Hi :D
I finally had the chance to try your solutions. It was fast to modify
the code of the function and it worked!
I really appreciate your comments and your help. You are fast and clear !
I modified my code and I will never put white axes ever again ;)
Thank you very much.
2013/2/26 Jari Oksanen
Two additional issues might be considered:
1. Correlated variables are still correlated after PCA or after tossing one of
the variables so teasing apart separate effects of the two variables is not
resolved (nor can it necessarily be resolved with the particular dataset at
hand).
2. The purp
There are a few places left on the following course: Beginner's Guide
to MCMC, GLM and GAM with R
When: 10 - 13 June 2013
Where: SAMS, Oban, Scotland
Further information: http://www.highstat.com/statscourse.htm
Flyer: http://www.highstat.com/Courses/Flyer2013June_SAMS.pdf
Kind regards,
With reference to jims point 2. One can use Partial Least Squares,
which finds orthogonal PC's that best explain a set of responses.
Chris Howden
Founding Partner
Tricky Solutions
Tricky Solutions 4 Tricky Problems
Evidence Based Strategic Development, IP Commercialisation and
Innovation, Data Ana
Li,
You can use the "predict" function in the 'raster' package. There are also
examples with randomForest and other techniques in this vignette that comes
with the 'dismo' package:
http://cran.r-project.org/web/packages/dismo/vignettes/sdm.pdf
Robert
> Li Wen-2 wrote
> > Dear list member
> >
>
Hi, Robert and all others
Thanks for the suggestions and directions pointed out from the community. It
seems there are two packages for the work:
1. Using predict function from package "raster". The function can project a
fitted model of any class that has a 'predict' method (or supplying a s
Hello,
I'm posting to this list because I believe it's the best place to
go. My question is R related only inasmuch as all the work I've
done so far has been with R and I expect any answers I get from
here will lead me to more R work.
I'm consulting with an ecologist and an engineer on a project
Dear list members,
I want to plot an nMDS diagram with points' area proportional to the
abundance of particular species. I could imagine just plotting with type =
"n" and then using points() with different cex, but may be some special
functions/packages exist for that which you can point me to?
T
