General Instructions
To do each example, just click the Submit
button.
You do not have to type in any R instructions or specify a dataset.
That's already done for you.
Notes
The handout for smoothing is available in Adobe PDF format. Paper copies were handed out in class. No need to print out another if you got one in class.
Running Mean Smoother
We use for example data the cholostyramine data from Section 7.3 in Efron and Tibshirani.
Comments
The R function ksmooth
(on-line
help) does simple smoothing.
With kernel = "box"
, which is the default, it does a
running mean smoother.
Note that this smooth
is not actually very smooth. This is a property
of the kernel
not being smooth.
In the next section we do better.
Try with different bandwidth
values.
General Kernel Smoother
We use for example data the cholostyramine data from Section 7.3 in Efron and Tibshirani.
Comments
Again we use the R function ksmooth
(on-line
help), this time
with kernel = "normal"
.
Try with different bandwidth
values.
Local Polynomial Smoother
We use for example data the cholostyramine data from Section 7.3 in Efron and Tibshirani.
Comments
The R function locpoly
(on-line
help) does local polynomial smoothing.
Try with different bandwidth
values.
The function locpoly
is in the R package KernSmooth
so you must do library(KernSmooth)
before using it.
Smoothing Spline
We use for example data the cholostyramine data from Section 7.3 in Efron and Tibshirani.
Comments
The R function smooth.spline
(on-line
help) does spline smoothing.
Try with different df
values.
One can specify the smoothing parameter by using
the spar
argument instead of the df
argument. The former is the amount of penalty, the latter analogous to
degrees of freedom (effective number of parameters) in the regression function.
One can omit any specification of the smoothing parameter. Then a supposedly optimal choice is made. See the web page about bandwidth selection for more on this.