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~ Much better. ~ If m is your linear regression model, ~ * boxcox(m) in the MASS package will look for a power (more or less) transformation to normalize the residuals -- see the book for more information ~ * plot(m) will produce plots including a Q-Q plot (testing normality) of the residuals ~ * don't forget to check for autocorrelation in the residuals (acf(residuals(m))) ~ Ben Bolker Jenny Barnes wrote: | Hi Ben and R-help communtiy, | | More specifics: | | I am using sea-surface temperature (averaged over an area) and also | winds (averaged over an area) to use in a linear regression model as | predictors for rainfall over a small region of Africa. So I have 1 time | series of sea-temp and one timeseries of rainfall (over 36 years - | seasonal average) and I have performed the linear regression between the | 2. I now want to check if the residuals are normally distributed. If | they are not I want an R function that will tell me what distribution | they are most similar to - so that I can apply a suitable transformation | to make the data normal..... | | Any more tips now that you have a few more details perhaps? :o) | | Thanks for your time, | | Jenny | | On Mon, 30 Jun 2008, Ben Bolker wrote: | |> Jenny Barnes <jmb <at> mssl.ucl.ac.uk> writes: |> |>> |>> Dear R-help community, |>> |>> Does anybody know of a stats function in R that tells you which |>> distribution best fits your data? I have tried look through the archives |>> but have only found functions that tell you if it's normal or log etc. |>> specifically - I am looking for a function that tells you (given a |>> timeseries) what the distribution is. |>> |>> Any help/advice will be greatly appreciated, |>> |>> All the best, |>> |>> Jenny Barnes |>> |>> jmb <at> mssl.ucl.ac.uk |> |> The problem is that it's not generally a good |> idea to data-dredge in this way. Your best bet is |> to think about the characteristics of the |> data (discrete or continuous, non-negative or real, |> symmetric or skewed) and try to narrow it down to |> a few distributions -- then you can use fitdistr() |> (from the MASS package) or something similar |> to compare among them. |> |> If you say a little bit more about what |> you're trying to do with the data you might |> get some more specific advice. |> |> Ben Bolker |> |> ______________________________________________ |> R-help@r-project.org mailing list |> https://stat.ethz.ch/mailman/listinfo/r-help |> PLEASE do read the posting guide |> http://www.R-project.org/posting-guide.html |> and provide commented, minimal, self-contained, reproducible code. |> -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.4.6 (GNU/Linux) Comment: Using GnuPG with Mozilla - http://enigmail.mozdev.org iD8DBQFIaM/Bc5UpGjwzenMRAitOAJ4qa01aXSjVyBupzBUuf0x8o/47iwCeKuno VElg6gIT01qCPvWmELvm63Y= =7cue -----END PGP SIGNATURE----- ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
