[R] [Fwd: Re: Parameterization puzzle]

[Murray Jorgensen]

Bother! This cold has made me accident-prone. I meant to hit Reply-all.
Clarification below.

-------- Original Message --------
Subject: Re: [R] Parameterization puzzle
Date: Fri, 21 Jul 2006 19:10:03 +1200
From: Murray Jorgensen <maj at waikato.ac.nz>
To: Prof Brian Ripley <ripley at stats.ox.ac.uk>
References: <44C063E5.3020703 at waikato.ac.nz> 
<Pine.LNX.4.64.0607210716270.12611 at gannet.stats.ox.ac.uk>

Apologies for a non-selfcontained example. Here is what I should have sent:

pyears <- scan()
18793 52407 10673 43248 5710 28612 2585 12663 1462 5317

deaths <- scan()
2 32 12 104 28 206 28 186 31 102

l <- log(pyears)
Smoke <- gl(2,1,10,labels=c("No","Yes"))
Age <- gl(5,2,10,labels=c("35-44","45-54","55-64","65-74","75-84"),
            ordered=TRUE)
mod1.glm <- glm(deaths ~ Age * Smoke + offset(l),family=poisson)
summary(mod1.glm)
age <- as.numeric(Age)
mod2.glm <- aso1.glm <- glm(deaths ~ poly(age,2) + Smoke +
                       poly(age,1):Smoke + offset(l),family=poisson)
summary(mod2.glm)


Cheers, Murray Jorgensen

Prof Brian Ripley wrote:
> R does not know that poly(age,2) and poly(age,1) are linearly dependent.
> (And indeed they only are for some functions 'poly'.)
> 
> I cannot reproduce your example ('l' is missing), but perhaps
> 
> glm(deaths ~ poly(age,2) + poly(age,1)*Smoke + offset(l), poisson)
> 
> was your intention?
> 
> On Fri, 21 Jul 2006, Murray Jorgensen wrote:
> 
>> Consider the following example (based on an example in Pat Altham's GLM 
>> notes)
>>
>> pyears <- scan()
>> 18793 52407 10673 43248 5710 28612 2585 12663 1462 5317
>>
>> deaths <- scan()
>> 2 32 12 104 28 206 28 186 31 102
>>
>> Smoke <- gl(2,1,10,labels=c("No","Yes"))
>> Age <- gl(5,2,10,labels=c("35-44","45-54","55-64","65-74","75-84"),
>>             ordered=TRUE)
>> mod1.glm <- glm(deaths ~ Age * Smoke + offset(l),family=poisson)
>> summary(mod1.glm)
>> age <- as.numeric(Age)
>> mod2.glm <- aso1.glm <- glm(deaths ~ poly(age,2) + Smoke +
>>                        poly(age,1):Smoke + offset(l),family=poisson)
>> summary(mod2.glm)
>>
>>
>>
>> The business part of the summary for the first model
>>
>>                 Estimate Std. Error z value Pr(>|z|)
>> (Intercept)    -5.92706    0.16577 -35.754  < 2e-16 ***
>> Age.L           4.06490    0.47414   8.573  < 2e-16 ***
>> Age.Q          -1.08293    0.41326  -2.620 0.008781 **
>> Age.C           0.24158    0.31756   0.761 0.446816
>> Age^4           0.04244    0.23061   0.184 0.853986
>> SmokeYes        0.61916    0.17296   3.580 0.000344 ***
>> Age.L:SmokeYes -1.31234    0.49267  -2.664 0.007729 **
>> Age.Q:SmokeYes  0.39043    0.43008   0.908 0.363976
>> Age.C:SmokeYes -0.29593    0.33309  -0.888 0.374298
>> Age^4:SmokeYes -0.03682    0.24432  -0.151 0.880218
>>
>> inspires me to fit the second model that omits the nonsignificant terms, 
>> however this produces the summary
>>
>>                        Estimate Std. Error z value Pr(>|z|)
>> (Intercept)            -5.8368     0.1213 -48.103  < 2e-16 ***
>> poly(age, 2)1           3.9483     0.1755  22.497  < 2e-16 ***
>> poly(age, 2)2          -1.0460     0.1448  -7.223 5.08e-13 ***
>> SmokeYes                0.5183     0.1262   4.106 4.02e-05 ***
>> SmokeNo:poly(age, 1)    1.3755     0.4340   3.169  0.00153 **
>> SmokeYes:poly(age, 1)       NA         NA      NA       NA
>>
>> Why do we get a SmokeNo:poly(age, 1) term? Can I re-express mod2.glm so 
>> that this term does not appear?
>>
>> Cheers,  Murray Jorgensen
>>
>>
> 

-- 
Dr Murray Jorgensen      http://www.stats.waikato.ac.nz/Staff/maj.html
Department of Statistics, University of Waikato, Hamilton, New Zealand
Email: maj at waikato.ac.nz                                Fax 7 838 4155
Phone  +64 7 838 4773 wk    Home +64 7 825 0441    Mobile 021 1395 862

-- 
Dr Murray Jorgensen      http://www.stats.waikato.ac.nz/Staff/maj.html
Department of Statistics, University of Waikato, Hamilton, New Zealand
Email: maj at waikato.ac.nz                                Fax 7 838 4155
Phone  +64 7 838 4773 wk    Home +64 7 825 0441    Mobile 021 1395 862