Dear all,
I was looking for methods in R that allow assessing the number of significant principal coordinates. Unfortunatly I was not very successful. I expanded my search to the web and Current Contents, however, the information I found is very limited.
Therefore, I tried to write code for doing a randomization. I would highly appriciate if somebody could comment on the following approach. I am neither a statistician, nor an R expert... the data matrix I used has 72 species (columns) and 167 samples (rows).
Many thanks in advance, Christian
# focus on ~80% of all the eigenvalues
nEigen <- round(ncol(Data*0.8))
# Calculate Weights for Principal Coordinates Analysis
Total <- apply(Data,1,sum) Weight <- round(Total/max(Total)*1000)
# Calculate Chord Distance
library(vegan) Chord <- vegdist(decostand(Data, "norm"), "euclidean")
# Calculate Principal Coordinates, including distance matrix row weights
library(ade4) PCoord.Eigen <- dudi.pco(Chord,row.w=Weight,scann=F,full=T)$eig[1:nEigen]
# Randomization of Principal Coordinates Analysis
library(labdsv)
for (i in 1:99) {
Data.random <- rndtaxa(Data,species=T,plots=T)
Total.random <- apply(Data.random,1,sum)
Weight.random <- round(Total.random/max(Total.random)*1000)
Chord.random <- vegdist(decostand(Data.random, "norm"), "euclidean")
PCoord.Eigen.random <- dudi.pco(Chord.random,row.w=Weight.random,scann=F,full=T)$eig[1:nEigen]
PCoord.Eigen <- cbind.data.frame(PCoord.Eigen, PCoord.Eigen.random)
}
# Plot scree diagramm with original eigenvalues and 95%-quantiles of eigenvalues from randomized principal coordinate analysis
plot(c(1:nEigen),PCoord.Eigen[,1],type="b") lines(c(1:nEigen),apply(PCoord.Eigen[,-1],1,quantile,probs=c(0.95)),col="red")
Christian Kamenik Institute of Plant Sciences University of Bern
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