Benjamin, A couple of points:
> fit <- glm(y ~ x, gaussian(link = "log")) does NOT fit a model with a lognormal response distribution. It fits a non-linear regression model with an ordinary gaussian response distribution. This model has constant variance, whereas the lognormal model (which you would fit by transforming the response) has constant coefficient of variation. You would transform the response for two reasons, namely it should linearize the relationship between (transformed) response and predictors AND it should change a constant CV into homoscedasticity, or constant variance. This latter property as important as the first, usually. You should not think of a glm with log link as a kind of handy alternative to a log-transformed regression as they are in reality very different models. Second point: you claim that the calls > fitA <- glm(y ~ x, gaussian(link = "log")) > fitB <- glm(y ~ x, gaussian) > fitC <- lm(y ~ x) give identical results. This is NOT true for me on R 2.0.1 (Windows), so you may care to check that, (although fitB and fitC are fully equivalent, of course). When you sort out the model you really need to use, you may find stepAIC in the MASS library useful as one tool for model selection, or at least for steps towards that generally rather complex goal. Bill Venables. -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Benjamin M. Osborne Sent: Monday, 13 December 2004 12:40 PM To: [EMAIL PROTECTED] Subject: [R] AIC, glm, lognormal distribution I'm attempting to do model selection with AIC, using a glm and a lognormal distribution, but: fit1<-glm(BA~Year,data=pdat.sp1.65.04, family=gaussian(link="log")) ## gives the same result as either of the following: fit1<-glm(BA~Year,data=pdat.sp1.65.04, family=gaussian) fit1<-lm(BA~Year,data=pdat.sp1.65.04) fit1 #Coefficients: #(Intercept) Year2004 # -1.6341 -0.2741 #Degrees of Freedom: 84 Total (i.e. Null); 83 Residual #Null Deviance: 1.521 #Residual Deviance: 1.476 AIC: -97.31 fit1<-glm(BA~Year,data=pdat.sp1.65.04, family=quasi(link="log")) # also gives the same result but returns AIC: NA ## Is it possible to model a lognormal distribution without having to transform ## the data themselves? (i.e.: fit1<-lm(log(BA)~Year,data=pdat.sp1.65.04) Thanks in advance, Ben Osborne -- Botany Department University of Vermont 109 Carrigan Drive Burlington, VT 05405 [EMAIL PROTECTED] phone: 802-656-0297 fax: 802-656-0440 ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
