RE: [S] Adjusted variables plots

John Wallace (jrw@fish.washington.edu)
Tue, 10 Mar 1998 11:27:04 -0800 (PST)


Steve,

It seems the online help for graphsheet() is misleading, it states (under
pages):

"pages="every graph" (or TRUE) means to always add tabbed pages."

But, I tried "every graph" and did get all 4 tabbed pages in the
first call to graphsheet.

---------------

fuel.lm <- lm(Fuel ~ Weight + Disp., data = fuel.frame)

graphsheet(page = "every graph")

plot(cooks.mat[,"Weight"], type = "h", xlab = "Weight",
ylab = "Cook's Distance")
plot(cooks.mat[,"Disp."], type = "h", xlab = "Disp.",
ylab = "Cook's Distance")
plot.gam(fuel.lm, resid = T, rug = F, scale = 2)

Thanks for the informative post.

-John

On Tue, 10 Mar 1998, Steven Paul Millard wrote:

> Hello,
>
> John Thaden asked about creating Cook's Distance plots for each
> coefficient in a multiple regression model, and also about partial
> residual plots.
>
> 1. You can use the function Cook.terms() to compute Cook's distances
> for each coefficient. This function is available in version 4.0, but
> there is no help file for it. It is explained on pp.230-233 of
> Chambers and Hastie ("Statistical Models in S"). It takes as its
> argument the result of calling lm() or glm().
>
> 2. To create partial residual plots, you can call the function
> plot.gam() explicitly for an "lm" or "glm" object. The partial
> residuals that are plotted are actually the centered partial residuals.
>
> You can get more information on these functions in the elusive
> "Statistical Models in S" book by Chambers and Hastie, and also in the
> "Statistical Models in S-PLUS" training manual (I'm not sure whether
> MathSoft sells this manual, you may have to actually take the course to
> get it).
>
> Here is an example:
>
> fuel.lm <- lm(Fuel ~ Weight + Disp., data = fuel.frame)
>
> cooks.mat <- Cook.terms(fuel.lm)
> graphsheet(page = T)
> plot(cooks.mat[,"Weight"], type = "h", xlab = "Weight",
> ylab = "Cook's Distance")
> plot(cooks.mat[,"Disp."], type = "h", xlab = "Disp.",
> ylab = "Cook's Distance")
>
> graphsheet()
> plot.gam(fuel.lm, resid = T, rug = F, scale = 2)
>
> Note: the second call to graphsheet() is used because otherwise
> plot.gam() will erase the cook's distance plots in the original
> graphsheet() (feature?).
>
>
> Sincerely,
>
> --Steve M.
>
> _____________
> | *** | Steven P. Millard, Ph.D.
> | * |
> | * * * | P robability, TEL: 206-528-4877
> | * * * | S tatistics & FAX: 206-528-4802
> | * | I nformation E-mail: SMillard@ProbStatInfo.com
> | * | Web: www.ProbStatInfo.com
> | *** | 7723 44th Avenue NE
> |___________| Seattle, WA 98115-5117 USA
>
>
> -----Original Message-----
> From: John Thaden [SMTP:jjthaden@life.uams.edu]
> Sent: Friday, March 06, 1998 12:30 PM
> To: s-news@wubios.wustl.edu
> Subject: [S] Adjusted variables plots
>
> I want to look for high-influence observations in my dataset. The
> Cook's
> distance is useful, but I'm interested in looking at influentials
> separately for each coefficient of a multivariate regression. I've
> used
> adjusted-variables plots (I think they are also called
> partial-regression
> plots) in the past to find outliers and leverage points. So for the
> k-th
> coefficient coefficient Beta sub-k, I would plot residuals of the
> regression of outcome Y on all predictors X except the k-th, versus
> residuals of the regression of the k-th X on all X's except the k-th.
>
> Before I try to write a function to speed up this type of diagnostic
> analysis, I'm wondering is such a function exists? Or can predict() be
> used to this end? I've found nothing in the SPlus v. 4.0
> documentation.
> Should I be looking in libraries contributed by others?
>
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-- 
John Wallace
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