Sigmund Freud wrote:
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.
I can't see what the problem is. Using the LifeCycleSavings
example from ?influence.measures:
lm.SR <- lm(sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings)
X <- model.matrix(lm.SR)
H <- X %*% solve(t(X) %*% X) %*% t(X)
hats1 <- diag(H)
hats2 <- hatvalues(lm.SR)
all.equal(hats1, hats2)
#[1] TRUE
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.
In a nutshell, hatvalues are a measure of how unusual
a point is in predictor space, i.e. to what extent it
"sticks out" in one or more of the X-dimensions.
-Peter Ehlers
Thanks,
Jean Yarrington
Independent consultant.
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