From: Prof Brian Ripley <ripley_at_stats.ox.ac.uk>

Date: Wed 03 May 2006 - 05:54:08 EST

Date: Wed 03 May 2006 - 05:54:08 EST

On Tue, 2 May 2006, Christos Hatzis wrote:

*> I think you got it right.
**>
**> The mean of the (weighted) sum of a set of random variables is the
**> (weighted) sum of the means and its variance is the (weighted) sum of the
**> individual variances (using squared weights). Here you don't have to worry
**> about weights.
**>
**> So what you proposed does exactly this.
*

Yes, but the theory has assumptions which are not met here: the random variables are correlated (in almost all case).

> -Christos

*>
**> -----Original Message-----
**> From: r-help-bounces@stat.math.ethz.ch
**> [mailto:r-help-bounces@stat.math.ethz.ch] On Behalf Of Bill Szkotnicki
**> Sent: Tuesday, May 02, 2006 2:59 PM
**> To: 'R-Help help'
**> Subject: [R] predict.lm
**>
**> I have a model with a few correlated explanatory variables.
**> i.e.
**>> m1=lm(y~x1+x2+x3+x4,protdata)
**> and I have used predict as follows:
**>
**>> x=data.frame(x=1:36)
**>> yp=predict(m1,x,se.fit=T)
*

How can this work? You fitted the model to x1...x4 and supplied x.

>> tprot=sum(yp$fit) # add up the predictions tprot

*>
**> tprot is the sum of the 36 predicted values and I would like the se of that
**> prediction.
**> I think
**>> sqrt(sum(yp$se.fit^2))
**> is not correct.
**>
**> Would anyone know the correct approach?
**> i.e. How to get the se of a function of predicted values (in this case sum)
*

You need to go back to the theory: it is easy to do for a linear function, otherwise you will need to linearize.

-- Brian D. Ripley, ripley@stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.htmlReceived on Wed May 03 06:04:21 2006

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