On Sun, 17 Feb 2008, Gustaf Granath wrote: > Hi John, > > In fact I am still a little bit confused because I had read the > ?effect help and the archives. > > ?effect says that the confidence intervals are on the linear predictor > scale as well. Using exp() on the untransformed confidence intervals > gives me the same values as summary(eff). My confidence intervals > seems to be correct and reflects the results from my glm models. > > But when I use exp() to get the correct SEs on the response scale I > get SEs that sometimes do not make sense at all. Interestingly I have
What exactly are you doing here? I suspect you are not using the correct formula to transform the SEs (you do not just exponeniate them), but without the reproducible example asked for we cannot tell. > found a trend. For my model with adjusted means ~ 0.5-1.5 I get huge > SEs (SEs > 1, but my glm model shows significant differences between > level 1 = 0.55 and level 2 = 1.15). Models with means around 10-20 my > SEs are fine with exp(). Models with means around 75-125 my SEs get > way too small with exp(). > > Something is not right here (or maybe they are but I don not > understand it) so I think my best option will be to use the confidence > intervals instead of SEs in my plot. If you want confidence intervals, you are better off computing those on a reasonable scale and transforming then. Or using a profile likelihood to compute them (which will be equivariant under monotone scale transformations). > Regards, > > Gustaf > > >> Quoting John Fox <[EMAIL PROTECTED]>: >> >> Dear Gustaf, >> >> From ?effect, "se: a vector of standard errors for the effect, on the scale >> of the linear predictor." Does that help? >> >> Regards, >> John >> >> -------------------------------- >> John Fox, Professor >> Department of Sociology >> McMaster University >> Hamilton, Ontario, Canada L8S 4M4 >> 905-525-9140x23604 >> http://socserv.mcmaster.ca/jfox >> >> >>> -----Original Message----- >>> From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] >>> project.org] On Behalf Of Gustaf Granath >>> Sent: February-16-08 11:43 AM >>> To: r-help@r-project.org >>> Subject: [R] Weird SEs with effect() >>> >>> Hi all, >>> >>> Im a little bit confused concerning the effect() command, effects >>> package. >>> I have done several glm models with family=quasipoisson: >>> >>> model <-glm(Y~X+Q+Z,family=quasipoisson) >>> >>> and then used >>> >>> results.effects <-effect("X",model,se=TRUE) >>> >>> to get the "adjusted means". I am aware about the debate concerning >>> adjusted means, but you guys just have to trust me - it makes sense >>> for me. >>> Now I want standard error for these means. >>> >>> results.effects$se >>> >>> gives me standard error, but it is now it starts to get confusing. The >>> given standard errors are very very very small - not realistic. I >>> thought that maybe these standard errors are not back transformed so I >>> used exp() and then the standard errors became realistic. However, for >>> one of my glm models with quasipoisson the standard errors make kind >>> of sense without using exp() and gets way to big if I use exp(). To be >>> honest, I get the feeling that Im on the wrong track here. >>> >>> Basically, I want to know how SE is calculated in effect() (all I know >>> is that the reported standard errors are for the fitted values) and if >>> anyone knows what is going on here. >>> >>> Regards, >>> >>> Gustaf Granath >>> >>> ______________________________________________ >>> 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. >> >> > > ______________________________________________ > 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. > -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ 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.