I have data of this form:
x y
1 0.19
2 0.26
3 0.40
4 0.58
5 0.59
6 1.24
7 0.68
8 0.60
9 1.12
10 0.80
11 1.20
12 1.17
13 0.39
I'm currently plotting a kernel-smoothed density estimate of the x versus y using this code:
smoothed = ksmooth( d$resi, d$score, bandwidth = 6 )
plot( smoothed )
I simply want a plot of the x versus smoothed(y) values, which is ## Heading ##
However, the documentation for ksmooth suggests that this isn't the best kernel-smoothing package available:
This function is implemented purely for compatibility with S, although it is nowhere near as slow as the S function. Better kernel smoothers are available in other packages.
What other kernel smoothers are better and where can these smoothers be found?
If you "simply want a plot of the x versus smoothed(y)", then I recommend considering loess
in package stats
- it's simple, fast and effective. If instead you really want a regression based on kernel smoothing, then you could try locpoly
in package KernSmooth
or npreg
in package np
.
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