passing arguments to a function

[Pravin Jadhav <pravinj@gmail.com>]

Dear all,

For my previous query (obtain predictions for the missing records), Dr.
Pinheiro pointed me to the "predict" function. Thanks! It is doing
exactly what I needed. But I am having some trouble putting it inside a
generic function. Here is an example (dummy):

I want to describe response (resp) as a function of time (time) using a
linear mixed effects model for several trials with subjects (id) as a
grouping variable. Of the 3 trials, the following code evaluates ONLY
the 1st  trial. Then the lmeObject is used for prediction.
Although, there is no missing data here, the actual dataset has some
missing observations. This step works just fine.

       #CREATE DATA
       data<-data.frame(trial=rep(1:3,each=30),id=rep(rep(1:10,each=3),3),time=rep(c(1:3),30))
       data$resp<-rep(rnorm(30,5,1),each=3)+rep(rnorm(30,0.5,0.2),each=3)*time+rnorm(90,0,1)
      #FIT DATA AND PREDICT
      
fit1<-lme(resp~time,data=""
na.action="">
       pred1<-predict(fit1, newdata=data, level=0:1)

Now, if I take this inside a generic funciton (only the relevant part
is shown) to evaluate all the trials, there is an error. The first
part, LME works fine but has problems with predict function. It turns
out that the lmeObject, "fit", does retain the information about the
linear two sided formula used to describe fixed effcts. If you take a
look at the object "fit1" from the previous step and "fit2" from the
next step, the difference is clear. "fit1" has explicit two sided
formla, whereas, "fit2" retains only list(fullF) and thus causing
problems in the predict step.

fit.F<-function (data, byvar, fullF, ranF,...)
                {
           
        fit<-by(data, byvar,
function(bydata, fullF, ranF,...)
           
           
           
            {
           
           
           
           
    fit<-lme(fixed=fullF, data="" random=ranF,
method="ML", na.action="">
           
           
           
            #   
pred.fit<-predict(fit,newdata=bydata, level=0:1)
           
           
           
           
    return(fit)
                                               
#    retrun(pred.fit)
           
           
           
           
},fullF=fullF,ranF=ranF)
                }
fit2<-fit.F(data, data$trial, resp~time,list(id=pdDiag(~1+time))) 

How do I tweak the function so that the predict step is also working?

Sorry for a lengthy email (if you are still reading it). Any comments are greatly appreciated.

Thanks,

Pravin