[R] Std.Error in Analysis of Deviance Table

[Alexander Jüterbock]

Hello,
I would be pleased if you could help me in determining the standard error of the estimate for “place2L1.N2” from the following table:


Call:
glm(formula = y ~ places2 + shellheight, family = quasibinomial)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-3.41415  -0.70817   0.01214   0.79700   3.18387  

Coefficients:
                        Estimate    Std. Error  t value Pr(>|t|)    
(Intercept)      -1.60286    0.22383  -7.161  8.92e-12 ***
places2L1.N2   0.31922    0.08095   3.944  0.000104 ***
shellheight       0.08742    0.01135   7.703  3.12e-13 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for quasibinomial family taken to be 1.354491)

    Null deviance: 441.61  on 252  degrees of freedom
Residual deviance: 355.52  on 250  degrees of freedom
AIC: NA

Number of Fisher Scoring iterations: 3


I know that the mean value is -1.60286+0.31922 and that the Std. Error (0.08095) should be the standard error for the difference between “(Intercept)” and “places2L1.N2” (i.e. 0.31922). 
But when I take this Std. Error as
Sqrt((sA2/nA)+(sB2/nB)) 
With 
sqrt(sA2/nA)
 being the Std. Error for the intercept (i.e. 0.22383), then
sqrt (sB2/nB))
should be the Std. Error for places2L1.N2. 
But this calculation isn’t right. I don’t get the right Std. Error for the (Intercept) and therefore also not for places2L1.N2. What’s wrong in this calculation? Thank you for your help,
Alexander.



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