Dear All, This question is regarding model.matrix function and the contrasts which can be made after applying it. I used this function as a part of edgeR package. Here are 2 designs: > design_1=model.matrix(~0+ Control+ Gender+ Location, data=data_2) > colnames(design_1) [1] "Control0" "Control1" "Gender1" "Location1" How could I get the contrast Gender1-Gender0, shouldn't it be included in the columns since there is no intercept? If I want to see the contrast (Gender1-Gender0), I could change the order of the factors in the formula: > design_2=model.matrix(~0+ Gender+ Control+ Location, data=data_2) > colnames(design_2) [1] "Gender0" "Gender1" "Control1" "Location1" But then there is a question: is there any mathematical difference between 2 designs? If someone knows a link/book where the function model.matrix is well and in details explained, please let me know. Thank you very much in advance! Mike [[alternative HTML version deleted]]

Dear Mike, > Mike Miller mike.bioc32 at gmail.com > Thu Mar 6 12:27:01 CET 2014 > > Dear All, > > This question is regarding model.matrix function and the contrasts which > can be made after applying it. I used this function as a part of edgeR > package. > > Here are 2 designs: > > > design_1=model.matrix(~0+ Control+ Gender+ Location, data=data_2) > > colnames(design_1) > [1] "Control0" "Control1" "Gender1" "Location1" > > How could I get the contrast Gender1-Gender0, shouldn't it be included > in the columns since there is no intercept? It is included. It is called "Gender1". By default, model.matrix() produces contrasts relative to the first level of each factor. > If I want to see the contrast (Gender1-Gender0), I could change the > order > of the factors in the formula: > > design_2=model.matrix(~0+ Gender+ Control+ Location, data=data_2) > > colnames(design_2) > [1] "Gender0" "Gender1" "Control1" "Location1" > > But then there is a question: is there any mathematical difference > between 2 designs? Yes, there is. Now "Control1" represents Control1-Control0 but "Gender1" is just Gender1. I would suggest that you only use "0+" for oneway layouts, not for additive models with multiple factors. > If someone knows a link/book where the function model.matrix is well and > in details explained, please let me know. The main document perhaps is Section 11.1 of the Introduction to R manual that comes with R. But I doubt you will find that fully helpful. You can also try asking questions on the R-help mailing list. But really, there are two main things you need to understand to follow design matrices reasonably well. First, each factor that you add to the linear model adds one fewer column than the factor has levels. You start with an intercept. Adding Control adds one further column (because Control has two levels). Adding Gender adds two columns (because Gender has three levels). Adding Location adds 1 column (because Location has two levels). That's four columns in total. No matter how you parametrize you must have exactly 4 columns. You can try fiddling the model by using "0+", in that case the first level of the first factor enters in place of the intercept. But you can't expect the first level of any other factor to appear because that would make more than 4 columns. Second, model.matrix() compares each level back to the first level of each factor. So simply using ~Control+Gender+Location will gives you coefficients representing Control1-Control0, Gender1-Gender0, Gender2-Gender0 and Location1-Location0. That's not too difficult! Best wishes Gordon > Thank you very much in advance! > Mike ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}