[R] What is se.fit in a glm predict list?

[Patrick Connolly]

I'm trying  to calculate a CI for predictions from a Poisson GLM object egg.glm.

Browse[2]> aa <- as.data.frame(predict(egg.glm, newdat, type = "response", se.fit = TRUE)[-3])
Browse[2]> bb <- as.data.frame(predict(egg.glm, newdat, se.fit = TRUE)[-3])
Browse[2]> aa
           fit       se.fit
1 6.144212e-07 0.0005114257
2 2.452632e+01 5.4657657443
3 1.440000e+01 2.5817126393
4 4.389796e+01 4.5533997800
5 3.820455e+01 4.4827326393
6 6.226667e+01 5.6589154967
Browse[2]> bb
         fit       se.fit
1 -14.302585 832.36979026
2   3.199747   0.22285311
3   2.667228   0.17928560
4   3.781868   0.10372691
5   3.642954   0.11733506
6   4.131426   0.09088194
Browse[2]> 

bb$fit is clearly log of aa$fit but just what is se.fit?  How do I use
it to get a CI which is calculated on the log scale?  The first one is
a bit messy since it is entirely from zeros.  Should I remove those or
would that be unnecessary?

TIA

-- 
~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.   
   ___    Patrick Connolly   
 {~._.~}                   Great minds discuss ideas    
 _( Y )_  	         Average minds discuss events 
(:_~*~_:)                  Small minds discuss people  
 (_)-(_)  	                      ..... Eleanor Roosevelt
	  
~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.