Dear Kelly and Jim,
On 2022-03-20 9:40 p.m., Jim Lemon wrote:
Hi Kelly,
Perhaps the best place to look is the "car" package. There is a
somewhat confusing reference in the "cookd" function help page to the
"cooks.distance" function in the "base" package that doesn't seem to
be there. Whether this is the case or not, I think you can still use
the "cookd" alias.
cookd() in the car package has been defunct for some time.
To address the original question: One can compute Cook's distances for
*any* regression model by brute-force, omitting each case i in turn and
computing the Wald F or chisquare test statistic for the "hypothesis"
that the deleted estimate of the regression coefficients b_{-i} is equal
to the estimate b for all of the data. In a linear model, D can be
computed much more efficiently based on the hatvalues, etc., without
having to refit the model n times, but that's not generally the case,
unless the model can be linearized (as for a GLM fit by IWLS).
I'm insufficiently familiar with the computational details of LAD
regression (or quantile regression more generally) to know whether a
more efficient computation is possible there, but unless the data set is
very large, in which case it's highly unlikely that influence of
individual cases is an issue, the brute-force approach should be
feasible and very easy to program.
I hope this helps,
John
--
John Fox, Professor Emeritus
McMaster University
Hamilton, Ontario, Canada
web: https://socialsciences.mcmaster.ca/jfox/
Jim
On Mon, Mar 21, 2022 at 11:57 AM Kelly Thompson wrote:
I'm wanting to calculate Cook's distance for least absolute deviation
(lad) regressions.
Which R packages and functions offer this?
Thanks!
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