Between- and within-class double principal coordinate analysis

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

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

Author

Stéphane Dray stephane.dray@univ-lyon1.fr

Examples

if (FALSE) { # \dontrun{

## 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
} # }