On Jan 20, 2011, at 2:37 PM, <jeffrey.mor...@sanofipasteur.com> <jeffrey.mor...@sanofipasteur.com
> wrote:
Hello, I have fit a simple spline model to the following data.
Data
x y
0 1.298
2 0.605
3 0.507
4 0.399
5 0.281
6 0.203
7 0.150
8 0.101
Model
Sp.1=lm(y~bs(x,df=4))
Now I wish to inverse predict the x for y=.75, say. Optimize works
fine
for a polynomial but I can figure out how to get the spline model into
the function argument.
Why not pass the reversed x and y vectors from the spline fit to
approxfun()?
> pred.Spl <- predict(Sp.1, data.frame(x=seq(0,8, by=0.01) ) )
> approxfun(x=pred.Spl, y=seq(0,8, by=0.01) )(0.75)
[1] 1.447311
Looks plausible, anyway.
--
David.
Can anyone help me out.
Thanks!!
Jeff
Jeff Morris
Sanofi Pasteur
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