Hi Dominique,
please, if you have some questions about a particular package, do not send
to R-Help. There is a list about ade4, see details at:
http://pbil.univ-lyon1.fr/ADE-4/adelist.html
cca is a correspondence analysis with constraint imposed by external
information. Redundancy analysis (available in ade4 by the function pcaiv)
is PCA with constraint.
Here is an example using pcaiv:
data(rhone)
pca1 <- dudi.pca(rhone$tab, scan = FALSE, nf = 3)
iv1 <- pcaiv(pca1, rhone$disch, scan = FALSE)
iv1$param
iner inercum inerC inercumC ratio R2 lambda
6.27 6.27 5.52 5.52 0.879 0.671 3.7
4.14 10.4 4.74 10.3 0.984 0.747 3.54
iner represent eigenvalues of the unconstrained analysis:
> pca1$eig[1:2]
[1] 6.274265 4.140947
inercum is equivalent to
cumsum(pca1$eig)
prediction of sites scores by environmental variables produce a new set
score (often named LC scores), inerC represent variance of this scores:
diag(t(iv1$ls)%*%diag(iv1$lw)%*%as.matrix(iv1$ls))
Axis1 Axis2
5.515241 4.736362
inercumC is the cumsum.
ratio is simply inercum/inerCcum
Finally note that CCA or RDA find a site score that maximizes the variance
explained by environmental variables. This is a product of variance by a R2:
0.671*5.52 # lambda = R2*inerC
[1] 3.70392
At 10:11 14/01/2005, Dominique Gr?ter wrote:>Does someone know the meaning of the following values relating to the
>"cca"-command?
>
> iner, inercum, inerC, inercumC
>
>Thanks!
>DOMI
>
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St?phane DRAY
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