I haven't seen any replies, so I will offer a few thoughts:
If this were my problem, I'd start using lm with terms that predict
the tide in terms of the positions of the sun and moon. If other things
were thought to be important, I'd include them also in the lm model.
However, I would not believe the answer from lm until the
autocorrelation function (acf) of the residuals was plausibly white
noise. If for some reason I couldn't get there, I'd use arima with
its xreg argument to describe the plausible deterministic effects plus
some ARMA model identified in the residuals. For documentation, I
suggest you start with Venables and Ripley (2002) Modern Applied
Statistics with S, 4th ed. (Springer), especially ch. 14. There may be
better books on R available today, but among the books I've used, this
is the premier general reference on R, and ch. 14 on time series should
help you get started. I'm not familiar with hoa and Rwave. They
may ultimately be the best tools for your need, but I don't know that.
If it were my project, I would likely try lm and arima as discussed
in Venables and Ripley first. Then I'd look at hoa, Rwave and
anything else that might look promising.
Should you desire other help from this listserve, I suggest you
PLEASE do read the posting guide!
www.R-project.org/posting-guide.html. Anecdotal evidence suggests
that people who follow more closely that guide tend to get more useful
replies quicker.
hope this helps.
spencer graves
William H. Asquith wrote:
I am looking at using R to analyze time series data containing a tidal
component. I need to remove the tidal signal to extract the time
series of the phenomena I seek to study. A browse of R-project search
engines has not been too fruitful? I've found 'hoa' and 'Rwave', but
need further help getting started. THANKS.
-wa
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