ade4 (version 1.7-18)

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,
RaoDecomp = TRUE)
# S3 method for dpcoa
plot(x, xax = 1, yax = 2, …)
# S3 method for dpcoa
print (x, …)
# S3 method for dpcoa
summary (object, …)

Arguments

df

a data frame with samples as rows and categories (i.e. species) as columns and abundance or presence-absence as entries. Previous releases of ade4 (<=1.6-2) considered the transposed matrix as argument.

dis

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

scannf

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

RaoDecomp

a logical value indicating whether Rao diversity decomposition should be performed

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, object

an object of class dpcoa

xax

the column number for the x-axis

yax

the column number for the y-axis

…

… 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

dw

a numeric vector containing the weights of the elements (was w1 in previous releases of ade4)

lw

a numeric vector containing the weights of the samples (was w2 in previous releases of ade4)

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

dls

a data frame with the coordinates of the elements (was l1 in previous releases of ade4)

li

a data frame with the coordinates of the samples (was l2 in previous releases of ade4)

c1

a data frame with the scores of the principal axes of the elements

References

Pavoine, S., Dufour, A.B. and Chessel, D. (2004) From dissimilarities among species to dissimilarities among communities: a double principal coordinate analysis. Journal of Theoretical Biology, 228, 523--537.

Examples

# NOT RUN {
data(humDNAm)
dpcoahum <- dpcoa(data.frame(t(humDNAm$samples)), sqrt(humDNAm$distances), scan = FALSE, nf = 2)
dpcoahum
if(adegraphicsLoaded()) {
  g1 <- plot(dpcoahum)
} else {
  plot(dpcoahum)
}
  
# }
# NOT RUN {
data(ecomor)
dtaxo <- dist.taxo(ecomor$taxo)
dpcoaeco <- dpcoa(data.frame(t(ecomor$habitat)), dtaxo, scan = FALSE, nf = 2)
dpcoaeco

if(adegraphicsLoaded()) {
  g1 <- plot(dpcoaeco)
} else {
  plot(dpcoaeco)
}
# }