ade4 (version 1.7-19)

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

Value

Objects of class betdpcoa, witdpcoa or betwit

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

Author

Stéphane Dray

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

See Also

dpcoa

Examples

if (FALSE) {

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

con <- url("https://pbil.univ-lyon1.fr/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
}