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 <[EMAIL PROTECTED]> 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 <[EMAIL PROTECTED]> 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 > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.