I was searching for a way to compute robust R-square in R in order to get an
information similar to the "Proportion of variation in response(s) explained
by model(s)" computed by S-Plus. This post is dealing with that. Would be
possible to have some hints on how to calculate this parameter within R?
Thank you very much in advance.
Laura Poggio
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Date: Mon, 20 Oct 2008 06:15:49 +0100 (BST)
From: Prof Brian Ripley <[EMAIL PROTECTED]>
Subject: Re: [R] R-square in robust regression
To: PARKERSO <[EMAIL PROTECTED]>
Cc: r-help@r-project.org
Message-ID:
<[EMAIL PROTECTED]>
Content-Type: TEXT/PLAIN; charset=US-ASCII; format=flowed
On Sun, 19 Oct 2008, PARKERSO wrote:
>
> Hi there,
> I have just started using the MASS package in R to run M-estimator robust
> regressions. The final output appears to only give coefficients, degrees
of
> freedom and t-stats. Does anyone know why R doesn't compute R or R-squared
These as only valid for least-squares fits -- they will include the
possible outliers in the measure of fit.
And BTW, it is not 'R', but the uncredited author of the package who made
such design decisions.
> and why doesn't give you any other indices of goodness of fit?
Which ones did you have in mind? It does give a scale estimate of the
residuals, and this determines the predition accuracy.
> Does anyone know how to compute these in R?
Yes.
> Sophie
--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics,
http://www.stats.ox.ac.uk/~ripley/<http://www.stats.ox.ac.uk/%7Eripley/>
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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