Ah, okay, binomial. Then it seems that the few responses you have within MagNew are all or mostly no sells (assuming no sell = 0 and sell = 1). At least, that would explain the really low coefficient for that level.
When I said that maybe the data have been entered incorrectly, I meant the response variable (not the Mag factor). But since you are dealing with a binomial response, this doesn't seem to be the issue here anyway. Now, regarding the AIC and why it would be lower if you include the Mag factor. I do not think the large SE itself has anything to do with it. Looking at the results you showed, it doesn't seem as if the Mag factor itself is a good predictor of sell/no sell. Another hunch: maybe there are suppression effects going on, so other predictors in the model are able to do a better job of predicting sell/no sell when the Mag factor is included. Best, -- Wolfgang Viechtbauer http://www.wvbauer.com/ Department of Methodology and Statistics Tel: +31 (0)43 388-2277 School for Public Health and Primary Care Office Location: Maastricht University, P.O. Box 616 Room B2.01 (second floor) 6200 MD Maastricht, The Netherlands Debyeplein 1 (Randwyck) ----Original Message---- From: Chris Mcowen [mailto:sam_sm...@me.com] Sent: Wednesday, October 06, 2010 16:38 To: Viechtbauer Wolfgang (STAT) Cc: r-help@r-project.org Subject: Re: [R] Highly significant intercept and large standard error > Hi Wolfgang, > > Thanks for this, it makes sense. > > I should of been more detailed when i described my model, it is in > fact binomial - sell or not. > >> remove the Mag factor from the model, you get a model with just an >> intercept, reflecting the overall mean > > This is true, but what i was trying to say ( not very well!) was i > have other factors such as price (High,Mid,Low), condition ( > Best,Average,Poor) etc etc and all models that have Mag in them have > a much better AIC than models without Mag, and i was unsure if this > was a artefact of the high SE for the MagNew rather than Mag being a > key factor? > >> Maybe the data have been entered incorrectly > > I have checked this and all is fine, they are categorical variables > not continuous so it is either MAG - New, Old or Mid. > > Sam > > > > On 6 Oct 2010, at 15:05, Viechtbauer Wolfgang (STAT) wrote: > > I do not know about the details of the model, but the results are not > all that strange. I'll assume that you are using family=gaussian(), > so you are essentially running a model where (Intercept) reflects the > mean of the dependent variable for that third category (MagMid) of > the Mag factor and MagNew and MagOld are the mean differences between > MagMid and those two other categories. > > If you remove the Mag factor from the model, you get a model with > just an intercept, reflecting the overall mean. Two things will > happen. That overall mean is essentially a weighted average of the > three level-specific means. MagMid and MagOld are the most frequent > categories and both these means are close to zero, so the overall > mean will be pulled close to zero. Moreover, the amount of > variability around the overall mean will be larger than the amount of > variability around the level-specific means. This will lead to a > larger standard error for the overall mean. Hence, it could very well > happen that the intercept is no longer significant when you remove > that factor. > > Given that MagNew only occured a few times and given its very > different mean and huge standard error, I suspect that some value(s) > within that level are "screwy". Maybe the data have been entered > incorrectly. One thing I have seen happen a few times is that missing > data were coded, for example, as a -9999 in the dataset created with, > for example, SPSS, but were then accidentally treated as observed > values when analyzed with some other software, such as R. That could > cause such a low mean for that category and the huge SE. > > It's just a hunch. Could be anything, but I would certainly take > another good look at the values within that level. > > Best, > > >> Dear list, >> >> I am running a lmer model and have a question. >> >> When ever i put a factor (Mag) in my model it lowers the AIC of the >> model, however the intercept is the only value with significant >> p-value. I have looked at the coefficients and the standard error >> and something jumps out at me. >> >> >> Estimate Std. Error z value Pr(>|z|) >> (Intercept) -1.35778 0.30917 -4.392 1.12e-05 *** >> MagNew -15.76939 1255.06372 -0.013 0.990 >> MagOld 0.14250 0.25246 0.564 0.572 >> >> MagNew relates to a categorical factor (Mag) that has 3 levels of >> which New is one and Old is another ( The third is not displayed). >> >> It appears MagNew has a huge Std.Error, what could cause this? >> >> When i do str(Mag) you will see that New is relatively rare (29 out >> of 871) i presume it is this that is raising the Std.Error value. >> however i am not sure why this is causing the intercept to have a >> highly significant p value . Furthermore how do i interpret it, I am >> using AIC values as my basis of model selection and i am unsure if >> this really is the most likely model or not? >> >> Thanks >> >> Sam ______________________________________________ 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.