[R] 95% confidence intercal with glm

[Sam]

I am looking to do the same but am a bit confused 

> and apply the inverse link function for your model.

i understand up to this point and i understand what this means, however i am unsure why it needs to be done and how you do it - i.e i use family="binomial" is this wrong if i use this method to calculate 95% CI?

Thanks

Sam
On 28 Sep 2010, at 14:50, Ben Bolker wrote:

zozio32 <remy.pascal <at> gmail.com> writes:

> 
> 
> Hi
> 
> I had to use a glm instead of my basic lm on some data due to unconstant
> variance.
> 
> now, when I plot the model over the data, how can I easily get the 95%
> confidence interval that sormally coming from:
> 
>> yv <- predict(modelVar,list(aveLength=xv),int="c")
>> matlines(xv,yv,lty=c(1,2,2))
> 
> There is no "interval" argument to pass to the predict function when using a
> glm, so I was wondering if I had to use an other function
> 

 You need to use predict with se=TRUE; construct the confidence
intervals by computing predicted values +- 1.96 times the standard
error returned; and apply the inverse link function for your model.

 If heteroscedasticity is your main problem, and not a specific
(known) non-normal distribution, you might consider using the gls
function from the nlme package with an appropriate 'weights' argument.

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