Dear Jim, Thanks for your explanation. My confusion is over the fact that both regression and ANOVA are called by the same function. I guess I don't need the intercept as Gordon suggested for the factor co-variates, but I thought I needed an intercept for a continuous co-variate. My original message indicated that the intercept can be calculated by loosing one of the samples when using group (factor) data. If I use only a continuous covariate, like age, I can get an intercept and still keep the age co-variate. The trouble arises when I add a factor (like group) and a continuous co-variate (like age). In this case, I can get an intercept, but I loose a factor. I am not intending to do anything with the intercept. For this reason, I am taking Gordon's advice and not calculating an intercept. Thanks all. Ramsi > Ramsi Haddad wrote: > > Dear List, > > > > I have been working with LIMMA and I'm a bit confused by the linear > > models. I have a group with 4 factors. I want to remove covariates > > from the main effects before I look at contrasts. Before I do this, I > > was trying out the different combinations of ~ . When I say > > lmFit(~group), this is supposed to calculate an intercept. This works. > > The problem is that my groupCTL disappears. If I say lmFit(~ -1 + > > group), I gather that the intercept is constrained to (0,0) and that > > lmFit(~ 0 + group) does not calculate an intercept. > > My problem is that I want lmFit to give me an intercept and not take > > away my groupCTL! Below is the code showing what I mean. Everything > > works, its just that I want my contrast matrix to include > > groupPE-groupCTL, but I can't do this when the intercept is calculated. > > You are misunderstanding the models you are fitting. In the first place, > with ANOVA there is no assumption of a linear relationship between the > factor levels, so removing the intercept term doesn't constrain the > intercept to (0, 0). In this case, the intercept term indicates what > sort of model you want to fit, either a cell means or factor effects model. > > Without an intercept you are fitting a cell means model in which you are > estimating the mean expression for each factor level (e.g., the model is > y_ij = u_i + e_ij). In this case, doing the contrasts is quite > straightforward. > > If you add an intercept term, you are fitting a factor effects model in > which all of the other factors are specified in relation to some mean > value. In this case, all the other factors are specified in relation to > the mean of the groupCTL (e.g., the model is y_ij = u. + t_i + e_ij). > Here u. is the mean of the groupCTL samples, and the t_i are the amounts > that each of the other group means differ from the groupCTL mean. > Therefore, the contrasts are specified by the t_i values themselves if > you are comparing to groupCTL, and are specified by e.g., groupPE - > groupTIL for the other contrasts. > > HTH, > > Jim > > > > > > Any assistance in clarifying this matter would be appreciated. Thanks > > > > Ramsi > > > > > > > >>table(group) > > > > group > > CTL PE TIL TNL > > 17 22 12 10 > > > > > >>design.e <- model.matrix(~group) > > > > > >>colnames(design.e) > > > > [1] "(Intercept)" "groupPE" "groupTIL" "groupTNL" > > > > > >>design.e <- model.matrix(~-1 + group) > > > > > >>colnames(design.e) > > > > [1] "groupCTL" "groupPE" "groupTIL" "groupTNL" > > > > > >>design.e <- model.matrix(~0 + group) > > > > > >>colnames(design.e) > > > > [1] "groupCTL" "groupPE" "groupTIL" "groupTNL" > > > > _______________________________________________ > > Bioconductor mailing list > > Bioconductor@stat.math.ethz.ch > > https://stat.ethz.ch/mailman/listinfo/bioconductor >