (>= 2)

Authors

Stéphane Dray <stephane.dray@univ-lyon1.fr>, Anne-Béatrice Dufour <anne-beatrice.dufour@univ-lyon1.fr>, and Jean Thioulouse <jean.thioulouse@univ-lyon1.fr>, with contributions from Thibaut Jombart, Sandrine Pavoine, Jean R. Lobry, Sébastien Ollier, Daniel Borcard, Pierre Legendre, Stéphanie Bougeard and Aurélie Siberchicot. Based on earlier work by Daniel Chessel.

Initial release