Hi Greg
 
Thanks for responding. 
 
When I scale stackloss and compute the fitted values of the scaled data frame, the values are not the same:
 
A<-data.frame(scale(stackloss))

lm<- lm(stack.loss ~ . -1, qr=T, data=A)
summary(lm)
fitted.values(lm)
 
Or should I not be comparing the two?

>>> Greg Snow <Greg.Snow@imail.org> 2009/02/19 07:31 PM >>>
You are computing the hat matrix to predict stack.loss, but stack.loss is a column in the A matrix, so you predictions are all perfect (given stack.loss, what is stack.loss, fairly simple answer, all errors are 0).  I think you want to redo this using only the 3 columns other than stack.loss in the call to svd, then you should get the results that you are expecting.

Hope this helps,

-- 
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow@imail.org 
801.408.8111


> -----Original Message-----
> From: r-help-bounces@r-project.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of Kutlwano Ramaboa
> Sent: Wednesday, February 18, 2009 10:43 PM
> To: r-help@r-project.org 
> Subject: [R] matrix computation???
> 
> Hello
> 
> Can anyone tell me what I am doing wrong below? My Y and y_hat are the
> same.
> 
> 
> 
> A<-scale(stackloss)
> n1<- dim(A)[1];n2<-dim(A)[2]
> X<-svd(A)
> Y<- matrix(A[,"stack.loss"],nrow=n1)
> Y
> y_hat <-matrix((X$u%*% t(X$u))%*%Y,nrow=n1,byrow=T)
> y_hat
> 
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> 
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> PLEASE do read the posting guide http://www.R-project.org/posting- 
> guide.html
> and provide commented, minimal, self-contained, reproducible code.

______________________________________________
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and provide commented, minimal, self-contained, reproducible code.

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