[R] predict.lm
Christos Hatzis
christos at silicoinsights.com
Tue May 2 21:24:47 CEST 2006
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.
-Christos
-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch
[mailto:r-help-bounces at 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)
> 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)
Thanks, Bill
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