lim=c(-20, 20))
arrows(0, 0, variables[ , 1], variables[ , 2], len=0.1, col="red")
text(2*variables, rownames(variables), col="red", xpd=TRUE)
axis(3); axis(4)
On 25 Feb 2009 at 9:52, Axel Strauß wrote:
Date sent: Wed, 25 Feb 2009 09:52:54 +01
Prof Brian Ripley schrieb:
On Tue, 24 Feb 2009, Axel Strauß wrote:
OK, the one thing I figured out:
Is should be like:
biplot(test.pca, cex=c(2,1), col=c("red","green")...
to change size, colours etc separately. But I still don't know how
change lables of observa
OK, the one thing I figured out:
Is should be like:
biplot(test.pca, cex=c(2,1), col=c("red","green")...
to change size, colours etc separately. But I still don't know how
change lables of observations to symbols properly.
Tipps? Thanks again,
Axel
Dear R helpers,
When producing a PCA biplot,
Dear R helpers,
When producing a PCA biplot, vectors of environmental variables (as red
arrows with labels) and scores of the observations (black labels
(observation names)) are plotted by default. How can I change the
graphical output? Let's say I would like that the scores are plottet
only
look at confirmatory factor analysis models instead, estimable in
R with John Fox' sem package.
On 1/19/09, Axel Strauß wrote:
G'Day R users!
Following an ordination using prcomp, I'd like to test which variables
singnificantly contribute to a principal component. T
Axel Strauß schrieb:
G'Day R users!
Following an ordination using prcomp,
Sorry, correction. I mean "using princomp".
I'd like to test which variables singnificantly contribute to a
principal component. There is a method suggested by Peres-Neto and al.
2003. Ecology
G'Day R users!
Following an ordination using prcomp, I'd like to test which variables
singnificantly contribute to a principal component. There is a method
suggested by Peres-Neto and al. 2003. Ecology 84:2347-2363 called
"bootstrapped eigenvector". It was asked for that in this forum in
Jan
7 matches
Mail list logo
