similar to: Specify ID variable in daisy{cluster}

Dear all, sorry, this is for sure really basic, but I searched a lot in the internet, and just couldn't find a solution. The problem is to create new variables from a data frame which contains both individual and group variables, such as mean age for an household. My data frame: df hhid h.age 1 10010020 23 2 10010020 23 3 10010126 42 4 10010126 60 5 10010142

Hello ! I am attempting to switch from being a long time SAS user to R, and would really appreciate a bit of help ! The first thing I do in getting a large dataset (thousands of obervations and hundreds of variables) is to run a SAS command PROC CONTENTS VARNUM command - this provides me a table with the name of each variable, its type and length; then I run a PROC MEANS - for numerical

Hi dears, I expect that Mr Thomas Lumley will read this message. I have data from a complexe stratified survey. The population is divide in 12 regions and a region consist to and urban area and rural one. there to region just with urbain area. stratification variable is a combinaison of region and area type (urban/rural) In rural area, subdivision are sample with probabilties proporionnal to

I am trying to estimate multinomial logit models off of a .csv table in IDCASE IDALT format where I have ROWS HHID PERID CASE ALTNUM NUMALTS CHOSEN IVTT OVTT TVTT COST DIST WKZONE HMZONE RSPOPDEN RSEMPDEN WKPOPDEN.... 1 1 2 1 1 1 5 1 13.38 2.00 15.38 70.63 7.69 664 726 15.52 9.96 37.26 2 2 2 1 1 2 5 0 18.38 2.00

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

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

Dear R-helpers, First of all, a happy new year to everyone! I succesfully used the daisy function (from package cluster) to find which two rows from a dataframe differ by only one value, and I now want to come up with a simpler way to find _which_ value makes the difference between any such pair of two rows. Consider a very small example (the actual data counts thousands of rows): input

I am currently using the function daisy in package cluster to create a dissimilarity matrix because my multivariate dataset contain missing data and variables of various types including factors, symmetric and asymmetric binary and quantitative. This is a step prior to using pco within ecodist. There is a warning which comes twice ">NAs introduced by coercion" I've used

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 have two groups, and I want to find the dissimiarity between the members of the two groups. Since I have mixed level variables on the members, I opt for the daisy function in the cluster package. Let's pretend that the following represent my groups: x <- data.frame(sex=factor(c(1,0,0,1,0,1), levels=0:1, labels=c('Male','Female'), ordered=FALSE),

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

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, 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