ade4: bwca.dpcoa – R documentation

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bwca.dpcoa

Between- and within-class double principal coordinate analysis

Description

These functions allow to study the variations in diversity among communities (as in dpcoa) taking into account a partition in classes

Usage

bwca.dpcoa(x, fac, cofac, scannf = TRUE, nf = 2, ...)
## S3 method for class 'dpcoa'
bca(x, fac, scannf = TRUE, nf = 2, ...) 
## S3 method for class 'dpcoa'
wca(x, fac, scannf = TRUE, nf = 2, ...) 
## S3 method for class 'betwit'
randtest(xtest, nrepet = 999, ...)
## S3 method for class 'betwit'
summary(object, ...)
## S3 method for class 'witdpcoa'
print(x, ...)
## S3 method for class 'betdpcoa'
print(x, ...)

Arguments

`x`	an object of class `dpcoa`
`fac`	a factor partitioning the collections in classes
`scannf`	a logical value indicating whether the eigenvalues barplot should be displayed
`nf`	if scannf FALSE, a numeric value indicating the number of kept axes
`...`	further arguments passed to or from other methods
`cofac`	a cofactor partitioning the collections in classes used as a covariable
`nrepet`	the number of permutations
`xtest, object`	an object of class `betwit` created by a call to the function `bwca.dpcoa`

Value

Objects of class betdpcoa, witdpcoa or betwit

Author(s)

Stéphane Dray stephane.dray@univ-lyon1.fr

References

Dray, S., Pavoine, S. and Aguirre de Carcer, D. (2015) Considering external information to improve the phylogenetic comparison of microbial communities: a new approach based on constrained Double Principal Coordinates Analysis (cDPCoA). Molecular Ecology Resources, 15, 242–249. doi:10.1111/1755-0998.12300

Examples

## Not run: 

## First example of Dray et al (2015) paper

con <- url("ftp://pbil.univ-lyon1.fr/pub/datasets/dray/MER2014/soilmicrob.rda")
load(con)
close(con)

## Partial CCA
coa <- dudi.coa(soilmicrob$OTU, scannf = FALSE)
wcoa <- wca(coa, soilmicrob$env$pH, scannf = FALSE)
wbcoa <- bca(wcoa,soilmicrob$env$VegType, scannf = FALSE)

## Classical DPCoA
dp <- dpcoa(soilmicrob$OTU, soilmicrob$dphy, RaoDecomp = FALSE, scannf = FALSE)

## Between DPCoA (focus on the effect of vegetation type)
bdp <- bca(dp, fac = soilmicrob$env$VegType , scannf = FALSE)
bdp$ratio ## 0.2148972
randtest(bdp) ## p = 0.001

## Within DPCoA (remove the effect of pH)
wdp <- wca(dp, fac = soilmicrob$env$pH, scannf = FALSE)
wdp$ratio ## 0.5684348

## Between Within-DPCoA (remove the effect of pH and focus on vegetation type)
wbdp <- bwca.dpcoa(dp, fac = soilmicrob$env$VegType, cofac =  soilmicrob$env$pH, scannf = FALSE)
wbdp$ratio ## 0.05452813
randtest(wbdp) ## p = 0.001

## End(Not run)

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