Thank you Shige,I got the Zelig package to work for me. For anyone who cares,
here's the code:
library(Zelig)order(df$WEIGHT)z.out<- zelig(WEIGHT~DISTANCE, model =
"normal.gee",id = "MOTHER", data = df,corstr =
"exchangeable")summary(z.out)date.range<-0:51 # sequence of values over the
range of DISTANCEx.high <- setx(z.out,DISTANCE=date.range)x.low <-
setx(z.out,DISTANCE=date.range)s.out <- sim(z.out, x = x.low, x1 =
x.high)summary(s.out)plot.ci(s.out,col="RED")abline(z.out)
The "plot.ci" function produces a plot without confidence bands, per se. The
resulting figure has a vertical confidence bar at every value of X. It's kind
of a cool effect and just as good, I guess, as the standard regression line
plot with confidence bands.
Thanks again Shige for pointing me in the right direction.cheers,Jason
On Mon, Oct 17, 2011 11:36 AM, Shige Song <shigesong@gmail.com> wrote:
>
Hi Jason,
>
>I would go for Zelig package to get simulated values and confidence
>intervals. It can handle gee model.
>
>Shige
>
>On Mon, Oct 17, 2011 at 9:38 AM, JASON M. HILL <jmh656@psu.edu> wrote:
>> Hello Fellow R
>> Users,I have
>> spent the last week trying to find a work around to this problem and I
>can't
>> seem to solve it. I simply want to plot my GEE model result with 95%
>confidence
>> bands.
>>
>>
>> I am using the geepack package to run a basic GEE model involving
>> nestling weights, to a Gaussian distribution, with
>"exchangeable" error
>> structure. I am examining how nestling weight varies as a function of
>distance
>> from a plot boundary.
>>
>>
>>
>> The response variable (WEIGHT) and the explanatory
>> variable (DISTANCE) are both continuous. The clustering factor
>(MOTHER) is
>> entered into my model to account for similarity of nestlings from the same
>nest
>> produced by a given mother.
>>
>>
>> My simplified code is
>> as follows:
>>
>>
>> summary(model1<-geeglm(WEIGHT~DISTANCE,
>> id=MOTHER,data=df,corstr="exchangeable")
>> #I've included part of the
>> model output
>> here
>>
>>
>>
>> <Coefficients:
>> <
>>                  Estimate Std.err
>>  Wald         Pr(>|W|)
>>
>> <(Intercept)     15.8702  0.4416
>> 1291.8  < 2e-16 ***
>> <Initiationdate  -0.0664
>>  0.0157   17.9          2.4e-05
>> ***
>> <
>> <Estimated Scale
>> Parameters:
>> <
>> <            Estimate
>> Std.err
>> <(Intercept)     5.78
>>  2.46
>>
>>
>>
>>
>>
>>
>> plot(df$DISTANCE,df$WEIGHT)
>>
>>
>> abline(model1)
>>
>>
>>
>x<-seq(min(df$DISTANCE),max(df$DISTANCE),l=1000)
>>
>>
>> y<-predict(model1,data.frame(DISTANCE=x)
>>
>>
>> Everything is fine up until this last line of code, when I get the following
>> error message:
>>
>>
>>
>> "Warning message:
>> In predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type ==  :
>>  calling predict.lm(<fake-lm-object>) ..."
>>
>>
>>
>> This isn't a geepack problem because I get the same error message using the
gee
>> package as well. The above simplified code is how I usually create and plot
95%
>> CI bands for a linear model, followed by:
>>
>>
>> matlines(x,y)
>>
>>
>> I have read through the package PDFs and searched the web and archives of
>> various listservs without success. Any help would be most appreciative. Seems
>> like there should be a simple work-around. Thank you,
>> Jason
>>
>>
>>
>>
>>
>>
>>        [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> R-help@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
>
>

'Nothing in biology makes sense, except in the light of evolution'
--Theodosius Dobzhansky--

Jason Hill
http://www.coopunits.org/Pennsylvania/People/Jason_Hill/index.html
PA Cooperative Fish and Wildlife Research Unit
221 Forest Resources Building
University Park, PA 16802-4705
Office: 814-865-0772
Fax: 814-863-4710
Ecology Program - PhD Candidate
Pennsylvania State University
School of Forest Resources




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