[R] Re: Load prediction
Johanus Dagius
jdagius at yahoo.com
Sun Jun 23 15:08:42 CEST 2002
Dr. Ripley,
I am not comparing R and Cubist on the same level. The
reason I am interested in R is because it is, as you
say, extensible and very flexible. (But much more
difficult to master). I hope it allow me to build
models (like Cubist), but also visualize and analyze
the results (which Cubist is not designed to do).
I have already used libsvm and know about neural nets,
but what is this "VR bundle"? Is it a CRAN package?
Thank you,
Johanus Dagius
--- ripley at stats.ox.ac.uk wrote:
> On Sat, 22 Jun 2002, Johanus Dagius wrote:
>
> > Hello,
> >
> > I have received no reply to my previous query, so
> I
> > will try again.
> >
> > I have tried glm on this problem with the default
> > parameters and it produced a model with mean
> absolute
> > error of approx 300 MWhrs. (The data is roughly
> > normally distributed with a mean of 1700 MWhrs and
> > SD=500). I know very little about R and so I am
> not
> > sure what parameter needs to be tweaked from here.
> >
> > Using Cubist (www.rulequest.com) I have created a
> > predictive model whose mean error is around 100
> MWhrs.
> > Cubist builds a recursively partitioned tree using
> > piecewise linear regression. Cubist also outputs a
> > nice set of rules which explain the model in terms
> of
> > feature splits.
> >
> > I think R should give a comparable result. Does R
> have
> > a method of piecewise approximation like this? I
> would
> > like to compare R against Cubist. What method(s)in
> R
> > must I learn to do this?
>
> R is an extensible software system, not a set of
> model-building
> techniques. You really didn't tell us anything like
> enough (either time)
> about your data. (E.g. Cubist is designed for
> thousands of records and
> tens to hundreds of variables: you showed five and
> around seven.) But as
> a general principle, this looks as if glm (as
> distinct from lm) is not
> needed, and the currently most promising prediction
> techniques for
> continuous quantities are thought to be neural
> networks (in the VR bundle)
> and SVMs (in package e1071). R also has several
> packages for tree-building
> (see the FAQ), and you could implement something
> very like Cubist in R.
> So `to compare R against Cubist' is not
> well-defined, both for `R' and for
> the criteria to be used.
>
> My advice would be to engage a statistical
> consultant to guide you.
>
>
> > At 12:13 PM 6/21/02 -0700, I wrote:
> > > Hello,
> > >
> > >This is perhaps more of a regression question
> than R,
> > >but I am learning both, so would appreciate your
> > >wisdom here.
> > >
> > >
> > >I have some data which reflects power load for an
> > >electrical generating system, with some temporal
> > >features. The data fields look like this:
> > >
> > >
> > >ID,MON,DAY,YR,HR,WDAY,DRYBULB,WETBULB,LOAD
> > >4455 5 13 92 13 4 70 63 1617
> > >4456 3 9 92 13 2 73 57 1397
> > >4457 10 5 92 8 2 58 58 1501
> > >4458 11 24 92 18 3 56 56 1885
> > >4459 9 27 92 8 1 65 65 1402
> > >
> > >
> > >What R methodology is likely to produce the most
> > >accurate load forecast prediction for a given
> date
> > and
> > >temperatures for problems like this?
> > >
> > >
> > >Thank you,
> > >Johanus Dagius
> >
> >
> > __________________________________________________
> >
> >
> >
> >
>
-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-
> > r-help mailing list -- Read
> http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html
> > Send "info", "help", or "[un]subscribe"
> > (in the "body", not the subject !) To:
> r-help-request at stat.math.ethz.ch
> >
>
_._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
> >
>
> --
> Brian D. Ripley,
> ripley at stats.ox.ac.uk
> Professor of Applied Statistics,
> http://www.stats.ox.ac.uk/~ripley/
> University of Oxford, Tel: +44 1865
> 272861 (self)
> 1 South Parks Road, +44 1865
> 272860 (secr)
> Oxford OX1 3TG, UK Fax: +44 1865
> 272595
>
__________________________________________________
-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-
r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html
Send "info", "help", or "[un]subscribe"
(in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch
_._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
More information about the R-help
mailing list