On Thu, 2010-08-19 at 13:42 -0700, Kay Cichini wrote:> hello everyone,
>
> i sampled 100 stands at 20 restoration sites and presence of 3 different
> invasive plant species.
> i came across logistic regression trees and wonder if this is suited for my
> purpose - predicting presence of these problematic invasive plant species
> (one by one) by a set of recorded ecological / geographical parameters.
> i'd be glad if someone would comment on applying this mehtod to such
data -
> maybe someone could point me useful references.
> also, i was not able to find out if there is a package implementing
logistic
> regression?
Not sure what a logistic regression tree is, but a classification tree
would be useful here: Treat each species as present (== 1) or absent (=0) and
try to fit a tree consisting of a set of splits in X covariates
that minimise a suitable deviance criterion.
If you want to fit all three species at once, try multivariate trees,
but IIRC, they (in package mvpart at least) expect a count-based data
set, i.e. the deviance criterion they used (sum of squares) is probably
not suited to binary type data.
The one problem I foresee is that you only have 100 data points and even
that number is pseudo replicated as you have multiple samples from just
20 "sites". Trees are unstable at the best of times and work best when
given a lot of data. Boosting, bagging and randomForests can help but
they again work best/well with large data sets. I suppose large will be
relative to the signal to noise ratio in your data.
Ecologically, one needs to consider what a 0 value means (an absence):
was the invasive not present due to the environment being bad or just
because it hasn't got there yet despite environment being good? How you
deal with that is anybody's guess.
Try the R-SIG-Ecology list for further help.
G
>
> thanks in advance,
> kay
>
> -----
> ------------------------
> Kay Cichini
> Postgraduate student
> Institute of Botany
> Univ. of Innsbruck
> ------------------------
>
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Dr. Gavin Simpson [t] +44 (0)20 7679 0522
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