Hello
I'm currently doing 30 samples of RNAseq anaylsis with DESeq, but now I have problem with model matrix again.
I posted my sample colData below.

donor disease batch donor.n
A Control B1 1
A Control B2 1
A Control B3 1
B Control B1 2
B Control B2 2
B Control B3 2
C Control B1 3
C Control B2 3
C Control B3 3
D Control B1 4
D Control B2 4
D Control B3 4
E muta B1 1
E muta B2 1
E muta B3 1
F muta B1 2
F muta B2 2
F muta B3 2
G muta B1 3
G muta B2 3
G muta B3 3
H mutb B1 1
H mutb B2 1
H mutb B3 1
I mutb B1 2
I mutb B2 2
I mutb B3 2
J mutb B1 3
J mutb B2 3
J mutb B3 3

I want to find disease samples' transcriptomic change without batch variation or donor variation, so I added donor.n to distinguish donor's effect.
Problem was that I had 4 donors samples in control group so I have to remove levels without samples as vignettes of DESeq says.
I tried with this code to make matrix model just like vignettes says.

m1<-model.matrix(~ disease+ disease:donor.n+ disease:batch, samples)
colnames(m1)
unname(m1)
all.zero <- apply(m1, 2, function(x) all(x==0))
all.zero
idx <- which(all.zero)
m1 <- m1[,-idx]
unname(m1)

It worked with DESeqDataSetFromTximport(txi, colData = samples, design = m1)
but when I tired to make the results, I couldn't find disease_muta_vs_Control nor disease_mutb_vs_Control in resultNames().
This is the results of the resultsNames()

resultsNames(dds)
 [1] "Intercept"                "diseasemuta"           "diseaseControl"         "diseasemutb.donor.n2"    
 [5] "diseasemuta.donor.n2"   "diseaseControl.donor.n2" "diseasemutb.donor.n3"     "diseasemuta.donor.n3"  
 [9] "diseaseControl.donor.n3" "conditionControl.donor.n4" "diseasemutb.batchB2"     "diseasemuta.batchB2"  
[13] "diseaseControl.batchB2" "diseasemutb.batchB3"     "diseasemuta.batchB3"   "diseaseControl.batchB3"

I can make results with res <- results(dds, contrast=list("diseasemuta","diseaseControl")) to get muta versus Control but I cannot compare mutb versus Control.
Can anyone explain what I missed and how to make model matix of this situation?
Always thanks to everyone's kind response.

You'll need to set Control to the reference level of disease, then you will have diseasemutb in the names of the model matrix, which is the disease_mutb_vs_Control term (DESeq2 when you provide design as a formula renames these coefficients to make them more human readable than model.matrix, but these are the same terms).