[R] ksmooth in SPLUS vs R

[Jean Eid]

Please do forget my questions as they are really trivial and I do not kno
what I was thinking of.

Thank you thomas for clarifying my cloudy head today.

jean,


On Mon, 22 Sep 2003, Jean Eid wrote:

> I am working with a model that I have to estimate a nonparametric
> function. The model is partial linear i.e.
>
> Y=X$\beta$ + f(z) + $\epsilon$
>
> I am using the ' double residual methods' Robinson (1988) Speckman (1988)
> where I estimate a nonparametric function for each of the parametric
> variables in terms of the nonparametric one i.e.
>
> X[,i]=g(Z)+ u
>
> this is done because I need the $E( X[,i]\vert Z)$ for each position j in
> the vectors.
>
> the problem is that when I use the ksmooth() function in R it estimates
> the function at different points and not those that consist of the Z
> vector.
>
> the ksmooth() function in Splus on the other hand evaluates the points at
> the corresponding Z vector. both codes are given below
>
>
> d<-ksmooth(lprice,XX[,i],kernel="box")
> unique(lprice-d$x)
>
> in SPLUS will generate 0 while in R it generates a vector of different
> values.
>
>
>
> My second question is regarding the sm library:
>
> d<-sm.regression(lprice, XX[,i], h=sd(lprice), display="none")
> will only generate 50 point estimates while  NROW(XX[,i]) = 3897
> and when I do
> d<-sm.regression(lprice, XX[,i], h=sd(lprice), display="none",
> ngrid=NROW(lprice))
>
> I get the right dimension of the estimated points but again they are not
> estimated at the points in lprice.
>
>
> Any help is greatly appreciated.
>
> P.S. I have Bowman and Azzalini book but unfortunately it does not clarify
> the procedures in sm.regression()
>
> Jean,
>
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