similar to: Why daisy() in cluster library failed to exclude NA when computing dissimilarity

Hi, I'm trying to apply the function daisy() to a data.frame 10000x10 but I have not enough space (error message: cannot allocate vector of length 1476173280). I didn't imagine I was not able to work with a matrix of just 10000 observations... I have setted in Rgui --max-mem-size=2G (I'm not able to set more space..) How can I solve this issue? Separating observations depending on

Hi All - I am using the daisy function from the cluster library to create a dissimilarity matrix. I'm going to use that matrix to run a cluster analysis. My participants are identified with the variable, hhid. However, when I try to keep hhid in the dataset that I use to create the dissimilarity matrix, daisy uses it to create the matrix rather than ignoring it as an ID variable. I need to

Dear David, you asked this question a while ago on the R mailing list and got no answer. I have the same problem and was wondering if you had found a solution Cheers Loic Loic Thibaut, PhD candidate, ARC Centre of Excellence for Coral Reef Studies, School of Marine Biology, James Cook University, Townsville, Qld, 4811, Australia. Tel + 61 747 815 735, Fax: + 61 747 251 570, email:

Hello, I would like to use my own dissimilarity matrix in a PAM clustering with method "pam" (cluster package) instead of a dissimilarity matrix created by daisy. I read data from a file containing the dissimilarity values using "read.csv". This creates a matrix (alternatively: an array or vector) which is not accepted by "pam": A call

Hi, I already have a dissimilarity matrix and I am submitting the results to the elbow.obj method to get an optimal number of clusters. Am I reading the below output correctly that I should have 17 clusters? code: top150 <- sampleset[1:150,] {cluster1 <- daisy(top150 , metric = c("gower") , stand = TRUE , type = list(symm

Hi folks, I tried for the first time hclust. Unfortunately, with missing data in my data file, it doesn't seem to work. I found no information about how to consider missing data. Omission of all missings is not really an option as I would loose to many cases. Thanks in advance Holger -- View this message in context:

I try to perform a clustering using an existing dissimilarity matrix that I calculated using distance (analogue) I tried two different things. One of them worked and one not and I don`t understand why. Here the code: not working example library(cluster) library(analogue) iris2<-as.data.frame(iris) str(iris2) 'data.frame': 150 obs. of 5 variables: $ Sepal.Length: num 5.1 4.9 4.7

Hello R-user community! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) Presently I try to run the function metaMDS (vegan) using an existing dissimilarity-matrix. As I would like to start with this matrix I thought I could just give the matrix using the x= -argument Test<-metaMDS(x=Dist.Gower) Fehler in inherits(comm, "dist") :

Hello, I am running DAISY in R and using the GOWER metric since I am working with mixed variables. I am wondering if there is a way to weight the different variables. I see that there is a weight value for Gower but do not know if this is how to weight the diffrent variables with different weighting values. Please advise if there is a way to weight the different variables. Thank you. -- View

Hi, First I should note I am relatively new to R so I would appreciate answers that take this into account. I am trying to perform an MDS ordination using the function ?metaMDS? of the ?vegan? package. I want to ordinate species according to a set of functional traits. ?Species? here refers to ?sites? in traditional vegetation analyses while ?traits? here correspond to ?species? in such

Hello, I want to make a fuzzy classification from a dissimilarity matrix (calculated with daisy from package 'cluster'). I have tried to use fanny (package cluster) but I have the same problems than described in a previous message (http://tolstoy.newcastle.edu.au/R/help/05/05/4546.html) i.e. it always gives me two clusters in the results (even if k is different from 2) with the same

Hi, I'm trying to use a similarity matrix (triangular) as input for pam() or fanny() clustering algorithms. The problem is that this algorithms can only accept a dissimilarity matrix, normally generated by daisy(). However, daisy only accept 'data matrix or dataframe. Dissimilarities will be computed between the rows of x'. Is there any way to say to that your data are already a

Hi all, I'm trying to perform a hierarchical clustering on some dissimilarity data that I have but the data matrix I have already contains the dissimilarity values. These values are calculated using a separate program. The dissimilarity matrix in complete with no missing values but the hclust, and agnes routines require it in the form produced by daisy or dist. Is there any of converting

I have a data which contain some NA value in their elements. What I want to do is to **perform clustering without removing rows** where the NA is present. I understand that `gower` distance measure in `daisy` allow such situation. But why my code below doesn't work? __BEGIN__ # plot heat map with dendogram together. library("gplots") library("cluster")

Hi, I'm a PhD student and I want to use the function 'daisy' from the package 'cluster' to compute dissimilarities. My variables are of mixed types so I use the argument 'stand' in daisy to define the type of my variables. I have the following error message : Warning message: binary variable(s) 13, 16, 17, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,

Hi there fellow R users, R has many different clustering packages (e.g. mclust,cluster,e1071). However, can anyone recommend a method to deal with data sets that contain categorial and continuous variables? 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)

Hi the list, I need to compute Gower distance between a specific individual and all the other individual. The function DAISY from package cluster compute all the pairwise dissimilarities of a population. If the population is N individuals, that is arround N^2 distances to compute. I need to compute the distance between a specific individual and all the other individual, that is only N

Hi all, I have a dataset with numeric and factor columns of data which I developed a Gower Dissimilarity Matrix for (Daisy) and used Agglomerative Nesting (Agnes) to develop 20 clusters. I would like to use the 20 clusters to determine cluster membership for a new dataset (using predict) but cannot find a way to do this (no way to "predict" in the cluster package). I know I can use

Hello Stephen, I am happy that you help me. Thanks a million. It is a good feeling that you confirm my assumption that dsvdis is not able to deal with missing data, because it says me that I am not completely incapable. Okay now I have the problem what to do. I used this function ´cause there is an option to weight columns differently what I haven´t found in other functions. But now I

I want to compute the distances in a mixed variable matrix using the Gower coefficient. I understand it is possible to calculate distances in a matrix with mixed variables using the dudi.pco command. How would this work? Jorine