similar to: k nearest neighbours

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

Dear R-helpers, I get GPS readings with bug counts (bugs meaning insects in this case) made along rows in crop fields and use these to make maps of bug distribution. The GPS readings are not quite accurate enough for my purpose, so since I know what row each reading is made in, I adjust the latitudinal coordinate using: grd<-lm(lat~lon+Row,data)

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 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

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

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

I appreciate the help I've been given so far. The issue I face is that the data I'm working with has 53000 rows, so in calculating distance, finding all recids that fall within 2km and summing the population, etc. - a) takes too long and b) have no sense of progress. Below is a loop that reads each recid one at a time, calculates the distance and identifies the recids that fall within 2

Gracias a los tres, Raúl, Marcelino y Carlos. Lo del "get" de Marcelino me da la respuesta a lo que yo exactamente preguntaba, y funciona, pero ahora tengo problemas con el for, por lo que probablemente recurra al eval parse de Raúl o Carlos, que ya tienen el for. Aún así, lo intento 1º con el get. Con subset(df, subset=get(GT[i])>0) el problema es que en el for hago un

Gracias Marcelino. Si, (i in 1:length(GT)), lo he utilizado mil veces, pero se me sigue olvidando de una vez a otra. Lo iba a mirar, pero me centré primero en que me hiciera bien el mapa. He probado el for y me da este error: Error in aes(x = lon, y = lat, color = get(GT[i]), size = 2) + scale_colour_gradient(low = ("white"), : non-numeric argument to binary operator

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

New installation seems to have behavior I cannot figure out. Here is illustrative sequence where I load a small data set (test) from Crawley's files and try to run a simple linear model and get an error message. Oddly, R reports that the variable 'test$ozone' is numeric while, after attaching test, the variable ozone is not numeric. Can someone please help? This behavior is

summary: I can display a lon-lat map on a lattice::layerplot, and I can display a Lambert conformal conic (LCC) map on a spam::image, but I can't display an LCC map on a lattice::layerplot. Example follows. What am I doing wrong? details: I've been using `lattice` (via `rasterVis`) successfully to display global atmospheric data, which works well enough (though I am definitely intrigued

> library(reshape2) > x = melt(airquality, id=c('month', 'day')) With reshape I can cast with multiple functions: > library(reshape) > cast(x, month+variable~., c(mean,sd)) month variable mean sd 1 5 ozone 23.615385 22.224449 2 5 solar.r 181.296296 115.075499 3 5 wind 11.622581 3.531450 4 5 temp 65.548387

I have created a symbol plot with circles that represent the mean temperature at lat/lon locations over the United States. The radius of the circle corresponds to the mean temperature. I would like to add a legend that identifies a range of temperatures (e.g. 0-10, 10-20, etc) with circles of the appropriate radii next to them. I've read the manual on how to add a legend, and I'm fine with

I want to estimate the effect of several independent variables on several dependent variables. In the example below I wanted to estimate the effect of three independent variables on ozone and temperature. My aim is to create a list of dependent and independent variables and automate the process rather than writing every dependent and independent variable in each model as I have done below.

I think I've gotten my Emacs/Sweave/R system set up correctly, thanks to Vincent and Jim, but I haven't been successful getting my first document produced. I'm trying to use one of Friedrich Leisch's examples, http://www.ci.tuwien.ac.at/~leisch/Sweave/example-1.Snw. I cut and pasted the text into a document sweaveexample.Rnw in Emacs. It seemed to be processed successfully with R:

Hello Given the following data for a data set called airquality. To identify the nature of the objects from the data set airquality example "Ozone" would it be best to use the command is. like is.character(airquality$Ozone) ....... I tried attributes(airquality$Ozone) but it came up null. Would there be a better way to identify these objects. Thanking you in advance for your

Hi all, I've got some functioning code that I've literally taken hours to write. My 'R' coding is getting better...it used to take days :) I know I've done a poor job of optimizing the code. In addition, I'm missing an important step and don't know where to put it. So, three questions: 1) I'd like the resulting output to be sorted on distance (ascending) and

Hi Gustav, Try this: lapply(1:length(models),function(i) lapply(models[[i]],function(x) summary(x)$coef[2,]))[[1]] #1st list component [[1]] #??? Estimate?? Std. Error????? z value???? Pr(>|z|) # pm10 #5.999185e-04 1.486195e-04 4.036606e+00 5.423004e-05 #[[2]] #??? Estimate?? Std. Error????? z value???? Pr(>|z|) #ozone #0.0010117294 0.0003792739 2.6675428048 0.0076408155 #[[3]] #???

>>>>> peter dalgaard <pdalgd at gmail.com> >>>>> on Mon, 26 Jun 2017 13:43:28 +0200 writes: > This seems to be due to changes made by Martin Maechler in > 2008. Presumably this fixed something, but it escapes my > memory. Yes: The change set (svn -c46441) also contains the following NEWS entry BUG FIXES o