[R] Relevel() catagorical variables in a GLM

[ashley]

Hello list readers, 

I am running a set of GLMs on fish spp presence/absence as a function of
various habitat characteristics. My response is binomial and I have four
predictors, three of which are categorical.

So, R takes one of my predictor-variables away to use as the intercept (the
first one alphabetically). However, I want to know the coefficient and SE of
this predictor. I tried relevel() and reran the model. Abbreviated summary()
results for each run are below. The results seem drastically different. Have
I done the wrong thing?

(Below is a result from the model with only one predictor, to save space and
hassle.)

Thanks, 
Ashley

#Default reference level = HH:

                                 Estimate Std. Error z value Pr(>|z|)    
(Intercept)                     -5.2671     0.2781 -18.942   <2e-16 ***
raw.table$SubsComboHS    0.8127     0.6438   1.262    0.207    
raw.table$SubsComboSH  -0.5736     1.0393  -0.552    0.581    
raw.table$SubsComboSS -18.2990   923.6023  -0.020    0.984  

#Command used to change reference level:
> raw.table$SubsCombo<-relevel(raw.table$SubsCombo, ref="SS")

#New reference level = SS:

                                   Estimate Std. Error z value Pr(>|z|)
(Intercept)                     -23.57     923.60  -0.026    0.980
raw.table$SubsComboHH    18.30     923.60   0.020    0.984
raw.table$SubsComboHS    19.11     923.60   0.021    0.983
raw.table$SubsComboSH    17.73     923.60   0.019    0.985



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