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Great thanks. Is there a way to specify the contrast for Grazing?

Great thanks. Is there a way to specify the contrast for Grazing?

I have computed GLMM using glmer in R. My response variable is species richness and my explanatory variable is grazing treatment (with three categories: cattle, sheep and ungrazed). In the model I have included site as a fixed variable and also a new object with the same number of variations as I have to attempt to account for underdispersal (obs):

model2<-glmer(VegRichness~Grazing+(1|Site)+(1|obs),family="poisson",data=veg.rich)

How do I interpret the fixed effects section? Cattle grazing seems to be missing in the oputput, is this because it is somehow incorporated into the intercept?

Thanks in advance, Ashley

summary(model2)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) [glmerMod]

Family: poisson ( log )

Formula: VegRichness ~ Grazing + (1 | Site) + (1 | obs) Data: veg.rich

 AIC      BIC   logLik deviance df.resid 
178.8    185.2    -84.4    168.8       22 

Scaled residuals:

Min.......... 1Q............ Median.... 3Q......... Max

-1.4936... -0.5698..... -0.1928... 0.4923... 1.3646

Random effects:

Groups ... Name...... Variance..... Std.Dev.

obs......... (Intercept).. 0.00000.... 0.0000

Site......... (Intercept).. 0.03596.... 0.1896

Number of obs: 27, groups: obs, 27; Site, 3

Fixed effects: .......Estimate.... Std. Error..... z value... Pr(>|z|)
(Intercept)............ 3.55358....... 0.12309....... 28.869..... < 2e-16 ***
GrazingSheep...... 0.01242...... 0.07876........ 0.158....... 0.87467
GrazingUngrazed -0.27526..... 0.08503........ -3.237...... 0.00121 **

Signif. codes: 0 xx 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects: (Intr)....GrzngS

GrazingShe......................... -0.322
GrzngUngrzd...................... -0.298... 0.466

I have computed GLMM using glmer in R. My response variable is species richness and my explanatory variable is grazing treatment (with three categories: cattle, sheep and ungrazed). In the model I have included site as a fixed variable and also a new object with the same number of variations as I have to attempt to account for underdispersal (obs):

model2<-glmer(VegRichness~Grazing+(1|Site)+(1|obs),family="poisson",data=veg.rich)

How do I interpret the fixed effects section? Cattle grazing seems to be missing in the oputput, is this because it is somehow incorporated into the intercept?

> summary(model2)

Generalized linear mixed model fit by maximum likelihood (Laplace
  Approximation) [glmerMod]
 
Family: poisson  ( log ) 

Formula: VegRichness ~ Grazing + (1 | Site) + (1 | obs)
   Data: veg.rich


     AIC      BIC   logLik deviance df.resid 
    178.8    185.2    -84.4    168.8       22 

Scaled residuals: 
    
Min..........           1Q............           Median....       3Q.........        Max
 
-1.4936...      -0.5698.....       -0.1928...      0.4923...   1.3646 

Random effects:

 Groups  ... Name......        Variance..... Std.Dev.

 obs.........      (Intercept).. 0.00000....  0.0000 
 
 Site.........     (Intercept).. 0.03596....  0.1896
  
Number of obs: 27, groups:  obs, 27; Site, 3

Fixed effects:
                .......Estimate.... Std. Error..... z value... Pr(>|z|)    
(Intercept)............      3.55358.......    0.12309.......  28.869.....  < 2e-16 ***                                                                 
GrazingSheep......     0.01242......    0.07876........   0.158.......  0.87467    
GrazingUngrazed -0.27526.....    0.08503........  -3.237......  0.00121 ** 

---
Signif. codes:  0 x***x 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr)....GrzngS

GrazingShe......................... -0.322       
GrzngUngrzd...................... -0.298...  0.466