similar to: plot a distance matrix

Hi All, I'm working on analyzing a large data set, lets asume that dim(Data)=c(1000,8700). I want to calculate the canberra distance between the columns of this matrix, and using a toy example ('test' is a matrix filled with random numbers 0-1): > system.time(d<-as.matrix(dist(t(test), method = "canberra", diag = FALSE, upper = FALSE, p = 2))) user system

Dear all, I have a problem when using some classification functions (Kmeans, PAM, FANNY...) with a distance matrix, and i would to understand how it proceeds for the positioning of centroids after one execution step. In fact, in the classical formulation of the algorithm, after each step, to re-position the center, it calculates the distance between any elements of the old cluster and its

Hi, I'm very new to R. I want to know if there is any way to write only the lower half of a distance matrix created in R to a csv file for example. I get the 'cannot coerce class "dist" into a data.frame' message when I try. I have used as.matrix and can write to a file this way, but as a full matrix. The reason I only want the lower half is that I've been doing some

Hello, I want do get the point to point distance matrix. Suppose, I have a data frame where the first column is X and second column is Y coordinate of points. I define a function dst(x1,y1,x2,y2) to claculate distance between two points. But I don't know how to tell R to calculate dst for every two points and create a matrix of these distances. Can you give me a little guidance, please? I am

Dear friends, I have install the last version 2.10 and now I have probles with 'mgcv' which can be load. I proved to eliminated and reinstalled but always obtain the same message: > library (mgcv) This is mgcv 1.6-1. For overview type `help("mgcv-package")'. Error in runif(1) : .Random.seed is not an integer vector but of type 'list' Error : .onAttach non

Hi, I'm trying to generate a distance matrix between sample pairs (example below). I'm not very familiar with the loop command which I expect I will need for this. The example below demosntrates what I'd like to get out of the data - essentially, to calculate the proportion of positions where two samples differ. Any help much appreciated! Also, any notes on how the functions work

Hi there, I have the problem, that I'm not able to reproduce the SPSS residual statistics (dfbeta and cook's distance) with a simple binary logistic regression model obtained in R via the glm-function. I tried the following: fit <- glm(y ~ x1 + x2 + x3, data, family=binomial) cooks.distance(fit) dfbetas(fit) When i compare the returned values with the values that I get in SPSS,

Hello, Im working on a 50933 point count bird abundance dataset. I've succeeded in calculating a distance matrix for this entire set, but I don't have sufficient memory to convert this to a matrix, as below... abun.dist <- dist(abun.mat[1:50993,1:235) test <- rowMeans(as.matrix(abun.dist)) Error in matrix(0, size, size) : too many elements specified ive been able to run a hclust()

Dear all, below you find enclosed my message from January 9th and my program (attention: beginner). Thanks for both answers! a. However, as far as I know the cosine is not the same as the Pearson correlation (only in special cases). b. Reid Huntsinger's hint was very useful, however I had to transpose the matrix first, for I want to calculate the distance of the _rows_. Regards, Petra

Hi all, Thanks for your help in generating the matrix of distance vs correlation. I did it using plot(as.vector(as.matrix(cormat)), as.vector(as.matrix(distmat))) Now I want to quantitate the same. May be on linear regression or some other statistical functions. I have tried using linmod for linear regression. But as I have two matrices in the form of the dataframes, I'm wondering if it

Dear R users I wondered if any of you ever tried to calculate distance matrix with very large data set, and if anyone out there can confirm this error message I got actually mean that my data is too large for this task. negative length vectors are not allowed My data size and code used dim(mydata_nor)[1] 365000 144> d <- dist(mydata_nor, method = "euclidean") Here my

Dear friends, I have several sets of points in a transformed environmental space. Each set of points can be represented as a cloud in the environmental space. This space is spanned by n coordinates, corresponding to the first n PCs of 36 PCs of some environmental variables (12 monthly minimum temperatures, 12 monthly maximum temperature, 12 monthly precipitations). I would like to calculate

Hi all, I've been struggling learning R and need to turn to the list again. I've got a dataset (comma-delimited file) with the following fields: recid, latitude, longitude, population, dwelling and age. For each observation, I'd like to calculate the total number of people and dwellings and average age within 2 k.m. Distance could be Euclidean, however, a proper distance

Dear all, We have a data frame x with n people as rows and k variables as columns. Now, for each person (i.e., each row) we want to calculate a distance between him/her and EACH other person in x. In other words, we want to create a n x n matrix with distances (with zeros in the diagonal). However, we do not want to calculate Euclidian distances. We want to calculate Mahalanobis distances, which

Hi, I am trying to calculate a distance matrix on a binary data frame using dist.binary() {ade4}. This is the code I run and the error I get: > sjlc.dist <- dist.binary(as.data.frame(data), method=2) #D = (a+d) / (a+b+c+d) Error in if (any(df < 0)) stop("non negative value expected in df") : missing value where TRUE/FALSE needed I don't know if the problem are the

I am trying to find the distance between a vector and each row of a dataframe. I am using the function "distancevector" in the package "hopach" as follows: mydata<-as.data.frame(matrix(c(1,1,1,1,0,1,1,1,1,0),nrow=2)) V1 V2 V3 V4 V5 1 1 1 0 1 1 2 1 1 1 1 0 vec <- c(1,1,1,1,1) d2<-distancevector(mydata,vec,d="euclid") The Euclidean distance

***********reading in data********** data<-read.table("microarray.txt",header=T, sep="\t") head(data) dim(data) attach(data) ***********creating matrix and calculating variance across probesets******** x<-1:20000 y<-2:141 data.matrix<-data.matrix(data[,y]) variableprobe<-apply(data.matrix[x,],1,var) hist(variableprobe) **************filter out low

Hi! I am trying to implement non-bipartite matching. I have around 500 sites which can be clustered by 10 regions. I am able to calculate pairwise Mahalanobis distances between sites (thanks to another post in the forum). However, I want to constrain my match to sites within the same region. Thus I want to replace elements of the distance matrix with a high value, say 999999, for sites not of the

Dear R-listers, Here, I would like to hearing helps from you. I have GPS data (multiple points in the geographic scale) in longitude/latitude. I intend to calculate distance (in kilometer) among such points and output the distance matrix in dist class. I have gotten some progress, but I still can not get final goal. Could please give me any directions/advice? This email cc. to Mr. Pierre, the

Hello, I have a self-defined function to be computed on each column in a matrix. The basic idea is to ignore the elements that have value of 0 during computation. I should be able to write my own function but it could be computational expensive, so I'd love to ask if anyone may have suggestions on how to implement it more efficiently. Thanks in advance. For example, there are three