help with R

Berton,

Firstly, thanks for your comments.

To address the what you first said, plotting the 3690-element vector is what
is causing R to hang.

 Rather than lose everything I've entered by hand each
interactive run I've switched to using a batch script, which I can now load
and run at prompt.  Using sink("filename.txt") I'm able to save the output
to study.

My usage of dummy variables is to identify seasonality on the daily level
over
15 years with 246 days per year.  I need to identify the day each month
when an (expected) event is occuring.  The date the event occurs does
not occur on necessarily the same day each month.  I don't know of
another method that could identify these statistically significant seasonal
events using R.  Dummy variables with a LM is the only method I have
experience with using R.  If you or anyone has suggestions on what other
methods to use I would appreciate some suggestions.  Using 245 dummy
variables is quite awkward.

I see lag() can be used to build a first- or multi-order differenced time
series to extract any underlying trend in a time series.

Using STL() might be promising.  It appears to be similar to other methods
I've used with MINITAB but called something different.

Nor an ARIMA nor a BSM is really what I need as I'm not focused on
performing predictions or modeling of the (possibly non-normal) properties
of the residuals.

Thanks.  All your advice is greatly appreciated.

Ed

      "A man is not old until regrets take the place of dreams."
                    Actor John Barrymore






From: Berton Gunter <gunter.berton at gene.com>
To: "'Ed Wang'" <eymw at hotmail.com>, <r-help at stat.math.ethz.ch>
Subject: RE: [R] help with R
Date: Tue, 29 Nov 2005 11:05:02 -0800

You're not telling us something or there's a problem with your R build: a
3960 element vectors of integer is tiny and will not cause R to crash.

Regarding your regression model. You do **not** need dummy variables in R.
Please read the docs (e.g. AN INTRODUCTION TO R) and help files on lm() and
factor() to see how to do linear modeling in R. lag() and diff() may also be
relevant. OTOH, R has many better ways to model time series and seasonality,
both in base R and numerous add-on packages. Try help.search('time series')
and RSiteSearch('time series')

-- Bert Gunter
Genentech Non-Clinical Statistics
South San Francisco, CA

"The business of the statistician is to catalyze the scientific learning
process."  - George E. P. Box