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Hi all, I would like to know a way of calculating the distance between 2 points when I already have the longitude and latitude of the points. For example : Point 1 : 52? 9'54.00"N 4?25'8.40"E Point 2 : 52?27'46.80"N 4?33'18.00"E Distance between point 1 and point in km .... Is there any functions already available...

Hey Everyone! I wanted to carry out Hierarchical clustering using distance matrices i have calculated ( instead of euclidean distance etc.) I understand as.dist is the function for this, but the distances in the dendrogram i got by using the following script(1) were not the distances defined in my distance matrices. script: var<-read.table("the distance matrix...

Hi, I want to know which distance is using in the function kmeans and if we can change this distance. Indeed, in the function pam, we can put a distance matrix in parameter (by the line "pam<-pam(dist(matrixdata),k=7)" ) but we can't do it in the function kmeans, we have to put the matrix of data directly ... Tha...

Hello, I want to use pearson's correlation as distance between observations and then use any centroid based linkage distance (ex. Ward's distance) When linkage distances are formed as the Lance-Williams recursive formulation, they just require the initial distance between observations. See here: http://en.wikipedia.org/wiki/Ward%27s_method It is...

Hello. Is it possible to choose the distance in the kmeans algorithm? I have m vectors of n components and I want to cluster them using kmeans algorithm but I want to use the Mahalanobis distance or another distance. How can I do it in R? If I use kmeans, I have no option to choose the distance. Thanks in advance, Arnau.

Dear all: I want to ask question about "binary" distance measure. As far as I know, there are many binary distance measures,eg, binary Jarcad distance, binary euclidean distance, and binary Bray-Curtis distance,etc. It is even more confusing because many have more than one name. So , I wan to know what the definite name of the binary distance measure o...

Hi, I have a dataframe like this: procedure property sensor_data sensor_date | | | | [1,] "PAT_Laser_2" "Distance" "30.42" "2010-09-30T15:00:12+0200" [2,] "PAT_Laser_2" "Distance" "31.22" "2010-10-31T15:05:07+0100" [3,] "PAT_Laser_2" "Distance" "30.42" "2011-09-30T15:00:12+0200" [4,] &...

I'm working on a project related to document clustering. I know that R has clustering algorithms such as clara, but only supports two distance metrics: euclidian and manhattan, which are not very useful for clustering documents. I was wondering how easy it would be to extend the clustering package in R to support other distance metrics, such as cosine distance, or if there was an API for custom distance metrics. Best regards, Raymon...

...simply because the loop function seems more natural to me. However, the current simulation takes forever and I have decided - finally - to learn how to use apply, but - as many other people before me - I am having a hard time changing habits. My current problem is: My current code for the loop is: distances <- matrix(NA, 1000, 5) distancer <- function(x, y){-(abs(x-y))} x <- as.matrix(rnorm(1000, 5, 1.67)) y <- rnorm(5, 5, 1.67) for (v in 1:1000){ distances[v,] <- distancer(x[v,], y) } The goal is to calculate the distances between the preferences of each voter (X) and all parties (...

...ta.matrix<-data.matrix(data[,y]) variableprobe<-apply(data.matrix[x,],1,var) hist(variableprobe) **************filter out low variance************* data.sub = data.matrix[order(variableprobe,decreasing=TRUE),][1:10000,] dim(data.sub) [1] 10000 140 What is the best way to calculate the distances between the samples using the euclidean or manhattan distance metrics? any suggestions? -- View this message in context: http://www.nabble.com/Calculating-the-distance-samples-using-distance-metics-method-tp15578860p15578860.html Sent from the R help mailing list archive at Nabble.com.

Dear all, I am exploring the possibilities for automated classification of my data. I have successfully used KNN, but was thinking about looking at SVM (which I did nto use before). I have a pairwise distance matrix of training observations which are classified in set classes, and a distance matrix of new observations to the training ones. Is it possible to use distance matrices for SVM, and if yes, which package would do so (e1071 ? ). I have little experience with SVM, and I had the impression th...

Hello R helpers, I'm trying to use Mahalanobis distance to calculate distance of two time series, to make some comparations with euclidean distance, DTW, etc, but I'm having some dificults. I have, for example, two objects: s.1 <- c( 5.6324702, 1.3994353, -3.2572327, -3.8311846, -1.2248719, 0.9894694, -2.2835332, -5.1969285, -5.2823988, -3.1...

Dear R helpers, I think that this may be a bit of a math question as the more I consider it, the harder it seems. I am trying to come up with a way to work out the minimum distance between line segments. For instance, consider 20 random line segments: x1 <- runif(20) y1 <- runif(20) x2 <- runif(20) y2 <- runif(20) plot(x1, y1, type = "n") segments(x1, y1, x2, y2) Inititally I thought the solution to this problem was to work out the distance between m...

...to R and modeling but need some help with visualization of glms. I'd like to make a graph of my glms to visualize the different effects of different parameters. I've got a binary response variable (bird sightings) and use binomial glms. The 'main' response variable is a measure of distance to a track and the parameters I'm testing for are vegetation parameters that effect the response in terms of distance. My glm is: glm(Response~NEdist+I(NEdist^2)+Distance+I(Distance^2) which is the basic model and where I add interactions to, like for exampls Visibility as an interaction to Di...

Dear All, I would like to use the simprof function (clustsig package) but the available distances do not include Jaccard distance, which is the most appropriate for pres/abs community data. Here is the core of the function: > simprof function (data, num.expected = 1000, num.simulated = 999, method.cluster = "average", method.distance = "euclidean", method.transform...

...issing values (geom_path).?3: Removed 6 rows containing missing values (geom_smooth).?-------------------------------------------------------------script R: suppressMessages (library(ggplot2)) source ("graph-style.R") input = "graphs/figure-3-data-propagation-vs-time/car-relay-jump-distance.txt.gz2"output = "graphs/pdfs/figure-3-data-propagation-vs-time.pdf" data <- read.table (bzfile(input, "r"), header=TRUE)data$Run = as.factor(data$Run)data$DistanceFromSource = data$NodeId * data$Distance data = subset(data, Distance %in% c(10, 50, 90, 130, 170))data$Di...

I am pretty new to R. So this may be an easy question for most of you. ? I would like to calculate the squared distances of a large set (let's say 20000) of vectors (let's say dimension of 5) to a fixed vector. ? Say I have a data frame MY_VECTORS with 20000 rows and 5 columns, and one 5x1 vector y. I would like to efficiently calculate the squared distances?between each of the 20000 vectors in MY_VECTORS a...

Dear R-experts, Searching the help archives I found a recommendation to do multivariate outlier identification by mahalanobis distances based on a robustly estimated covariance matrix and compare the resulting distances to a chi^2-distribution with p (number of your variables) degrees of freedom. I understand that compared to euclidean distances this has the advantage of being scale-invariant. However, it seems that such mahalano...

dear useRs, i created a distance matrix, of certain voltage values. unfortunately, i lost the original values. i am only left with the distance matrix that i created from those values. i wanted to ask that is there a way in R to reverse distance matrix for the original values? thanks in advance eliza [[alternative H...