Thank you, Dr Ripley. After some false starts and consulting MASS2,
Chambers&Hastie and the help files, this worked acceptably.
> xxx$issuecat2<-C(xxx$issuecat2,poly,1)
> attr(xxx$issuecat2,"contrasts")
.L
0-39 -6.324555e-01
40-49 -3.162278e-01
50-59 -3.287978e-17
60-69 3.162278e-01
70+ 6.324555e-01
> exp.mdl<-glm(actual~gendercat+issuecat2+smokecat,
data=xxx,family="poisson",offset=expected)
> summary(exp.mdl)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.5596 -0.2327 -0.1671 -0.1199 5.2386
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.57125 0.06650 -68.743 < 2e-16 ***
gendercatMale 0.29660 0.06426 4.615 3.92e-06 ***
issuecat2.L 2.09161 0.09354 22.360 < 2e-16 ***
smokecatSmoker 0.22178 0.07870 2.818 0.00483 **
smokecatUnknown 0.02378 0.08607 0.276 0.78233
The reference category is different, but the effect of a one category
increase in age-decade on the log(rate) is(2.09*0.316) = 0.6604 which
seems acceptable agreement with my earlier as.numeric(factor) estimate
of 0.6614.
--
David Winsemius
Prof Brian Ripley <[EMAIL PROTECTED]> wrote in
news:[EMAIL PROTECTED]:
> Further to Duncan's comments, you can control factor codings via
> options(contrasts=), by setting contrasts() on the factor and via
> C(). This does enable you to code an ordered factor as a linear
> term, for example.
>
> The only place I know that this is discussed in any detail is in
> Bill Venables' account in MASS chapter 6.
>
> On Sat, 5 Jan 2008, Duncan Murdoch wrote:
>
>> On 05/01/2008 7:16 PM, David Winsemius wrote:
>>> David Winsemius <[EMAIL PROTECTED]> wrote in
>>> news:[EMAIL PROTECTED]:
>>>
>>>> I have a variable which is roughly age categories in decades. In
>>>> the original data, it came in coded:
>>>>> str(xxx)
>>>> 'data.frame': 58271 obs. of 29 variables:
>>>> $ issuecat : Factor w/ 5 levels "0 - 39","40 - 49",..: 1 1 1
>>>> 1...
>>>> snip
>>>>
>>>> I then defined issuecat as ordered:
>>>>> xxx$issuecat<-as.ordered(xxx$issuecat)
>>>> When I include issuecat in a glm model, the result makes me think
>>>> I have asked R for a linear+quadratic+cubic+quartic polynomial
>>>> fit. The results are not terribly surprising under that
>>>> interpretation, but I was hoping for only a linear term (which I
>>>> was taught to call a "test of trend"), at least as a starting
>>>> point.
>>>>
>>>>> age.mdl<-glm(actual~issuecat,data=xxx,family="poisson")
>>>>> summary(age.mdl)
>>>> Call:
>>>> glm(formula = actual ~ issuecat, family = "poisson", data = xxx)
>>>>
>>>> Deviance Residuals:
>>>> Min 1Q Median 3Q Max
>>>> -0.3190 -0.2262 -0.1649 -0.1221 5.4776
>>>>
>>>> Coefficients:
>>>> Estimate Std. Error z value Pr(>|z|)
>>>> (Intercept) -4.31321 0.04865 -88.665 <2e-16 ***
>>>> issuecat.L 2.12717 0.13328 15.960 <2e-16 ***
>>>> issuecat.Q -0.06568 0.11842 -0.555 0.579
>>>> issuecat.C 0.08838 0.09737 0.908 0.364
>>>> issuecat^4 -0.02701 0.07786 -0.347 0.729
>>>>
>>>> This also means my advice to a another poster this morning may
>>>> have been misleading. I have tried puzzling out what I don't
>>>> understand by looking at indices or searching in MASSv2, the Blue
>>>> Book, Thompson's application of R to Agresti's text, and the FAQ,
>>>> so far without success. What I would like to achieve is having
>>>> the lowest age category be a reference category (with the
>>>> intercept being the log-rate) and each succeeding age category
>>>> be incremented by 1. The linear estimate would be the
>>>> log(risk-ratio) for increasing ages. I don't want the higher
>>>> order polynomial estimates. Am I hoping for too much?
>>>>
>>>
>>> I acheived what I needed by:
>>>
>>>> xxx$agecat<-as.numeric(xxx$issuecat)
>>>> xxx$agecat<-xxx$agecat-1
>>>
>>> The results look quite sensible:
>>>> exp.mdl<-glm(actual~gendercat+agecat+smokecat, data=xxx,
>>> family="poisson", offset=expected)
>>>> summary(exp.mdl)
>>>
>>> Call:
>>> glm(formula = actual ~ gendercat + agecat + smokecat, family =
>>> "poisson",
>>> data = xxx, offset = expected)
>>>
>>> Deviance Residuals:
>>> Min 1Q Median 3Q Max
>>> -0.5596 -0.2327 -0.1671 -0.1199 5.2386
>>>
>>> Coefficients:
>>> Estimate Std. Error z value Pr(>|z|)
>>> (Intercept) -5.89410 0.11009 -53.539 < 2e-16 ***
>>> gendercatMale 0.29660 0.06426 4.615 3.92e-06 ***
>>> agecat 0.66143 0.02958 22.360 < 2e-16 ***
>>> smokecatSmoker 0.22178 0.07870 2.818 0.00483 **
>>> smokecatUnknown 0.02378 0.08607 0.276 0.78233
>>>
>>> I remain curious about how to correctly control ordered factors,
>>> or I should just simply avoid them.
>>
>> If you're using a factor, R generally assumes you mean each level
>> is a different category, so you get levels-1 parameters. If you
>> don't want this, you shouldn't use a factor: convert to a numeric
>> scale, just as you did.
>>
>> Duncan Murdoch
>>
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