search for: jaccards

Hi I have a large dataset, containing a wide range of binary variables. I would like first of all to compute a jaccard matrix, then do a PCA on this matrix, so that I finally can do a hierarchical clustering on the principal components. My problem is, that I don't know how to compute the jaccard dissimilarity matrix in R? Which package to use, and so on... Can anybody help me? Alternatively

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 =

Does anyone have a good method for calculating Jaccard coefficients now that the dissimilarity() function is no longer an option? Wen Gu John Jay College of Criminal Justice445 West 59 StreetNew York, NY 10029 wgu@gc.cuny.edu _________________________________________________________________ Express your personality in color! Preview and select themes for Hotmail®.

Good morning, I am analyzing a dataset composed by 364 subjects and 13 binary variables (0,1 = absence,presence). I am testing possible association (co-presence) of my variables. To do this, I was trying with cluster analysis. My main interest is to check for the significance of the obtained clusters. First, I tried with the pvclust() function, by using method.hclust="ward" and

I have read everything I can find on how to manipulate a results matrix in R and I have to admit I'm stumped. I have set up a process to extract a dataset from ArcGIS to compute a similarity index (Jaccards) in Vegan. The dataset is fairly simple, but large, and consists of rows = sample area, and columns = elements. I've been able to view the results in R, but I want to get the results out to a database and a matrix that is 6000-rows x 6000-columns can be very difficult to manipulate in Windows...

hi, I want to do hierarchical clustering with Jaccord index. I tried to do with vegan package for finding index and hierarchical clustering with hclust function. While doing clustering it is showing an error message as "invalid distance method". I would be grateful if anyone tells how to rectify the error. Thanks in advance,   kind regards, Ms.Karunambigai M PhD Scholar Dept. of

Dear All, It's Eszter again from Hungary. I could not solve my problem form yesterday, so I still have to ask your help. I have a binary dataset of vegetation samples and species as a comma separated file. I would like to calculate the Jaccard distance of the dataset. I have the following error message: Error in rowSums(x, prod(dn), p, na.rm) : 'x' must be numeric In addition:

---------- Forwarded message ---------- From: <r-help-owner@r-project.org> Date: 2013/2/8 Subject: vegdist Error en double(N * (N - 1)/2) : tama?o del vector especificado es muy grande To: caro.bello58@gmail.com Message rejected by filter rule match ---------- Mensaje reenviado ---------- From: caro bello <caro.bello58@gmail.com> To: r-help@r-project.org Cc: Date: Fri, 8 Feb 2013

Hello R, could you explain to me how to resolve this question: If this is a matrix: Element S1 S2 S3 S4 0 0 1 0 1 1 0 1 0 0 2 1 0 0 1 3 0 0 1 0 4 0 0 1 1 5 1 0 0 0 1. How is possible to ompute the minhash signature for each column if we use the following three hash functions:

...> I have read everything I can find on how to manipulate a results matrix in <http://tolstoy.newcastle.edu.au/R/e2/help/07/06/19875.html#19887qlink1> > R and I have to admit I'm stumped. I have set up a process to extract a > dataset from ArcGIS to compute a similarity index (Jaccards) in Vegan. The > dataset is fairly simple, but large, and consists of rows = sample area, and > columns = elements. I've been able to view the results in R, but I want to > get the results out to a database and a matrix that is 6000-rows x > 6000-columns can be very difficult to...

Hi, I'm trying to use adonis on a subset of data from a dataframe. The actual data is in columns 5:118, and the first four columns are various factors. There are 3 levels of the factor Habitat, and I want to examine differences among only two of them. So I started with: > CoastNear = subset(gel_data, Habitat != "I") The resulting data.frame has three levels for Habitat, but

Dear All, I get some problems using the 'phylo' and dissimilarity functions in R. I converted an output from 'hclust' into an order of phylo so as to be able to use the 'consensus' function on it. Each time I submit the consensus codes, my computer hangs. When I tried to see what the contents of the object converted into order phylo is, I get the message

I am trying to cluster some binary data using k-means . As the regular "kmeans" available from stats package in R does'nt provide the option to change the distance method. I was wondering there is any package available to specify type of distance measure to be used in k means clustering in R. Especially distances like "Jaccard" which is good for binary data.

I am doing hierarchical clustering with cluster package.  I couldnot find similarity measures like matching coefficient, Jaccard coefficient and sokal and sneath. Could anyone please tell package with similarity measures for binary data? kind regards, Ms.Karunambigai M PhD Scholar Dept. of Biostatistics NIMHANS Bangalore India From cricket scores to your friends. Try the Yahoo! India

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

Dear R users, I'm interested in measuring the stability of a heirarchical clustering, of the overall clustering and finding sub clusters (from cutting the heirarchical clustering at different levels) which demonstrate stability. I saw some postings on the R help from a while back about bootstrapping for clustering (using sample and generating a consesus tree with a web based tool CONSENSE)

Dear All, It's Eszter from Hungary, a total beginner with R. My problem is the following: I have a dataset with binary values as a comma separated textfile. The samples are in the coloumns and the species are in the rows. I have to transpose it for the further PCoA analysis. There is no problem with reading the dataset. When I transpose the dataset, the original values become

Antes de nada, me gustaría daros las gracias por toda vuestra ayuda. He estado probando todo lo que me habéis dicho a la vez, y no hay manera, sigo teniendo el problema con el espacio. En cuanto al tamaño de la base de datos, es más grande de lo que puse, me equivoqué y puse el tamaño de una base anterior con la que estuve trabajando, la actual tiene 36866 filas x 6500 columnas. He seguido todas

Hi all, I want to conduct a cluster analysis with quantitative variables. More precisely, it concerns binary and non-ordered categorical variables. For such data, various similarity measures have been proposed, such as the Jaccard index or the simple matching index. So, is there a package such as mva or multiv in the case of quantitative variables? Could you indicate me reviews, papers or

Hello, I have a contingency table showing relation between two datasets. I tried to see association among them with the assocplot, but it shows error. mosaicplot of the same data worked perfectly. Can anyone please help me. Con.table=as.matrix(Con.table) > dim(Con.table) [1] 27 27 > assocplot(Con.table,space = 0.3, + main = NULL, xlab = NULL, ylab = NULL) Error in