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.ens...@nt.gov.au
R version 2.8.1
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