Re: [R] Least Squares Method

From: Joshua Wiley <jwiley.psych_at_gmail.com>
Date: Tue, 26 Apr 2011 14:56:24 -0700

Hi Vana,

I am not sure what package gls() is in off hand, but many model fitting functions have a subset argument. If not, supposing your data is in "dat", and the variable with the zeros in it that are concerning you is "X", then something like:

newdat <- dat[dat[, "X"] != 0, ]

and now fit gls() on "newdat" instead of "dat".

HTH, Josh

P.S. Throwing out data (even zeros) is typically not a good choice and leads to biased results. Before using this for any serious research, I would strongly recommend consulting with a local statistician or some other individual who can work with you to understand your data, where those zeros come from and what they might mean, come up with reasonable assumptions, and use modelling techniques that can include the information encoded in those zeros rather than simply throwing them out.

On Tue, Apr 26, 2011 at 1:43 PM, Vana <vana_at_auth.gr> wrote:
> Hi everyone,
> I am running the 'gls' command (least squares method) for a number of data
> out of which many are zeros. I strongly believe that the output is wrong and
> I think that this is due to the large number of zero values included in my
> dataset.
> I would like to ask if there is a command that would allow me to run the gls
> command disregarding all the zero values?
>
> Thank you in advance...
>
> --
> View this message in context: http://r.789695.n4.nabble.com/Least-Squares-Method-tp3476485p3476485.html
> Sent from the R help mailing list archive at Nabble.com.
>
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>

-- 
Joshua Wiley
Ph.D. Student, Health Psychology
University of California, Los Angeles
http://www.joshuawiley.com/

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