Dear R list,
I have the following data:
set.seed(1)
n <- 5 # number of subjects
tt <- 3 # number of repeated observation per subject
numco <- 2 # number of covariates
x <- matrix(round(rnorm(n*numco),2), ncol=numco) # the actual covariates
x
> x
[,1] [,2]
[1,] -0.63 -0.82
[2,] 0.18 0.49
[3,] -0.84 0.74
[4,] 1.60 0.58
[5,] 0.33 -0.31
I need to form a matrix X such that
X = x11 0 0 x21 0 0
0 x11 0 0 x21 0
0 0 x11 0 0 x21
x12 0 0 x22 0 0
0 x12 0 0 x22 0
0 0 x12 0 0 x22
...
x15 0 0 x25 0 0
0 x15 0 0 x25 0
0 0 x15 0 0 x25
where both tt and numco can change. (So if tt=5 and numco=4, then X
needs to have 20 columns and n*tt rows. Each diagonal matrix should be
5x5 and there will be 4 of them for the 4 covariates.) I wrote this
funky for loop:
idd <- length(diag(1,tt)) # length of intercept matrix
X <- matrix(numeric(n*numco*idd),ncol=tt*numco)
for(i in 1:numco){
X[,((i-1)*tt+1):(i*tt)] <- matrix(
c(matrix(rep(diag(1,tt),n),ncol=tt, byrow=TRUE)) *
rep(rep(x[,i],each=tt),tt)
, ncol=tt)
}
X
It works fine, but is there an easier way when n, tt, and numco get
larger and larger?
Thanks,
Tina
--
Clemontina Alexander
Ph.D Student
Department of Statistics
NC State University
Email: ckale...@ncsu.com
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