Not a direct answer but you may find lm.fit worth experimenting with.
Also try the high-performance computing task view on CRAN
Cheers,
Andrew
--
Andrew Robinson
Chief Executive Officer, CEBRA and Professor of Biosecurity,
School/s of BioSciences and Mathematics & Statistics
University of Melbo
Hi John,
the negative binomial is a tricky one - there are several different
parameterisations and therefore different interpretations of the parameters.
Joseph Hilbe wrote a whole book on it that might be wroth checking.
Cheers,
Andrew
--
Andrew Robinson
Chief Executive Officer, CEBRA and
Hi Bob,
there may be more efficient ways to go about it but I would use R to scrape the
contents of
http://home.brisnet.org.au/~bgreen/Data/Hanson1/
http://home.brisnet.org.au/~bgreen/Data/Hanson2/
in order to form the URLs of the files, and then loop over the URLs.
Cheers,
Andrew
--
Andrew
Try something like
with(df, predict(smooth.spline(x = altitude, y = atm_values), deriv = 1))
Cheers,
Andrew
--
Andrew Robinson
Chief Executive Officer, CEBRA and Professor of Biosecurity,
School/s of BioSciences and Mathematics & Statistics
University of Melbourne, VIC 3010 Australia
Tel: (+61)
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