Correct SE in a poisson model.

Hi all,

I'm here again with my newbies questions :(

I have a simple example:

count of slugs in two fields.

I need to make a barplot with mean and SE of mean.

So I have:

The mean:

tapply(slugs,field,mean)

Nursery Rookery 
  1.275   2.275 

The SE:

tapply(slugs,field,sd)/sqrt(tapply(slugs,field,length))

Nursery   Rookery 
0.3651264 0.3508004 

If the data has been normally distributed it is correct, but it is 
overdipersed count data.

I make a model

m.poisson <- glm(slugs~field,family=quasipoisson)

And I have these coefficients:

Coefficients:
             Estimate Std. Error t value Pr(>|t|)  
(Intercept)    0.2429     0.2490   0.976   0.3323  
fieldRookery   0.5790     0.3112   1.861   0.0666 .

The estimate mean = mean
1.275 = exp(0.2429)
2.275 = exp(0.2429+0.5790)

But and the correct standard error of mean? How to obtain this? Exist any 
function for calculate this? Exist another better measure than SE for 
non-normal errors (poisson, quasi, binomial, gamma etc)?


Thanks
Ronaldo

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