From john.maindonald at anu.edu.au  Wed Sep 14 08:09:06 2005
From: john.maindonald at anu.edu.au (John Maindonald)
Date: Wed, 14 Sep 2005 16:09:06 +1000
Subject: [Rd] plot(<lm>): new behavior in R-2.2.0 alpha
In-Reply-To: <17190.57225.254938.977464@stat.math.ethz.ch>
References: <17190.57225.254938.977464@stat.math.ethz.ch>
Message-ID: <81A1F734-F502-4D4E-9C73-D59CB11EF010@anu.edu.au>

Following the >20 messages that Martin mentioned, I had
private discussion with John Fox, which in part lies behind
following questions:

(1) In plot 5, should we have, maybe as an option, vertical
lines at 2hbar and 3hbar, as in the plots produced by the
function that John Fox sent me.  I think this would be a useful
addition, but made no move to add it at that time, considering
it best to put that on a toThink About list.

(2) John also sent code for a plot that place contours of the
covratio on the points that are shown in plot 5.  This could
be added as an option to plot 5.
(The covratio statistic is a measure of the effect of omitting a
point on the variance-covariance matrix.  It is a function of
the residual and the leverage.)  Also there is the possibility
(I am not keen on this) to show Bonferroni critical values for
studentized residuals.

(3) My reaction to the new plot 6 (David Firth's proposal) is
sufficiently similar to John Fox's that I would not use it as a
matter of course.  I think it useful however, precisely because
it does offer a perspective on the information in plot 5 that
is, at first look, startlingly different.

(4) Are there other diagnostics that ought to be included in
stats? (perhaps in a function other than plot.lm(), which risks
being overloaded).  One strong claiment is vif() (variance
inflation factor), of which there are versions both in car and
(written by myself) in DAAG.  John Fox's function does more
than mine.  Thus, assuming that he is willing for it to be taken
across, that should go into stats.

(5) termplot() provides partial residual (component + residual)
plots, which I think extraordinarily useful.  They deserve to be
widely used.
Should partial regression plots also be available?

(6) It should be fairly easy to construct a function that would
examine the distribution of statistics of interest under repeated
bootstrap sampling or simulation.  This can be useful when
with small samples, when it is easy to over-interpret diagnostic
statistics.

(7) There are special issues, not just for aov models, but also
for glm() and (extending the discussion quite a lot) the models
that are fitted by lme()/lmer() [nlme/lme4].

(8) Are there special issues that require attention for large
datasets? [I'm sure there are, but regression diagnostics may
not be the best point of entry into the discussion.]

(9) How about a help(Diagnostics) entry?

(10) Maybe it would be useful to form a (small?) group to look at
what should go into:
  (a) stats
  (b) a specialist diagnostics package
Even if this idea is taken up, some preliminary wider canvassing
of the opinions of members of this list seems desirable.

John Maindonald.


On 14 Sep 2005, at 12:17 AM, Martin Maechler wrote:

> As some of you R-devel readers may know, the plot() method for
> "lm" objects is based in large parts on contributions by John
> Maindonald, subsequently "massaged" by me and other R-core
> members.
>
> In the statistics litterature on applied regression, people have
> had  diverse oppinions on what (and how many!) plots should be
> used for goodness-of-fit / residual diagnostics, and to my
> knowledge most people have agreed to want to see one (or more)
> version of a Tukey-Anscombe plot {Residuals ~ Fitted} and a QQ
> normal plot.
> Another consideration was to be somewhat close to what S
> (S-plus) was doing.  So we have two versions of residuals vs
> fitted, one for checking  E[error] = 0, the other for checking
> Var[error] = constant.  So we got to the first three plots of
> plot.lm() about which I don't want to debate at the moment
> {though, there's room for improvement even there: e.g., I know of at
>  least one case where plot(<lm>) wasn't used because the user
>  was missing the qqline() she was so used to in the QQ plot}
>
> The topic of this e-mail is the (default) 4th plot which I had
> changed; really prompted by the following:
> More than three months ago, John wrote
>   http://tolstoy.newcastle.edu.au/R/devel/05/04/0594.html
>     (which became a thread of about 20 messages, from Apr.23 -- 29,  
> 2005)
>
> and currently,
> NEWS for R 2.2.0 alpha contains
>
>
>>> USER-VISIBLE CHANGES
>>>
>>>    o plot(<lm object>) uses a new default for the fourth panel when
>>>      'which' is not specified.
>>>      ___ may change before release ___
>>>
>
> and the header is
>
> plot.lm <-
> function (x, which = c(1:3, 5),
>           caption = c("Residuals vs Fitted",
>                   "Normal Q-Q", "Scale-Location",
>               "Cook's distance", "Residuals vs Leverage",
>               "Cook's distance vs Leverage"),
>        ......... ) {..............}
>
> So we now have 6 possible plots, where 1,2,3 and 5 are the
> defaults (and 1,2,3,4 where the old defaults).
>
> For the influential points and combination of 'influential' and  
> 'outlier'
> there have been quite a few more proposals in the past. R <= 2.1.x
> has been plotting the  Cook's distances vs. observation number,  
> whereas
> quite a few people in the past have noted that all influence
> measures being more or less complicated functions of residuals
> and "hat values" aka "leverages", (R_i, h_{ii}), it would really
> make sense and fit more to the other plots
> to plot residuals vs. Leverages --- with the additional idea of
> adding *contours* of (equal) Cook's distances to that plot, in
> case one would really want to seem them.
>
> In the mean time, this has been *active* in R-devel for quite a
> while, and we haven't received any new comments.
>
> One remaining problem I'd like to address is the "balanced AOV"
> situation, something probably pretty rare nowadays in real
> practice, but common of course in teaching ANOVA.
> As you may remember, in a balanced design, all observations have
> the same leverages h_{ii}, and the plot  R_i  vs  h_ii is really
> not so useful.  In that case,  the cook distances CD_i = c *  R_i ^2
> and so  CD_i  vs  i {the old "4-th plot in plot.lm"} is
> graphically identical to   R_i^2 vs i.
> Now in that case (of identical h_ii's), I think one would really
> want  "R_i  vs  i".
>
> Question to the interested parties:
>
>   Should there be an automatism
>      ``when h_ii == const''  {"==" with a bit of numerical fuzz}
>   plot a)  R_i   vs i
>   or   b)  CD_i  vs i
>
> or should users have to manually use
>     plot(<lm>,  which=1:4, ...)
> in such a case?
>
> Feedback very welcome,
> particularly, you first look at the examples in help(plot.lm)
> in *R-devel* aka R-2.2.0 alpha.
>
> Martin Maechler, ETH Zurich
>
>

John Maindonald             email: john.maindonald at anu.edu.au
phone : +61 2 (6125)3473    fax  : +61 2(6125)5549
Centre for Bioinformation Science, Room 1194,
John Dedman Mathematical Sciences Building (Building 27)
Australian National University, Canberra ACT 0200.



[Rd] plot(<lm>): new behavior in R-2.2.0 alpha
John Maindonald