similar to: PR#4195

Dear R users, I used nndist() to gain the distance of 2 nearest neighbours of the points in my dataset. Is there a way of getting the ID numbers of these nearest neighbours (along with their distances). The command I used: nn2 <- nndist(X2, k=1:2). This is the output of the nearest neighbour distances I got (the IDs of the given points are there but their IDs aren't). Any help or

Hi there, is there any R-function for k-nearest neighbour agglomerative hierarchical clustering? By this I mean standard agglomerative hierarchical clustering as in hclust or agnes, but with the k-nearest neighbour distance between clusters used on the higher levels where there are at least k>1 distances between two clusters (single linkage is 1-nearest neighbour clustering)? Best regards,

Shown below is most of the FORTRAN subroutine named HCLUST. My question concerns the DO loop labeled as '10'. What happened to its CONTINUE statement? I will assume that after FLAG(I)=.TRUE. is executed that control returns to DO 10 I=1,N. Am I correct? Dave ---------------------------- C Initializations C DO 10 I=1,N C We do not initialize MEMBR in order to be able to

[[this bounced first, because it has 'help' in the Subject line ... -- Martin Maechler ]] I am a complete novice R programmer. (Though I know C quite well) I am trying to write some R code to do the following simulation. There is a 2-frame "movie" of noise and signal dots. the noise dots have random positions in each frame. The signal dots are placed randomly in frame 1,

I want to 1) Select for each of the n points in a matrix A, those of the m points in B that lay within a given radius. 2) Of those points within the radius, select the k nearest ones. What I now do is 1) Create an n*m matrix C were I put the distances from all the points in B to the points in A and make NA those cells were the distance is larger than the radius. (The points are geographical

Dear All, I am not able to run the random forest with my dataset.. X<- 280 records with satellite data(28 columns) - B1min, b1max, b1std etc.. y<- 280 records with 3 columns - TotBasal Area, Stem density and Volume yref <- y[1:230,] #Keeping 1st 230 records as reference records want to set 0 to y values for records 231 to 280.. yimp <- y[231:280,] #records for which we want

>>>>> "BDR" == Prof Brian Ripley <ripley at stats.ox.ac.uk> >>>>> on Fri, 30 May 2008 22:34:28 +0100 (BST) writes: BDR> I think it is ESS that is parsing this as a help BDR> request (so it can divert it to an ESS buffer). BDR> Looks like this is an ESS issue, not an R one. yes, indeed, hence much more belonging the ESS-help

Hi: First my apologies for cross-posting. A few days back I posted my queries ar R-sig-geo but did not get any response. Hence this post. I am working on two parcel-level housing dataset to estimate the impact of various variables on home sale prices. I created the spatial weight metrics in ArcGIS 10 using sale year of four nearest houses to assign weights.  Next, I ran LM tests and then ran

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

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 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)

Hi, I've been trying to pass a character vector from R to a FORTRAN subroutine. There have been several posts discussing this issue (e.g. http://tolstoy.newcastle.edu.au/R/help/98a/0547.html, http://tolstoy.newcastle.edu.au/R/help/05/10/13558.html, http://tolstoy.newcastle.edu.au/R/help/01a/2577.html, http://tolstoy.newcastle.edu.au/R/help/01c/1795.html,

I am extracting climate data for coastal areas from 5km grid data for specific xy coordinates that relates to individual study sites (SQ). Code so far: setwd("/Users/roblewis/Documents/PhD/Climate Data") fnamestemp=read.table("Path_Directory_Maxt") fnames=paste(fnamestemp[,1],fnamestemp[,2]) nfiles = length(fnames) xy=as.matrix(read.table("xy_.txt"))

Hello all, I am brand new to R and doing an exercise for a class. I need to find the nearest neighbour for points in the following matrix: > DistanceMatrix x1 x2 x3 x4 x5 [1,] 0.000000 2.828427 1.581139 2.236068 2.000000 [2,] 2.828427 0.000000 1.581139 4.123106 2.000000 [3,] 1.581139 1.581139 0.000000 2.549510 2.121320 [4,] 2.236068 4.123106 2.549510

Hi, I have ~40,000 rows in a database, each of which contains an id column and 20 additional columns of count data. I want to cluster the rows based on these count vectors. Their are ~1.6 billion possible 'distances' between pairs of vectors (cells in my distance matrix), so I need to do something smart. Can R somehow handle this? My first thought was to index the database with

>>>>> "BDR" == Prof Brian Ripley <ripley at stats.ox.ac.uk> >>>>> on Tue, 13 May 2008 07:32:43 +0100 (BST) writes: BDR> This example does not crash in R 2.7.0, R-patched nor BDR> R-devel (r45677) for me (x86_64 F8 Linux.) It also BDR> does not crash with the CRAN build of R 2.7.0 on BDR> Windows XP. Neither does it

I have two dataframes A and B consisting of latitude longitude coordinates of points. For each point in A I want to find the k-nearest neighbours in B. Currently, I calculate the distance from each point in A to all the points in B (using rdist.earth() in fields package), sort the points of B by distance and select the k nearest ones. Is there a more efficient way to do this? Function knearneigh

Hi all, I'm very new to R and read a few tutorials, however I'm having difficulty trying to figure out how to plot a minimum spanning tree. I have a csv file that contains an n-by-n matrix of distances between strains of bacteria called matrix.csv. Looks like: id,strain1, strain2,strain3 strain1,0,.2,.8 strain2,.3,0,.7 strain3,.4,.6,0 I've been messing around with some information

On Tue, 7 Mar 2000, Martin Maechler wrote: > >>>>> "BDR" == Prof Brian D Ripley <ripley@stats.ox.ac.uk> writes: > > BDR> On Tue, 7 Mar 2000, Trenkler, Dietrich wrote: > >> I think I've discovered what went wrong. > >> > >> My workspace included a function wilcox.test formerly copied from > >>

Hello, I'm new to R and to Spatial Analysis and got a problem trying to create a Spatial Weights Matrix. *I us the following code to create the Neighbourslist:* >library(maptools) >library(spdep) >library(rgdal) >location_County<- readShapePoly("....") >proj4string(location_County)<- CRS("+proj=longlat ellps=WGS84") >location_nbq<-