I have trouble with interpreting the output of a general linear model, which I have fitted to some data. Here are my data: 

```
     Gall  Site healthy unhealthy
1  absent Site1    1750        35
2  absent Site2    1642       146
3 present Site1    1146        23
4 present Site2     333        30

```

I have made a barplot to visualize this better:

[![My barplot](https://i.stack.imgur.com/ADRkk.png)](https://i.stack.imgur.com/ADRkk.png)

I want to test whether healthy or unhealthy (saplings) was induced by the galls being present or absent. The saplings were randomly collected in each site. Therefore, I fit a generalized linear model to these data: 

```
abc <- glm(cbind(healthy,unhealthy)~Site*Gall, family="binomial", data=f1)

```

I summarize my model

```
summary(abc)

Call:
glm(formula = cbind(healthy, unhealthy) ~ Site * Gall, family = "binomial", 
    data = f1)

Deviance Residuals: 
[1]  0  0  0  0

Coefficients:
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)            3.912023   0.170713  22.916  < 2e-16 ***
SiteSite2             -1.491959   0.191314  -7.798 6.27e-15 ***
Gallpresent           -0.003484   0.271097  -0.013    0.990    
SiteSite2:Gallpresent -0.009634   0.342474  -0.028    0.978    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1.1069e+02  on 3  degrees of freedom
Residual deviance: 4.5963e-14  on 0  degrees of freedom
AIC: 30.235

Number of Fisher Scoring iterations: 3

```

Now I try to simplify the model:

```
drop1(abc, test="Chi")

Single term deletions

Model:
cbind(healthy, unhealthy) ~ Site * Gall
          Df   Deviance    AIC        LRT Pr(>Chi)
<none>       0.00000000 30.235                    
Site:Gall  1 0.00079137 28.236 0.00079137   0.9776

```

I omit the two-way interaction between `Site` and `Gall`:

```
> f1.rid1 <- update(abc, .~. - Site:Gall)

```

I test each 1-way term, each at a time:

```
> drop1(f1.rid1, test="Chi")
Single term deletions

Model:
cbind(healthy, unhealthy) ~ Site + Gall
       Df Deviance     AIC     LRT Pr(>Chi)    
<none>       0.001  28.236                     
Site    1  104.027 130.262 104.027   <2e-16 ***
Gall    1    0.004  26.239   0.003   0.9542    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

```

The model tells me to omit `Gall`, so my final model is

```
> f1.rid2 <- update(f1.rid1, .~., - Gall)
> summary(f1.rid2)

Call:
glm(formula = cbind(healthy, unhealthy) ~ Site + Gall, family = "binomial", 
    data = f1)

Deviance Residuals: 
        1          2          3          4  
-0.014033   0.007093   0.017279  -0.015671  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  3.914420   0.148106  26.430   <2e-16 ***
SiteSite2   -1.494968   0.158755  -9.417   <2e-16 ***
Gallpresent -0.009518   0.165722  -0.057    0.954    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1.1069e+02  on 3  degrees of freedom
Residual deviance: 7.9137e-04  on 1  degrees of freedom
AIC: 28.236

Number of Fisher Scoring iterations: 3

```

**My question is:** 

Why am I dropping Gall in my model? I know the output tells me to do so, but by looking at the initial barplot, I figured the presence or absence of Gall must have a significant effect on healthy or unhealthy.