similar to: Nearest Neighbor Algorithm in R -- again.

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

Hi, I have a 1.000 observations with 10 attributes (of different types: numeric, dicotomic, categorical ecc..) and a measure M. I need to cluster these observations in order to assign a new observation (with the same 10 attributes but not the measure) to a cluster. I want to calculate for the new observation a measure as the average of the meausures M of the observations in the cluster

I was wondering if there is a way in R to find k nearest neighbors of various orders, say order 2, 3, or 4. In otherwords neighbors of neighbors of neighbors. You get the idea. I know that I can use knearneigh(matrix.data, k) but this only gives me the k nearest neighbors and not of a particular order. Thanks in advance. -- View this message in context:

Is fast nearest-neighbor functionality available in R? I was thinking of something along the lines of what's currently in S+SPATIALSTATS. Thanks for any information anyone might have on this. - MZ

Hi there fellow R-users, Does anyone know of a function which does exactly what knearneigh{spdep} (finds the k nearest neighbours) does in the package spdep but for more than 2D data? Regards Wayne KSS Ltd Seventh Floor St James's Buildings 79 Oxford Street Manchester M1 6SS England Company Registration Number 2800886 Tel: +44 (0) 161 228 0040 Fax: +44 (0) 161 236 6305

I'm trying to do the following: For each ordered pair of a data frame (D1) containing longitudes and latitudes and unique point IDs, calculate the distance to every point in another data frame (D2) also containing longitudes, latitudes and point IDs, and return to a new variable in D1 the point ID of the nearest element of D2. Dramatis personae (mostly self-explanatory): D1$long

Is there an implementation of a reasonable k-nearest neighbor finder already in one of the packages? -- +---------------------------------------------------------------------------+ | Robert Gentleman phone : (617) 632-5250 | | Associate Professor fax: (617) 632-2444 | | Department of Biostatistics office: not yet

Hi all, Say I have a matrix A with dimension m x 2 and matrix B with dimension n x 2. I would like to find the row in A that is closest to the each row in B. Here's an example (using a loop): set.seed(1) A <- matrix(runif(12), 6, 2) # 6 x 2 B <- matrix(runif(6), 3, 2) # 3 x 2 m <- vector("numeric", nrow(B)) for(j in 1:nrow(B)) { d <- (A[, 1] - B[j, 1])^2 + (A[,

Hi I want to create a nice picture about my result of k -the nearest neighbours algorithm. Here is my easy code: ################################# library(klaR) library(ipred) library(mlbench) data(PimaIndiansDiabetes2) dane=na.omit(PimaIndiansDiabetes2)[,c(2,5,9)] dane[,2]=log(dane[,2]) dane[,1:2]=scale(dane[,1:2]) zbior.uczacy=sample(1:nrow(dane),nrow(dane)/2,F)

I have to remove missing data both in character and numeric datatype.I tried using NA condition but it is not working ,please help me to solve this. -- View this message in context: http://www.nabble.com/Handling-missing-data-tp25530192p25530192.html Sent from the R help mailing list archive at Nabble.com.

https://bugs.freedesktop.org/show_bug.cgi?id=94817 Bug ID: 94817 Summary: Nearest neighbor scaling? Product: xorg Version: unspecified Hardware: Other OS: All Status: NEW Severity: enhancement Priority: medium Component: Driver/nouveau Assignee: nouveau at

I've written the function below to simulate the mean 1st through nth nearest neighbor distances for a random spatial pattern using the functions nndist() and runifpoint() from spatsat. It works, but runs relatively slowly - would appreciate suggestions on how to speed up this function. Thanks. --Dale library(spatstat) sim.nth.mdist <- function(nth,nsim) { D <- matrix(ncol=nth,

Hi there fellow R-users, Does anyone know if there is a package for k nearest neighbours prediction as opposed to classification? I have found the package knncat but can't see a way to adjust it to predict a continuous variable. Any help would be great, Regards Wayne Jones KSS Ltd Seventh Floor St James's Buildings 79 Oxford Street Manchester M1 6SS England Company

Hi all I am trying to determine the distances between two datasets of x and y points. The number of points in dataset One is very small i.e. perhaps 5-10. The number of points in dataset Two is likely to be very large i.e. 20,000-30,000. My initial approach was to append the first dataset to the second and then carry out the calculation: dists <- as.matrix(dist(gis data from 2 * datasets))

Dear R-helpers, I have lat/lon coordinates of regularly spaced grid points, about 4Km apart, covering the entire US continental region. I would like to mask this rectangular grid in order to extract all and only the grid points within a specific region. Today I want to extract Montana, say, from this grid, and I am hoping to somehow use the returned value of the function

Hi I need some help with ploting the ROC for K-nearest neighbors. Since KNN is a non-parametric classification methods, the predicted value will be either 0 or 1. It will not be able to test for different cutoff to plot ROC. What is the package or functions I should use to plot ROC for KNN? Thanks. Qian [[alternative HTML version deleted]]

Dear All, I cannot find a solution to the following problem although I imagine that it is a classic, hence my email. I have a vector V of X values comprised between 1 and N. I would like to get random samples of X values also comprised between 1 and N, but the important point is: * I would like to keep the same distribution of distances between the X values * For example let's say N=10 and

I am currently doing some prediction work using the knn script in the 'class' package. Does anyone know a way of having R return the IDs (sample IDs, or column IDs of the training matrix) of the 'k' samples that are chosen by the algorithm as being nearest to a given test sample? I have searched/read everything I can about the script, however have not found anything other than the

Hi, I'm quite new to the R-project. I was suggested to look into it because I am trying to solve the "Subset sum" problem", which basically is: Given a set of integers and an integer s, does any non-empty subset sum to s? (See http://en.wikipedia.org/wiki/Subset_sum_problem) I have been searching the web for quite some time now (which is how I eventually discovered that my

I have gotten this sort of problem some weeks ago and have solved it with the mailing list archive. But now I have this problem again with the class library. What should I put in library/class/R/zzz.R? Everything I put in that file leads to the same error message. I'm running 0.61.3 on Linux. > knn1(train, test, cl) Error in .C("VR_knn1", as.integer(ntr), as.integer(nte),