bwca.dpcoa {ade4} | R Documentation |
These functions allow to study the variations in diversity among communities (as in dpcoa) taking into account a partition in classes
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, ...)
x |
an object of class |
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 |
Objects of class betdpcoa
, witdpcoa
or betwit
Stephane Dray stephane.dray@univ-lyon1.fr
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, in press.
## Not run: library(adegraphics) ## 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)