From maechler at stat.math.ethz.ch  Wed Apr 27 17:30:06 2005
From: maechler at stat.math.ethz.ch (Martin Maechler)
Date: Wed Apr 27 17:30:27 2005
Subject: [Rd] Enhanced version of plot.lm()
In-Reply-To: <x2zmvkz2wl.fsf@biostat.ku.dk>
References: <200504251000.j3PA01lD009332@hypatia.math.ethz.ch>
	<40567787fcb17bee0bf9ecf403fb8e21@anu.edu.au>
	<320af97cfe90da4c59b7e9c8cd9956d4@anu.edu.au>
	<9717926f912f9fb045c57dd9be4e743c@warwick.ac.uk>
	<b21d1d1a13e8f82d4e1d44c233e9f76e@anu.edu.au>
	<17006.5202.986122.321760@stat.math.ethz.ch>
	<17007.39440.105964.263932@stat.math.ethz.ch>
	<x2zmvkz2wl.fsf@biostat.ku.dk>
Message-ID: <17007.45054.561976.705623@stat.math.ethz.ch>

>>>>> "PD" == Peter Dalgaard <p.dalgaard@biostat.ku.dk>
>>>>>     on 27 Apr 2005 16:54:02 +0200 writes:

    PD> Martin Maechler <maechler@stat.math.ethz.ch> writes:
    >> I'm about to commit the current proposal(s) to R-devel,
    >> **INCLUDING** changing the default from 
    >> 'which = 1:4' to 'which = c(1:3,5)
    >> 
    >> and ellicit feedback starting from there.
    >> 
    >> One thing I think I would like is to use color for the Cook's
    >> contours in the new 4th plot.

    PD> Hmm. First try running example(plot.lm) with the modified function and
    PD> tell me which observation has the largest Cook's D. With the suggested
    PD> new 4th plot it is very hard to tell whether obs #49 is potentially or
    PD> actually influential. Plots #1 and #3 are very close to conveying the
    PD> same information though...

I shouldn't be teaching here, and I know that I'm getting into fighted
territory (regression diagnostics; robustness; "The" Truth, etc,etc)
but I believe there is no unique way to define "actually influential"
(hence I don't believe that it's extremely useful to know
exactly which Cook's D is largest).

Partly because there are many statistics that can be derived from a
multiple regression fit all of which are influenced in some way. 
AFAIK, all observation-influence measures g(i) are functions of
(r_i, h_{ii}) and the latter are the quantities that "regression
users" should really know {without consulting a text book} and
that are generalizable {e.g. to "linear smoothers" such as
gam()s (for "non-estimated" smoothing parameter)}.

Martin


[Rd] Enhanced version of plot.lm()
Martin Maechler