# Interpreting Logistic Regression In R

I have an experiment where I have 4 treatments and 2 outcomes (1 or 0) possible for each treatment. This is the result I calculated in R but I don't understand why only 3 of my treatments come up in the summary. How do I interpret this? The 4 treatments relate to nest sites that may either be a control, no egg, quail egg or artificial egg.

> fm <- glm(Disturbed~Trial+Treatment, data= data, family = binomial)
> summary(fm)
Call:
glm(formula = Disturbed ~ Treatment, family = binomial, data = data)

Deviance Residuals:
Min       1Q   Median       3Q      Max
-1.4823  -0.6681  -0.3715   0.9005   2.3272

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept)        0.6931     0.5477   1.266  0.20569
TreatmentControl  -3.3322     1.1711  -2.845  0.00444 **

TreatmentNo egg   -2.0794     0.8466  -2.456  0.01404 *
TreatmentQuail    -0.8267     0.7536  -1.097  0.27263
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 77.694  on 59  degrees of freedom
Residual deviance: 62.183  on 56  degrees of freedom
AIC: 70.183

Number of Fisher Scoring iterations: 5


Why is there no Artificial egg treatment?