[R] Re: Load prediction

[Johanus Dagius]

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
> >
> >
> > __________________________________________________
> >
> >
> >
> >
>
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> >
> 
> -- 
> 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
> 


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