统计问题:R 中的核平滑 [英] Statistics Question: Kernel Smoothing in R

问题描述

我有这种形式的数据:

 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

我目前正在使用以下代码绘制 x 与 y 的核平滑密度估计:

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 )

我只想绘制 x 与平滑 (y) 值的关系图，即 ## Heading ##

I simply want a plot of the x versus smoothed(y) values, which is ## Heading ##

但是，ksmooth 文档 表明这不是最好的内核平滑包:

However, the documentation for ksmooth suggests that this isn't the best kernel-smoothing package available:

这个功能是纯粹实现的为了与 S 兼容，虽然它远不及 S 慢功能.更好的内核平滑器是在其他包中可用.

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.

还有哪些内核平滑器更好?在哪里可以找到这些平滑器?

What other kernel smoothers are better and where can these smoothers be found?

推荐答案

如果你只是想要一个 x 与平滑 (y) 的图"，那么我建议考虑包 中的 loess>stats - 简单、快速且有效.相反，如果您真的想要基于内核平滑的回归，那么您可以尝试包 KernSmooth 中的 locpoly 或 np<包中的 npreg/代码>.

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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