How to do knn regression?
Hi Shengqiao, I don't know any direct solutions to your question, but I don't think it's difficult to write a few lines of code to find the k-nearest neighbours for an observation with a missing value. Typically you need the function dist() to compute distances, rank() or order() to find the k-nearest neighbours, and finally using mean() or median() or any statistic to make predictions. To assure you the light work of programming, I can tell you all the code of this example (http://animation.yihui.name/dmml:k-nearest_neighbour_algorithm) is no more than 100 lines :-D But seriously speaking, I don't think my method is efficient. Maybe C code will be much faster, as the knn() function in package 'class' has called. Regards, Yihui -- Yihui Xie <xieyihui at gmail.com> Phone: +86-(0)10-82509086 Fax: +86-(0)10-82509086 Mobile: +86-15810805877 Homepage: http://www.yihui.name School of Statistics, Room 1037, Mingde Main Building, Renmin University of China, Beijing, 100872, China
On Fri, Sep 19, 2008 at 10:17 AM, Shengqiao Li <shli at stat.wvu.edu> wrote:
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? Your help is highly appreciated. Shengqiao Li
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