interactions in fixed effects

Hello

I am working whit GLMM (binomial family) in lme4. In order to test
differences between amphibian parasitic infections in two study sites, I am
using a model consisting of two fixed effects ("sa" and "sp" ) and a random
effects ("sdy"). After testing the significance of the random effect, I
chose to use GLM  based on the same error structure and variables. My
problem is: when I try to explore the interaction between two fixed
effects, anova () does not show the values of Chi and the probability value.

I am sorry for my question, maybe this is a bit basic. But I could not get
these results.

Thanks!

 names(data)
[1] "sa"  "sdy"  "sp"  "inf"  "noinf"

### sa(study area)
### ps(species)
### sdy(sample day)
### inf(individual infected)
### noinf(individual non infected)

class(data$sa)

[1] "factor"

levels(data$sa)

[1] "cult" "ref"

class(data$sp)

[1] "factor"

levels(data$sp)

[1] "hpa" "lla" "llj" "rfa"

library(lme4)

data$Ymat<-cbind(data$inf,data$noinf-data$inf)
GLM0<-glm(Ymat~sa+sp+sa*sp,data=data,family=binomial)
summary(GLM0)

Call:
glm(formula = Ymat ~ sa + sp + sa * sp, family = binomial, data = data)

Deviance Residuals:
    Min       1Q   Median       3Q      Max
-2.2910  -0.8011   0.0000   0.6543   2.5593

Coefficients:
            Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.18122    0.19937 -10.941  < 2e-16 ***
saref       -1.43460    0.36483  -3.932 8.42e-05 ***
splla        1.82021    0.33694   5.402 6.58e-08 ***
spllj        2.09878    0.20676  10.151  < 2e-16 ***
sprfa        2.10572    0.24216   8.696  < 2e-16 ***
saref:splla -0.08470    0.57874  -0.146    0.884
saref:spllj -0.01143    0.37498  -0.030    0.976
saref:sprfa -0.47644    0.43437  -1.097    0.273
---
Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1013.00  on 363  degrees of freedom
Residual deviance:  355.21  on 356  degrees of freedom
AIC: 935.48

Number of Fisher Scoring iterations: 5

GLM1<-glm(Ymat~sa+sp,data=data,family=binomial)
anova(GLM0,GLM1)

Analysis of Deviance Table

Model 1: Ymat ~ sa + sp
Model 2: Ymat ~ sa + sp + sa * sp
  Resid. Df Resid. Dev Df Deviance
1       359     358.80
2       356     355.21  3   3.5908


Thanks!!!

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