Hello all,
I'm trying to establish some confidence intervals on predictions I am making
using
>predict(nls(...))
and predict.nls (unfortunately) does not utilize the se.fit option. A little
more background is that I am trying to match the output with older SAS routines
to maintain consistency. Because predict.nls does not provide se's for
individual predictions, I have been using a method suggested on r-help
previously (by P. Dalgaard) using deriv() and vcov(). Specifically,
se.fit<-sqrt(apply(gradient, 1, function(x)
sum(vcov(myNLSmodel)*outer(x,x))))
where I determine "gradient" by using deriv() and the points for which I am
making the predictions. This works for predicting the confidence interval for
the mean predicted Y, however, I need the confidence interval for the
individual predicted Y. According to the stats text I looked at, I need to
change the above formula to
se.fit<-sqrt(apply(gradient, 1, function(x)
sum(vcov(myNLSmodel)*outer(x,x))+NEWTERM))
where NEWTERM is
NEWTERM<-myNLSmodel$m$deviance()/(nObservations - nParameters)
So the problem is that while the confidence intervals are close to those
provided by SAS, they are always smaller. This, in the end, makes a large
difference for the application. Can anyone suggest how I might make the
confidence intervals I calculate in R match up with those of SAS for nls?
Thanks in advance,
Ben Ridenhour
________________________________
Benjamin Ridenhour
Centers for Disease Control & Prevention
Atlanta, GA 30329
"If we knew what we were doing,
it wouldn't be research."
--Einstein
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