# [Rd] Bug in glm.fit() or plot.lm() (PR#778)

**murdoch@stats.uwo.ca
**
murdoch@stats.uwo.ca

*Tue, 19 Dec 2000 20:56:11 +0100 (MET)*

Here's a bug one of my students noticed.
When you call plot() on a glm object, plot.lm gets called. The second
plot it shows is supposed to give a normal QQ plot of the standard
deviance residuals, but it doesn't. The glm object created by glm.fit
returns something (the IRLS weights?) in fit$weights which plot.lm
takes as observation weights, so you get strange residuals in the QQ
plot.
For example, here I fit a simulated Poisson model where the model
perfectly fine, but the plot.lm QQ plot makes the residuals look huge,
ranging between -45 and 40.
>*set.seed(1)
*>*x <- seq(4,5,len=1000)
*>*y <- rpois(1000,exp(x))
*>*fit <- glm(y ~ x, family=poisson)
*>*plot(fit, which=2)
*
If I extract the residuals myself and plot them, I see that the model
is fine:
>*qqnorm(residuals(fit))
*
The problem is that the weights are big; plot.lm should be using
prior.weights, or glm should be returning the big weights under a
different name:
>* summary(fit$weights)
* Min. 1st Qu. Median Mean 3rd Qu. Max.
54.35 69.90 89.90 93.76 115.60 148.70
>* summary(fit$prior.weights)
* Min. 1st Qu. Median Mean 3rd Qu. Max.
1 1 1 1 1 1
Duncan Murdoch
--please do not edit the information below--
Version:
platform = i386-pc-mingw32
arch = x86
os = Win32
system = x86, Win32
status =
major = 1
minor = 2.0
year = 2000
month = 12
day = 15
language = R
Windows 9x 4.10 (build 1998)
Search Path:
.GlobalEnv, package:ctest, Autoloads, package:base
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