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
