[R] prediction intervals (alpha and beta) for model average estimates from binomial glm and model.avg (library=dRedging)

[David Winsemius]

On Apr 27, 2009, at 3:19 AM, Michelle Ensbey wrote:

> Thanks for your swift reply
>
> I'm sorry to say that I tried that, and it doesn't appear to work  
> for predicting from the "model.avg" object (ouput). Model.avg is a  
> model averaging function in dRedging. I am NOT trying to predict  
> from the coefficients estimated directly from the glm.
>
> For :
>> 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")
>
> ##Then when trying to predict you get the error below I understand  
> it is because it is not a glm (or other compatable object) but I  
> thought maybe someone had come across and solved this problem  
> already so I thought I'd check:
>
>> predict(model.averaging)
> OR
>> predict(model.averaging,"Patch_Num")
> Error in UseMethod("predict") : no applicable method for "predict"
> ##Comes up.
> Does anyone have a function or code or has done this (for  
> coefficients obtained from the model.avg function) in the past and  
> can give advice.

It will need to be someone other than me. I thought to look at an  
object created by that function to see if there were a path to  
success, but there is no dRedging package in either the CMU CRAN  
mirror or the BioC repository.

It makes me wonder whether the authors of that package may have  
decided for valid reasons not to provide such a facility. The question  
of how to concoct standard errors for the lme4 package comes up here  
on a regular basis and the answer is that the process is not at all  
straightforward.

Best of luck;
David
>
>
> Thanks again for your help, let me know if I've just missed something.
> Cheers
> M
>
>
> -----Original Message-----
> From: David Winsemius [mailto:dwinsemius at comcast.net]
> Sent: Friday, 24 April 2009 10:24 PM
> To: Michelle Ensbey
> Cc: r-help at r-project.org
> Subject: Re: [R] prediction intervals (alpha and beta) for model  
> average estimates from binomial glm and model.avg (library=dRedging)
>
> In R, the predict family of functions provides that facility. If you
> want the code it will be in the particular function associated with
> the model type.
>
> ?predict
> ?predict.glm
> # the example illustrates creation of prediction curves on the
> response scale for  a specific range of data.
> # create the desired CI's by appropriate use of the se.fit value
> returned from the predict call.
> # This is the code inside predict.glm that does the work when se.fit
> is set as TRUE in the predict call:
>
> se.fit <- pred$se.fit
> switch(type, response = {
>             se.fit <- se.fit * abs(family(object)$mu.eta(fit))
>             fit <- family(object)$linkinv(fit)
>         }, link = , terms = )
>
> -- 
> David Winsemius
>
> On Apr 24, 2009, at 3:03 AM, Michelle Ensbey wrote:
>
>> 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 at nt.gov.au
>>
>> R version 2.8.1

David Winsemius, MD
Heritage Laboratories
West Hartford, CT