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, ...)

Arguments

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

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

Examples

Run this code

# 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
# }