Yes, you are right that the results of smooth.spline are slightly worse than
that of sm.spline.
The Doppler function is "tricky". At small `x' values, it oscillates rapidly.
Hence it is not surprising that the smoothers do not do as well.
Here is a noisy version of your Doppler function. I have also considered
another smoother `glkerns'. As you can see, the smoothers do better for larger
`x' than for small `x'. It is impossible to distinguish changes in function
from noise.
require(pspline)
require(lokern)
x=array(0,1000)
y=array(0,1000)
for (i in 1:1000){
x[i] = i/1000
y[i] = (x[i]*(1-x[i]))^.5 * sin(2*pi*(1.05/(x[i]+.05)))
}
y <- y * (1 + rnorm(1000, 0, 0.2))
plot(x,y, cex=0.4, xlim=c(0,0.1))
fit = sm.spline(x, y, norder=2, cv=FALSE)
lines(fit$x,fit$y, col=2)
fit2 = smooth.spline(x, y, cv=FALSE)
lines(fit2$x,fit2$y, col=3)
fit3 = glkerns(x, y)
lines(fit3$x.out,fit3$est, col=4)
Ravi.
________________________________________
From: r-help-boun...@r-project.org [r-help-boun...@r-project.org] On Behalf Of
guy33 [david.res...@magd.ox.ac.uk]
Sent: Sunday, May 29, 2011 6:28 PM
To: r-help@r-project.org
Subject: Re: [R] Fitting spline using Pspline
Ravi,
Thanks so much! You're right, smooth.spline does work on larger n.
Although, for some reason it's results are different (slightly less good?,
but I'm not sure). For example, on the simple doppler function below,
sm.spline seems to be closer to the true function than smooth.spline:
x=array(0,1000)
y=array(0,1000)
for (i in 1:1000){
x[i] = i/1000
y[i] = (x[i]*(1-x[i]))^.5 * sin(2*pi*(1.05/(x[i]+.05)))
}
plot(x,y)
fit = sm.spline(x, y, norder=2, cv=FALSE)
lines(fit$x,fit$y)
fit2 = smooth.spline(x, y, cv=FALSE)
lines(fit2$x,fit2$y)
What do you make of that?
-guy33
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