From: Berwin A Turlach <berwin_at_maths.uwa.edu.au>

Date: Fri 21 Jul 2006 - 18:16:32 EST

In this parameterisation a 'poly(age,1)' term will appear among the coefficients with an estimated value of NA since it is aliased with 'poly(age, 2)1'. So I don't believe that this was Murray's intention.

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Fri Jul 21 18:23:21 2006

Date: Fri 21 Jul 2006 - 18:16:32 EST

>>>>> "BDR" == Prof Brian Ripley <ripley@stats.ox.ac.uk> writes:

BDR> (And indeed they only are for some functions 'poly'.) I am surprised about this. Should probably read the help page of 'poly' once more and more carefully.

BDR> I cannot reproduce your example ('l' is missing), [...] My guess is that 'l' is 'pyears'. At least, I worked under that assumption.

Interestingly, on my machine (using R 2.3.1, 2.2.1 and 2.1.1) I cannot fit any of the Poisson GLM that Murray tried. I always get the error message:

Error: no valid set of coefficients has been found: please supply starting values

But I have to investigate this further. I can fit binomial models that give me similar answers.

BDR> [...] but perhaps BDR> glm(deaths ~ poly(age,2) + poly(age,1)*Smoke + offset(l), BDR> poisson) BDR> was your intention?

In this parameterisation a 'poly(age,1)' term will appear among the coefficients with an estimated value of NA since it is aliased with 'poly(age, 2)1'. So I don't believe that this was Murray's intention.

The only suggestion I can come up with is:

> summary(glm(cbind(deaths, l-deaths) ~ age*Smoke+I(age^2), family=binomial))

[...]

Coefficients:

Estimate Std. Error z value Pr(>|z|)

(Intercept) -10.79895 0.45149 -23.918 < 2e-16 *** age 2.37892 0.20877 11.395 < 2e-16 *** SmokeYes 1.44573 0.37347 3.871 0.000108 *** I(age^2) -0.19706 0.02749 -7.168 7.6e-13 *** age:SmokeYes -0.30850 0.09756 -3.162 0.001566 **

[...]

Which doesn't use orthogonal polynomials anymore. But I don't see how you can fit the model that Murray want to fit using orthogonal polynomials given the way R's model language operates.

So I guess the Poisson GLM that Murray wants to fit is:

glm(deaths~ age*Smoke+I(age^2)+offset(l), family=poisson)

Cheers,

Berwin

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