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Dear R users: I'm new to R and am trying to fit a mixed model Cox regression model with coxme function. I have one two-level factor (treat) and one covariate (covar) and 32 different groups (centers). I'd like to fit a random coefficients model, with treat and covar as fixed factors and a random intercept, random treat effect and random covar slope per center. I haver a couple of

Dear all, I am using "coxme" function in Kinship library to fit random treatment effect nested within centre. I got 3 treatments (0,1,2) and 3 centres. I used following commands, but got an error. > ugroup=paste(rep(1:3,each=3),rep(0:2,3),sep='/') > mat1=bdsmatrix(rep(c(1,1,1,1,1,1,1,1,1),3),blocksize=rep(3,3),dimnames=list(ugroup,ugroup)) >

The result of rowsum() in R doesn't have the dimnames I'd expect, e.g.: > rowsum(matrix(1:12, 3,4), c("Y","X","Y")) [,1] [,2] [,3] [,4] 1 2 5 8 11 2 4 10 16 22 whereas S-Plus gives the more useful result: [,1] [,2] [,3] [,4] X 2 5 8 11 Y 4 10 16 22 This is because R's rowsum() code gives the

Hi, The result of rowsum() in R doesn't have the dimnames I'd expect, e.g.: > rowsum(matrix(1:12, 3,4), c("Y","X","Y")) [,1] [,2] [,3] [,4] 1 2 5 8 11 2 4 10 16 22 whereas S-Plus gives the more useful result: [,1] [,2] [,3] [,4] X 2 5 8 11 Y 4 10 16 22 This is because R's rowsum() code gives

Is there a better or more efficent approach than this without the use of t() ? > (m <- matrix(1:40, ncol=4)) [,1] [,2] [,3] [,4] [1,] 1 11 21 31 [2,] 2 12 22 32 [3,] 3 13 23 33 [4,] 4 14 24 34 [5,] 5 15 25 35 [6,] 6 16 26 36 [7,] 7 17 27 37 [8,] 8 18 28 38 [9,] 9 19 29 39[10,] 10 20 30 40 >