[R] Decision Trees /Decision Analysis with R?

stefan.duke at gmail.com stefan.duke at gmail.com
Wed Jun 8 15:47:11 CEST 2011


Thank you so much for reply. But I am looking for the exact opposite.

I do not have a data set which I want to partition. But already a
sequence/tree-like set of decision rules and with which I want to
simulate what is my expected outcome/pay-off given a particular
scenario.
As far as I understand it, those packages could calculate the expected
outcome AFTER having fit them to a particular data set and not
construct a "synthetic" tree with exogenously defined decision
nods/rules. Or am I wrong?


Thanks and best,
Stefan



On Wed, Jun 8, 2011 at 2:03 PM, Jonathan Daily <biomathjdaily at gmail.com> wrote:
> See packages rpart, randomForest, party.
>
> Also, typing "R Decision Trees" produced good google results.
>
> http://www.google.com/search?aq=f&sourceid=chrome&ie=UTF-8&q=R+Decision+Trees
>
> On Wed, Jun 8, 2011 at 7:02 AM, stefan.duke at gmail.com
> <stefan.duke at gmail.com> wrote:
>> Hello,
>>
>> this question is a bit out of the blue.
>>
>> I am a big R fan and user and in my new job I do some decision
>> modeling (mostly health economics). For that decision trees are often
>> used (I guess the most classic example is the investment decision A,
>> B, and C with different probabilities, what is the expected payoff).
>> We use a specialized software called TreeAge that some might know.
>> The basic setup of such simulations is actually very simple and I
>> guess useful in many fields. So I was wondering whether there is
>> already a package out there in R that is doing such a thing?
>>
>> Thanks for any hints!
>> Best,
>> Stefan
>>
>> PS
>> (By decision tree I don't mean cluster-like analysis of a data set
>> splitting by identifying decision nods, but the other way around: I
>> have decision nodes, what is my expected outcome.)
>>
>> ______________________________________________
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>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
>
>
> --
> ===============================================
> Jon Daily
> Technician
> ===============================================
> #!/usr/bin/env outside
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>



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