ade4 (version 1.2-2)

dpcoa: Double principal coordinate analysis

Description

Performs a double principal coordinate analysis

Usage

dpcoa (df, dis = NULL, scannf = TRUE, nf = 2, full = FALSE, tol = 1e-07)
plot.dpcoa (x, xax = 1, yax = 2, option = 1:4, csize = 2, ...)
print.dpcoa (x, ...)

Arguments

df

a data frame with elements as rows, samples as columns and abundance or presence-absence as entries

dis

an object of class dist containing the distances between the elements.

scannf

a logical value indicating whether the eigenvalues bar plot should be displayed

nf

if scannf is FALSE, an integer indicating the number of kept axes

full

a logical value indicating whether all non null eigenvalues should be kept

tol

a tolerance threshold for null eigenvalues (a value less than tol times the first one is considered as null)

x

an object of class dpcoa

xax

the column number for the x-axis

yax

the column number for the y-axis

option

the function plot.dpcoa produces four graphs, option allows us to choose only some of them

csize

a size coefficient for symbols

...

... further arguments passed to or from other methods

Value

• Returns a list of class dpcoa containing:
• call
  call
• nf
  a numeric value indicating the number of kept axes
• w1
  a numeric vector containing the weights of the elements
• w2
  a numeric vector containing the weights of the samples
• eig
  a numeric vector with all the eigenvalues
• RaoDiv
  a numeric vector containing diversities within samples
• RaoDis
  an object of class dist containing the dissimilarities between samples
• RaoDecodiv
  a data frame with the decomposition of the diversity
• l1
  a data frame with the coordinates of the elements
• l2
  a data frame with the coordinates of the samples
• c1
  a data frame with the scores of the principal axes of the elements

References

Pavoine, S., Dufour, A.B. and Chessel, D. (in press) From dissimilarities among species to dissimilarities among communities: a double principal coordinate analysis. Journal of Theoretical Biology.

Examples

data(humDNAm)
dpcoahum <- dpcoa(humDNAm$samples, sqrt(humDNAm$distances), scan = FALSE, nf = 2)
dpcoahum
plot(dpcoahum, csize = 1.5)
data(ecomor)
ecomor.phylog <- taxo2phylog(ecomor$taxo)
dpcoaeco <- dpcoa(ecomor$habitat, ecomor.phylog$Wdist, scan = FALSE, nf = 2)
dpcoaeco
plot(dpcoaeco, csize = 1.5)