Hi all,

I was wondering if there is a function out there, or someone has written code for making confidence intervals around model averaged predictions (y~á+âx). The model average estimates are from the dRedging library?

It seems a common thing but I can't seem to find one via the search engines

Examples of the models are:

fit1 <- glm(y~ dbh, family = binomial, data = data)

fit2 <- glm(y~ dbh+vegperc, family = binomial, data = data)

fit3 <- glm(y~ dbh, family = binomial, data = data)

and the model averaging

model.averaging <-model.avg(fit1,fit2,fit3, method="0")

 and the output (from model.avg) has the following items: Coefficient, Variance, Standard error, adjusted standard error and lower and upper confidence interval for each parameter (and intercept).

What I would like to do is make "prediction intervals". I know I need to include covariance and variance. Please let me know if anyone has a function or code to get these prediction intervals out of this output.

Thanks in advance for your help, and please advise me if you need more information

M
michelle.ensbey@nt.gov.au

R version 2.8.1

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