similar to: nearest neighbours and their ID

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,

Can anyone tell me why I would get different average nearest neighbor values for the same set of coordinates between ArcGIS 10 and R? Sometimes the difference in distance is over 1.3 km. Alexis -- View this message in context: http://r.789695.n4.nabble.com/nndist-R-vs-ArcGIS-tp3442375p3442375.html Sent from the R help mailing list archive at Nabble.com.

Hi all, I'm trying to install the splancs contributed package on a Redhat 7.3 system with R 1.5.1 installed. I've installed the readline41-4.1-10 RPM which Redhat requires for R and it works great, thanks very much. However, when I attempt to R CMD INSTALL splancs_2.01-9.tar.gz as root, I get the following error message during the install: [snip] g77 -fPIC -O2 -m486

Hello all: Is there any way to add points to a point pattern, while keeping a given minimum distance amongst this new points and the pre-existing points? Thanks, Frederico [[alternative HTML version deleted]]

Dear R-user, For the "matplot" of the following data drawn by >matplot(t, pch=1) structure(c(2372L, 2494L, 2170L, 2238L, 1918L, 715L, 1573L, 2437L, 2481L, 1881L, 440L, 2165L, 343L, 121L, 597L, 1163L, 1459L, 2093L, 1454L, 1761L, 1872L, 174L, 2040L, 552L, 1403L, 6381L, 1649L, 4136L, 4415L, 10650L, 12111L, 4397L, 9593L, 7040L, 3888L, 8340L, 11475L, 11413L, 3985L, 1077L, 7723L, 1392L,

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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 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 having a problem where I have to apply a function to a subset of a variable, where the subset is defined by the n nearest neighbours of a second variable. Here's an example applied to the 'iris' dataset: $ head(iris) Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4

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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 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

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