similar to: weighting variables using Gower with DAISY

I am using DIANA/DAISY/GOWER. Some of my categorical data include NULLS. When assessing disimilarity, these NULLS are considered similar. I do not want these NULLS to contribute towards similarity. Instead is there a way for these NULLS to each be considered different so as to contribute to disimiliarity and not simillarity? Also, I do not want to change these NULLS in the data as I need them for

Hi, According to daisy function from cluster documentation, it can compute dissimilarity when NA (missing) value(s) is present. http://stat.ethz.ch/R-manual/R-devel/library/cluster/html/daisy.html But why when I tried this code library(cluster) x <- c(1.115,NA,NA,0.971,NA) y <- c(NA,1.006,NA,NA,0.645) df <- as.data.frame(rbind(x,y)) daisy(df,metric="gower") It gave this

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

I am having difficulty getting the dynamic tree cut package to work. Given the data table "myddtable" LengthPlaceColorAge5HRed224ABlue205WGreen243GRed222GBlue236WGreen255ARed194H Blue23 I created a similarity matrix using DAISY and Gower metric and specified Place and Color columns as characters (since they are categorical variables) > dd.daisy<-daisy(myddtable, metric =

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

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")

Hello I have 2 rows in a matrix and I want to calculate the Gower Distance between the 2 , how can I do it? I searched and found nothing that can help me, and my program doesn't know the gdist function and I couldn't find it on the R help site. Can anyone help me plz Thank u all [[alternative HTML version deleted]]

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

Dear Rhelp,   I did a daisy on 5 lifestyle variables, 3 of which were nominal and 2 were ordinal and assigned types “nominal” and “ordinal” for the variables, respectively.  I got an output indicating their types as “I” for interval(?). Doing it on the Rdata example “flower” gave the same types in the output as the types they were assigned to.  Why is this so? Below are the codes and outputs.  

Hi, I'm trying to create a distance matrix. And it works out somewhat ok. However, I suspect that there are some efficiency issues with my efforts. Plz have a look at this: donor <- matrix(c(3,1,2,3,3,1,4,3,5,1,3,2), ncol=4) receiver <- matrix(c(1,4,3,2,4,3,1,5,1,3,2,1,4,5,3,5,1,3,2,4,5,1,2,3,1,4,5,5,1,2,1,3,4,3,2,5,5,1,4,2,5,4,3,2), ncol=4) The above creates my two matrices. I have

I am having difficulty getting the dynamic tree cut package to work. Given the data table "myddtable" LengthPlaceColorAge5HRed224ABlue205WGreen243GRed222GBlue236WGreen255ARed194H Blue23 I created a similarity matrix using DAISY and Gower metric and specified Place and Color columns as characters (since they are categorical variables) > dd.daisy<-daisy(myddtable, metric =

Hi, I'm using the cluster library to examine multivariate data. The data come from a connection to a postgres database, and I did a short R script to do the analisys. With the cluster version included in R1.8.0, daisy worked well for my data, but now, when I call daisy, I obtain the following messages: --------- Error in if (any(sx == 0)) { : missing value where TRUE/FALSE needed In

Alguém sabe onde encontro os comandos para utilizar em daisy() no pacote cluster o método de Gower para dados moleculares codominantes, quantitativos e multicategóricos "conjuntamente"? Agradeço. OBS.: confirmar por favor o recebimento da mensagem. #################################### Adésio Ferreira Universidade Federal do Espírito Santo Centro de Ciências Agrárias Departamento

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 List, I am looking for a variable selection procedure with a forward-backward selection method. Firstly, it is meant to work with the cophenetic correlation coefficient (CPCC) and intended to find the variable combination with the highest cophenetic correlation. Secondly, it is aimed at Gower metric with wards method (though this could be easily extended) aimed at categorical data. What I

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, I am a relatively new user of R. I have written a basic function to calculate the Gower similarity function. I was motivated to do so partly as an excercise in learning R, and partly because the existing option (vegdist in the vegan package) does not accept missing values. I think I have succeeded - my function gives me the correct values. However, now that I'm starting to use it with

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

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

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