see inline below. On Wed, Jun 8, 2011 at 12:37 PM, Anupam <anupa...@gmail.com> wrote: > It is difficult for someone from a statistical frame of mind to understand > what this is about --- you need to think a bit differently. It is mostly a > simulation and decision analysis, with some use of statistical functions to > draw random samples to simulate the fact that outcome of interest can take > any value from a known or unknown distribution. For example, you may be > comparing two interventions and a do-nothing decision to improve some health > outcome of interest. The decision maker is interested in *relative* > effectiveness and costs of the interventions to improve the outcome of > interest. You have results from published literature that you can use as > inputs into a simulation exercise to compare relative costs and > benefits/effectiveness of the three options. A small decision tree can be > easily simulated in a spreadsheet; for long trees with many decision nodes > it is useful to have a specialized software. There are some Excel plugins > that are sold about $100. Others are more expensive. > > I think R is not well suited for this kind of work. A decision analysis
Not necessarily! A desicion tree model is a kind of graphical model. See the CRAN task view gR (graphical models in R) and maybe ask on the special interest mailing list R-sig-gR kjetil > package in R may require user to write code like the one used in LaTeX or > related programs (Metapost) to draw graphs of trees (e.g. complicated > organizational trees, or hierarchical trees). However, in such a package > there can be useful outputs, measures and graphs generated by R using code > that may already exist for other packages. > > Look up journal "Medical Decision Making" to know what is being discussed. > This method is used extensively in medicine and public health to study > decisions. It even uses MCMC, though with a different flavor --- it may even > be a different kind of food. > > Anupam. > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On > Behalf Of Jonathan Daily > Sent: Wednesday, June 08, 2011 7:47 PM > To: stefan.d...@gmail.com > Cc: r-help@r-project.org > Subject: Re: [R] Decision Trees /Decision Analysis with R? > > So TreeAge fits models but won't predict from them? That seems like bizarre > behavior. I suppose I would recommend, then, looking at the source code from > the aforementioned packages for how they store their split data. It sounds > like you would have to write code to hack TreeAge outputs into another > packages' format (e.g. look at ?rpart.object). > > Sorry I couldn't help more, > Jon > > On Wed, Jun 8, 2011 at 9:47 AM, stefan.d...@gmail.com > <stefan.d...@gmail.com> wrote: >> 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 <biomathjda...@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+Decisi >>> on+Trees >>> >>> On Wed, Jun 8, 2011 at 7:02 AM, stefan.d...@gmail.com >>> <stefan.d...@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.) >>>> >>>> ______________________________________________ >>>> R-help@r-project.org mailing list >>>> 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 >>> # It's great, trust me. >>> >> > > > > -- > =============================================== > Jon Daily > Technician > =============================================== > #!/usr/bin/env outside > # It's great, trust me. > > ______________________________________________ > R-help@r-project.org mailing list > 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. > > ______________________________________________ > R-help@r-project.org mailing list > 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. > ______________________________________________ R-help@r-project.org mailing list 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.
