Not sure what you mean.

yi <- c(2,3,2,4,3,6)
xi <- c(1,4,3,2,4,5)

res <- lm(yi ~ xi)
hatvalues(res)

X <- cbind(1, xi)
diag( X%*%solve(t(X)%*%X)%*%t(X) )

Same result.

Best,

--
Wolfgang Viechtbauer                        http://www.wvbauer.com/
Department of Methodology and Statistics    Tel: +31 (0)43 388-2277
School for Public Health and Primary Care   Office Location:
Maastricht University, P.O. Box 616         Room B2.01 (second floor)
6200 MD Maastricht, The Netherlands         Debyeplein 1 (Randwyck)
________________________________________
From: r-help-boun...@r-project.org [r-help-boun...@r-project.org] On Behalf Of 
Sigmund Freud [ss_freud...@yahoo.com]
Sent: Sunday, November 08, 2009 8:14 AM
To: r-help@r-project.org
Subject: [R]  influence.measures(stats): hatvalues(model, ...)

Hello:

I am trying to understand the method 'hatvalues(...)', which returns something 
similar to the diagonals of the plain vanilla hat matrix [X(X'X)^(-1)X'], but 
not quite.

A Fortran programmer I am not, but tracing through the code it looks like 
perhaps some sort of correction based on the notion of 'leave-one-out' variance 
is being applied.

Whatever the difference, in simulations 'hatvalues' appears to perform much 
better in the context of identifying outiers using Cook's Distance than the 
diagonals of the plain vanilla hat matrix. (As in 
http://en.wikipedia.org/wiki/Cook's_distance).

Would prefer to understand a little more when using this method. I have 
downloaded the freely available references cited in the help and am in the 
process of digesting them. If someone with knowledge could offer a pointer on 
the most efficient way to get at why 'hatvalues' does what it does, that would 
be great.

Thanks,
Jean Yarrington
Independent consultant.
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