R: [R] partial linear model

Vito Muggeo vito.muggeo at giustizia.it
Thu Dec 16 17:25:35 CET 2004

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

#the deviance function depending on th
#th: nonlinear parameter to be estimated
#y: the response
#X: the design matrix

#search the optimum

th1<-ob$minimum #(or ob$maximum)
o<-glm(y~X+_someKnownFunction(th1)_+..) #fit the model assuming th=th1

Hope this helps,
vito muggeo

----- Original Message -----
From: Jin Shusong <jinss at hkusua.hku.hk>
To: R Help <r-help at stat.math.ethz.ch>
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
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
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