On 05-Dec-04 Patrick Foley wrote:
It is easy to spot response nonlinearity in normal linear
models using plot(something.lm).
However plot(something.glm) produces artifactual peculiarities
since the diagnostic residuals are constrained by the fact
that y can only take values 0 or 1.
What do
(Ted Harding) wrote:
On 05-Dec-04 Patrick Foley wrote:
It is easy to spot response nonlinearity in normal linear
models using plot(something.lm).
However plot(something.glm) produces artifactual peculiarities
since the diagnostic residuals are constrained by the fact
that y can only take values 0
On 05-Dec-04 Ted Harding wrote:
[...]
For example, adopting the ritual sigificant == P0.05,
power = 80%, you can see a histogram of the p-values
over the conventional significance breaks with
hist(pvals,breaks=c(0,0.01,0.03,0.1,0.5,0.9,0.95,0.99,1),freq=TRUE)
Sorry for the typo! That
On 05-Dec-04 Peter Dalgaard wrote:
Peter Dalgaard [EMAIL PROTECTED] writes:
Re. the smoothed residuals, you do need to be careful about the
smoother. Some of the robust ones will do precisely the wrong thing
in this context: You really are interested in the mean, not some
trimmed mean (which
(Ted Harding) [EMAIL PROTECTED] writes:
x - runif(500)
y - rbinom(500,size=1,p=plogis(x))
xx - predict(loess(resid(glm(y~x,binomial))~x),se=T)
matplot(x,cbind(xx$fit, 2*xx$se.fit, -2*xx$se.fit),pch=20)
Not sure my money isn't still on the splines, though.
.
Serves me right for
On 05-Dec-04 Peter Dalgaard wrote:
(Ted Harding) [EMAIL PROTECTED] writes:
x - runif(500)
y - rbinom(500,size=1,p=plogis(x))
xx - predict(loess(resid(glm(y~x,binomial))~x),se=T)
matplot(x,cbind(xx$fit, 2*xx$se.fit, -2*xx$se.fit),pch=20)
Not sure my money isn't still on the splines,
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of
[EMAIL PROTECTED]
Sent: Sunday, December 05, 2004 7:14 PM
To: [EMAIL PROTECTED]
Subject: Re: [R] What is the most useful way to detect
nonlinearity in lo
On 05-Dec-04 Peter Dalgaard wrote:
