[R] How to do knn regression?

[Shengqiao Li]

  Date: Fri, 19 Sep 2008 07:00:33 +0000 (UTC)
> From: "Hans W. Borchers" <hwborchers at gmail.com>
> Subject: Re: [R] How to do knn regression?
> To: r-help at stat.math.ethz.ch
> Message-ID: <loom.20080919T065847-65 at post.gmane.org>
> Content-Type: text/plain; charset=us-ascii
>
> Shengqiao Li <shli <at> stat.wvu.edu> writes:
>>
>> Hello,
>>
>> I want to do regression or missing value imputation by knn. I searched
>> r-help mailing list. This question was asked in 2005. ksmooth and loess
>> were recommended. But my case is different. I have many predictors
>> (p>20) and I really want try knn with a given k. ksmooth and loess use
>> band width to define neighborhood size. This contrasts to knn's 
variable
>> band width via fixing a k. Are there any such functions I can use in R
>> packages?
>>
>
> The R package 'knnFinder' provides a nearest neighbor search based on 
> the approach through kd-tree data structures. Therefore, it is extremely 
> fast even for very large data sets. It returns as many neighbors as you 
> need  and can also be used, e.g., for determining distance-based 
> outliers.
>

Thanks for your info. But it seems that there are problems to use 
knnFinder. knnFinder doesn't distinguish Test data and Train data. It 
searches in all data. New data with unknow Y's may appear in neighbors in 
the X space. The mask arg. seems not solving this problems. In addtion, I 
notice that there are several other possible problems with knnFinder:

(1) Ties are ignored.
(2) knnFinder is slower than class::knn when number of 
variables is relatively small, eg. 70. 
(3) Memory leakage.
(4) Maximum distance is small.
(5) One extra column is needed.

I rewrote knnFinder code to solve the last three problems for other 
purposes for which the self-match is not allowed. But self-math option is 
not a function parameter. It's a MACRO variable. So this option cannot be 
changed once the library is compiled. For regression, ties should be 
used. I have to compile two versions. This is not neat.

Any other convenient ways?


> Hans Werner Borchers
> ABB Corporate Research
>
>>
>> Your help is highly appreciated.
>>
>> Shengqiao Li
>>
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>
>