I am using Limma for an experimental design with three categories, two biological replicates each and two technical replicates of each biological replicate for a total of 12 arrays.   Since this goes just a little bit beyond the Limma user guide, I want to make sure I am setting this up correctly.  Also, figure out why I am getting the error message below.

The categories are:
Cyt1
Cyt2
Mock (which is control for both of the other groups)

The biological replicates are indicated with A and B.  The technical replicates are indicated with .1 and .2.  

The variable expr has the array data and the sample columns are:
MockA.1 MockA.2 Cyt1A.1 Cyt1A.2 Cyt2A.1 Cyt2A.2 MockB.1 MockB.2 Cyt1B.1 Cyt1B.2 Cyt2B.1 Cyt2B.2

> treatment
 [1] MockA MockA Cyt1A Cyt1A Cyt2A Cyt2A MockB MockB Cyt1B Cyt1B Cyt2B Cyt2B
Levels: Cyt1A Cyt1B Cyt2A Cyt2B MockA MockB
> design = model.matrix(~ 0 + treatment)
> design
   Cyt1A Cyt1B Cyt2A Cyt2B MockA MockB
1      0     0     0     0     1     0
2      0     0     0     0     1     0
3      1     0     0     0     0     0
4      1     0     0     0     0     0
5      0     0     1     0     0     0
6      0     0     1     0     0     0
7      0     0     0     0     0     1
8      0     0     0     0     0     1
9      0     1     0     0     0     0
10     0     1     0     0     0     0
11     0     0     0     1     0     0
12     0     0     0     1     0     0
attr(,"assign")
[1] 1 1 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$treatment
[1] "contr.treatment"
> block
 [1] "MockA" "MockA" "Cyt1A" "Cyt1A" "Cyt2A" "Cyt2A" "MockB" "MockB" "Cyt1B" "Cyt1B" "Cyt2B" "Cyt2B"
> dupcor = duplicateCorrelation(expr, design, block=block)
> fit.raw = lmFit(expr, design, block=block,
+             correlation=dupcor$consensus)
Error in chol.default(V) : 
  the leading minor of order 2 is not positive definite
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