search for: distances

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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 for this ? Any help will be much

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 i calculated", header=TRUE, sep=" ") var_HC<-hclust(as.dist(var),method="average") var_dendro<-as....

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 ... Thanks in advance, Nicolas BOUGET

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 said that you have to use euclidean distance between the initial observations. However i have found this: http://resear...

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 of the "dist" function

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"

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

...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 (Y)...

...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.

...o 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 that it is a/ usually used with data taht have observations in terms of a number of variables (hence, not pariwise distances); b/ it is not well suited for large multidimensional spaces (I have a distance matrix of 200*200 observations, a part of this could be used as training data, but still, we are looking at say 50 distances per observation). Thanks Martin [[alternative HTML version deleted]]

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, -3.1499400, -1.7307950, 2.8221209,

...een midpoints (it quickly became apparent that this is totally wrong when looking at the plot). So, I thought that perhaps finding the minimum distance between each of the lines endpoints AND their midpoints would be a good proxy for this, so I set up a loop that uses pythagoras to work out these 9 distances and find the minimum. But, this solution is obviously flawed as well (sometimes lines actually intersect, sometimes the minimum distances are less etc). Any help/dection on this one would be much appreciated. Thanks in advance, Darcy.

I'm very new 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

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 =...

hi all ,? i have an problem in script R . But when I execute the script R I face this error . can you help me please ???error:----------------------------------------- Error in FUN(X[[i]], ...) :?? Theme element 'text' has NULL property: margin, debugIn addition: Warning messages:1: Removed 361 rows containing non-finite values (stat_smooth).?2: Removed 361 rows containing missing values

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 and...

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 mahalanobi...

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 HTML version deleted]]