[R] Model parameterization / Factor Levels

[Peyuco Porras Porras .]

Dear R users; 

I'm looking for some hint about how to deal with the following situation: 

Response = Y 
Factor A = levels: 0, 1 
Factor B = levels: 0, 1 
Factor C = levels: 1,2,3,4 

Model: Logistic 3-parms. 
where th1~1+A+C, th2~1+C; th3~1 

For 'simplicity' (for me) I'm using the SAS contrast parameterization. 

The output looks like 

Beta p-value 
th1.(Intercept) 550 <0.000 
th1.A1 -15 <0.000 
th1.B1 5 <0.032 
th1.C1 -12 <0.001 
th1.C2 -5 0.022 
th1.C3 -3 0.222 
th2.(Intercept) ...... 

......etc 

if we look at the results, we may conclude that level 3 for Factor C is not 
statiscally significant. The question is: How can I remove this level of this factor 
from the analysis? Let's say that the final results looks like 

Model: Logistic 3-parms. 
where th1~1+A+C, th2~1+C; th3~1, but C with levels 1,2 and 4 only 

Beta p-value 
th1.(Intercept) 560 <0.000 
th1.A1 -15 <0.000 
th1.B1 5 <0.032 
th1.C1 -15 <0.001 
th1.C2 -8 0.031 
th2.(Intercept) ...... 

......etc 


I tried replacing Factor C by 4 different columns, say FACTORC_1, FACTOR_C2, 
FACTOR_C3, and FACTOR_C4 each one of them with 0 or 1, and the model I tried was 

f1<-nlme(Y~SSlogis(X,th1,th2,th3)|Subject,fixed=list(th1~A+B+FACTORC_1+FACTOR_C2, etc 

but, as I expected, the model can't be solved 

I will appreciate any help