Double principal coordinate analysis
Performs a double principal coordinate analysis
dpcoa(df, dis = NULL, scannf = TRUE, nf = 2, full = FALSE, tol = 1e-07, RaoDecomp = TRUE) ## S3 method for class 'dpcoa' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'dpcoa' print(x, ...) ## S3 method for class 'dpcoa' summary(object, ...)
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 |
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 |
xax |
the column number for the x-axis |
yax |
the column number for the y-axis |
... |
|
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
|
lw |
a numeric vector containing the weights of the samples (was
|
eig |
a numeric vector with all the eigenvalues |
RaoDiv |
a numeric vector containing diversities within samples |
RaoDis |
an object of class |
RaoDecodiv |
a data frame with the decomposition of the diversity |
dls |
a data frame with the coordinates of the elements (was
|
li |
a data frame with the coordinates of the samples (was
|
c1 |
a data frame with the scores of the principal axes of the elements |
Daniel Chessel
Sandrine Pavoine pavoine@mnhn.fr
Stéphane Dray stephane.dray@univ-lyon1.fr
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.
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)
}
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.