When computing regression models with R, I regularly use the relevel function to get my model to give me results for the other level, too. I noticed that sometimes, but not often, this changed the model in the sense that levels of other factors that were significant before the relevelling are not any more. Is this inherent to relevelling or exceptional and maybe due to some problem with my data? Does it show that my data likely does not meet one of the prerequisites of linear models?
Related to that, is it alright if I use relevel, recompute my model, and then report significance values from both models in my article? If significance differs between the two models for a certain factor, I suppose I should then go with one that is less optimistic?
I suppose my question betrays that I don't know enough about lm to grasp the need for a base level. I thought I understood it pretty well ;) Somehow none of the introductions I read explained that point, or I was too daft to grasp it. So if someone could direct me to a site where the point of having base levels in lm is explained or explain it themselves, that would be great, too!
Edit: Here's a minimal example:
library(datasets) sprays<-OrchardSprays model<-lm(decrease~treatment+rowpos+colpos,data=sprays) summary(model)
Part of the summary says
treatmentC 20.625 9.731 2.120 0.03866 *
So if treatment == C this has significant positive influence on 'decrease'. Now I relevel 'treatment' to B to find out what influence treatment == A has:
And now treatment == C is not significant in this new model:
treatmentC 17.625 9.731 1.811 0.07567 .
Sorry for posting in the wrong place! Can I move my question to stats statexchange or should I open a new one there?