Good Day All,
I am a newbie to SPLUS and had a question which I can't seem to figure
out reading the documentation or searching the web.
My goal is to create a generalized linear model using the negative
binomial distribution. I created that model with the full dataset (~500
sample points) and now that it is done, I wish to evaluate the
robustness of the model. Conceptually what I would like to do is see
how the coefficient estimates vary using the standard 80:20 rule. I
create the model with 80% of the data, I would then take the 20% of the
data I reserved to test the model, then I would repeat the procedure
1000 times or so.
Conceptually this is in essence a resampling technique and I thought I
would be using the bootstrapping and/or jackknife methods in SPLUS.
So two calls such as :
l.nb <-glm.nb(formula = TC ~ TE + C + SQC + CINC + SQC.IN +
offset(log(AP)), data = LD, na.action= na.omit, control =
glm.control(maxit = 500))
and then
l.boot1 <- bootstrap(data=LD, statistic=coef(eval(l.nb$cal)), B=200)
would in turn create my model and then resample my data 200 times (I set
it to 200 so it would not take to long). What I can't seem to figure
out is how to get the 80/20 split nor retaining the 20% for model
validation.
I have limited understanding of the SPLUS bootstrap/jackknife function
and the documentation leaves me puzzled so I can't nail down the syntax.
I know that this has been done (alot), am I on the right track here ?
Could someone point me to some resources in SPLUS documentation and/or
webpages that I could read up on before I ask more questions ?
Thanks in advance !
--
-Don
Don Catanzaro, PhD Landscape Ecologist
dgcatanzaro@gmail.com 16144 Sigmond Lane
479-751-3616 Lowell, AR 72745
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