Dear Alexandre,
If your variables have only positive values, there will be strong
correlation between linear, quadratic and cubic terms that leads to high
VIF. You can avoid it by centering the variables before calculating
quadratic and cubic terms:
quadratic<-(x-mean(x))^2
cubic<-(x-mean(x)
Thanks Jari, I understand
Before going trough the code of rda I would prefer to see if I can do this
using RsquareAdj
When you say "The default method can be called as RsquareAdj(x, n, m), and in
the default method x is the unadjusted correlation...etc.."
my problem is to extract the global unad
Paolo,
See ?RsquareAdj for the call interface. The default method can be called as
RsquareAdj(x, n, m), and in the default method x is the unadjusted correlation,
n is the number of observations and m is the number of parameters (degrees of
freedom) in the fitted model. Specific methods for uni
Thankyou very much Jari!
I think that it is nearly ok
what I would like to have is the same as in
RsquareAdj(vegan::rda(yy,xx))
that is a GLOBAL measure of the association
BUT...I want it for a multiple-multivariate lm model that does not include the
intercept;
an alternative could be to build a
Dear colleagues,
I am working on a Linear Mixed Model with nested structure, based on the
book of Zuur et al. 2009. Mixed Effects Models and Extensions in Ecology with
R, and have a question to ask to those of you more experienced with this model
family.
I am using several topographic va
Specific reason is that nobody has implemented this. These things don't come by
automatic writing, but somebody must do them.
What would you expect to get? Is this what was on your mind:
> sapply(summary(lm(yy~xx-1)), function(x) c("r.squared" = x$r.squared,
> "adj.r.squared" = x$adj.r.squared)
Dear list,
I would like to easily compute the adjusted R-square in a lm model without
intercept (excluding the intercept is essential for my analysis)
I found that RsquareAdj() in vegan returns NA if the argument is a
multiple-multivariate lm model thus including multivariate responses and
mul
