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 ______________________________________________ R-help@r-project.org mailing list 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.
