> -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] > On Behalf Of Chris Mcowen > Sent: Wednesday, October 06, 2010 7:38 AM > 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, > > -- > 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: r-help-boun...@r-project.org > [mailto:r-help-boun...@r-project.org] On Behalf Of Sam Sent: Wednesday, > October 06, 2010 14:03 To: r-help@r-project.org > Subject: [R] Highly significant intercept and large standard error > > > 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 > > > > [1] Old Old Old Old Old Old Old Old Old Old Old > > [12] Old Old Old Old Old Old Old Old Old Old Old > > [23] Old Old Old Mid Old Old Old Mid Old Old Old > > [34] Old Old Old Old Mid Old Old Old Old Old Old > > [45] Mid Mid Mid Old Old Old Mid Mid Mid > > Mid Old [56] Old Old Old Old Old Old Old Old Old Old Old > > [67] Old Old Old Old Old Old Old Old Old Old Old > > [78] Old Old Old Old Old Old Old Old Old Old Old > > [89] Old Old Old Old Old Old Old Old Old Old Old > > [100] Old Old Old Old Old Old Old Old Old New New > > [111] Old Old Old Old Old Old Old Old Old Old Mid > > [122] Mid Mid Mid Mid Old Old Old Old Mid Mid > > Mid [133] Mid Mid Mid Mid Mid Mid Mid Mid > > Mid Mid Mid [144] Mid Mid Mid Mid Old Old > > Old Mid Mid Mid Mid [155] Mid Mid Mid Mid > > Mid Mid Mid Old Old Old Old [166] Old Old Old Mid > > Mid Mid Mid Mid Mid Mid Mid [177] Mid Mid > > Mid Mid Mid Mid Mid Mid Mid Old Mid > > [188] Mid Mid Mid Mid Old Mid Mid Mid > > Mid Mid Mid [199] Mid Mid Old Old Old Old Old > > Old Old Old Old [210] Old Old Old Old Old Old Old Old Old > > Old Old [221] Old Old Old Old Old Old Old Old Old Old Old > > [232] Old Old Old Old Old Old Old Old Old Old Old [243] Old > > Old Old Old Old Old Old Old Old Old Old [254] Old Old Old > > Old Old Old Old Old Old Old Old [265] Old Old Old Old Old > > Old Old Old Old Old Old [276] Old Old Old Old Old Old Old > > Old Old Old Old [287] Old Old Old Old Old Old Old Old Old > > Old Old [298] Old Old Old Old Old Old Old Old Old Old Old > > [309] Old Old Old Old Old Old Old Old Old Old Old > > [320] Old Old Old Old Old Old Old Old Old Old Old > > [331] Old Old Old Old Old Old Old Old Old Old Mid > > [342] Old Old Old Old Old Old Old New New New New > > [353] New New New New New Old Old Old Old Old Old > > [364] Old New Old Old Old Old Old Old Old Old Old > > [375] Old Old Old Old Old Old Old Old Old Old Old > > [386] Old Old Old Old Old Old Old Old Mid Mid Mid > > [397] Mid Mid Mid Old Old Mid Old Old Mid Mid > > Mid [408] Mid Mid Mid Mid Mid Mid Mid Mid > > Mid Mid Mid [419] Old Old Old Old Mid Mid Mid > > Mid Mid Old Mid [430] Mid Mid Mid Mid Mid > > Mid Mid Mid Mid Mid Mid [441] Mid Mid Mid > > Mid Mid Mid Old Old Old Old Old [452] Old Old Old > > Old Old Old Old Mid Mid Old Old [463] Mid Mid > > Old Old Mid Mid Mid Mid Mid Old Mid [474] Mid > > Mid Old Mid Old Old Old Old Old Old Old [485] Mid > > Mid Mid Mid Mid Mid Mid Mid Mid Mid > > Old [496] Old Old Old Old Old Old Mid Old Mid Old Old > > [507] Old Old Old Old Old Old Old Old Old Old Old [518] Mid > > Mid Mid Mid Old Mid Old Mid Old Mid Mid > > [529] Old Old Mid Mid Mid Mid Mid Mid Old > > Mid Mid [540] Mid Mid Mid Mid Mid Mid Old > > Old Old Old Mid [551] Mid Mid Old Old Mid Mid > > Old Mid Old Old Old [562] Old Mid Old Old Old Mid > > Old Old Old Old Mid [573] Mid Mid Old Old Mid Mid > > Mid Mid Old Old Old [584] Mid Old Old Old Old Old > > Old Mid Mid Mid Old [595] Mid Mid Mid Old > > Old New Mid Mid Old Mid Mid [606] Mid Old Mid > > Old Old Mid Mid Mid Mid Mid Old [617] Mid > > Old Old Old Old Old Old Old Old Old Old [628] Old Old Mid > > Old Old Old Old Old Old Old Old [639] Old Old Old Old Old > > Old Old Old Old Old New [650] Old Mid Old Old Old Old > > Old Old Old Old Old [661] Old Old Old Old Old Old Old Old > > Old Old Old [672] Old Old New Old Old Old Old Old Old Old > > Old [683] New Old Old Old Old Old Old Old Old Old Old [694] > > Old Old Old Old Old Old Old Old Old Old Old [705] Old Old > > Old New Old Old New Old Old Old Old [716] New New New New New > > Old Old Old New Old Old [727] Old Old Old Old Old Old Mid > > Old Old Old New [738] Old Old Old Old Old Old Old Old Old > > Old Old [749] Old Old Old Old Old Old Old Old Old Old Old > > [760] New Old Old Old Old Old Old Old Old Old New > > [771] Old Old Old Old Old Old Mid Old Old New Old > > [782] Old Old Old Old Old Old Old Old Old Old Old > > [793] Old Old Old Old Old Old Old Old Old Old Old > > [804] Old Old Old Old Old Old Old Old Old Old Old > > [815] Old Old Old Old Old Old Old Old Old Old Old > > [826] Old Old Old Old Old Old Old Old Old Old Old > > [837] Old Old Old Old Old Old Old Old Old Old Old > > [848] Old Old Old Old Old Old Old Old Old Old Old > > [859] Old Old Old Old Old Old Mid Mid Old Old Old > > [870] Old Old > > Levels: Mid New Old > >
The fact that MagNew has such a large coefficient and large SE suggests that your model exhibits what some refer to as "complete separation" or "quasi-complete separation" in the data and there is no maximum likelihood estimate for the coefficient. What does a cross-tabulation of Mag with your DV look like? You might want to read up on quasi-complete separation and suggestions for dealing with that. Hope this is helpful, Dan Daniel Nordlund Bothell, WA USA ______________________________________________ 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.
