Dear Jin,
if you mean `conditional linearity', (i.e. given the nonlinear parameter,
the model is linear) you can use nls() with algorithm = "plinear". See ?nls
Alternatively, if your model has just one nonlinear parameter th, say, I
think you can write the objective function (for instance the logLik)
depending on th and use optimize() to search for the optimum; Then fit your
model assuming th known (and ignoring its (co)variability.. ). Something
like:
fn<-function(th,y,X){
#the deviance function depending on th
#th: nonlinear parameter to be estimated
#y: the response
#X: the design matrix
o<-glm(y~X+_someKnownFunction(th)_+..)
o$dev
}
#search the optimum
ob<-optimize(fn,..
th1<-ob$minimum #(or ob$maximum)
o<-glm(y~X+_someKnownFunction(th1)_+..) #fit the model assuming th=th1
*known*
Hope this helps,
vito muggeo
----- Original Message -----
From: Jin Shusong <[EMAIL PROTECTED]>
To: R Help <[EMAIL PROTECTED]>
Sent: Thursday, December 16, 2004 4:33 PM
Subject: [R] partial linear model
> Dear all,
>
> Are there any packages can estimate the partial linear
> model. Or any one can give me any suggestions.
>
> Many thanks in advance.
>
>
> Jin
>
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