[R] Singularities in glm()

[voodooochild at gmx.de]

Hello,

i have the following model,

poi1<-glm(F~S+T+L+C,family=poisson,x=T)

where F,S,T,L are metric and C is a factor variable with the levels "0", 
"1", "2", "3", "4", "5" and "6"

if i do summary(poi1), i get the following

Call:
glm(formula = F ~ S + T + L + C, family = poisson, x = T)

Deviance Residuals: 

     Min        1Q    Median        3Q       Max  

-2.44054  -0.80997  -0.04627   0.69402   2.90301  

Coefficients: (1 not defined because of singularities)
              Estimate Std. Error z value Pr(>|z|)    

(Intercept)  -10.45553    1.16221  -8.996  < 2e-16 ***

S              1.08024    0.13609   7.938 2.06e-15 ***

T              1.63582    0.05170  31.643  < 2e-16 ***

L              3.31684    0.49965   6.638 3.17e-11 ***

C1             0.21256    0.16449   1.292   0.1963    

C2            -0.10895    0.06675  -1.632   0.1027    

C3             0.15159    0.06992   2.168   0.0302 *  

C4             0.50949    0.05870   8.680  < 2e-16 ***

C5             0.11240    0.01686   6.667 2.61e-11 ***

C6                  NA         NA      NA       NA    

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 47365.29  on 495  degrees of freedom

Residual deviance:   574.64  on 487  degrees of freedom

AIC: 4091.8

Number of Fisher Scoring iterations: 4


my question now is why do i get those "singularities" and where do they 
come from, i guess because of the singularites i get the NA's, what can 
i do here to avoid them?
I think i have done some wrong dummy coding or something like that? By 
the way, i have 7 levels and only got estimates for 6 levels, whats 
wrong here?

best regards
andreas