similar to: agnes not working

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, In the documentation for agnes in the package 'cluster', it says that NAs are allowed, and sure enough it works for a small example like : > m <- matrix(c( 1, 1, 1, 2, 1, NA, 1, 1, 1, 2, 2, 2), nrow = 3, byrow = TRUE) > agnes(m) Call: agnes(x = m) Agglomerative coefficient: 0.1614168 Order of objects: [1] 1 2 3 Height (summary): Min. 1st Qu. Median Mean 3rd

Hi, this is rather a (presumed) bug report than a question because I can solve my personal statistical problem by working with hclust instead of agnes. I have done a complete linkage clustering on a dist object dm with 30 objects with agnes (R 1.8.0 on RedHat) and I want to obtain the partition that results from a cut at height=0.4. I run > cl1a <- agnes(dm, method="complete")

Hi, this is rather a (presumed) bug report than a question because I can solve my personal statistical problem by working with hclust instead of agnes. I have done a complete linkage clustering on a dist object dm with 30 objects with agnes (R 1.8.0 on RedHat) and I want to obtain the partition that results from a cut at height=0.4. I run > cl1a <- agnes(dm, method="complete")

Hello, I had a question regarding clustering using the agnes() function from the 'cluster' package. I was wondering if anyone knew how I can identify cluster points after running the agnes function. For example, I created a dataset with points randomly scattered around (0,0), (0,1) and (1,0). After clustering, the dendrogram shows all the clustered points and I get the ordering and

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

This message is in MIME format. Since your mail reader does not understand this format, some or all of this message may not be legible. ------_=_NextPart_000_01C35A53.75780090 Content-Type: text/plain; charset="iso-8859-1" Anyone have a clue why hclust() and agnes() produce different results in the example below when both use method="average"?? I'm not able to reproduce

Hi, I'm mighty new to R. I'm using it on Windows. I'm trying to cluster using a distance matrix I created from the data on my own and called it D10.dist. I loaded the cluster package. Then tried the following command... > agnes("E:D10.dist", diss = TRUE, metric = "euclidean", stand = FALSE, > method = "average", par.method, keep.diss = n < 1000,

Hi, I'm trying to use the cluster package and I'm having some trouble... I always get the message: > myagnes <- agnes("datafile.dat") > Error: could not find function "agnes" the package cluster is listed in the library() command, and I can reach the help files from Agnes as well I know that this can be some really easy thing to fix, but right now I have no

Dear R users, I discovered something strange using the function agnes() of the cluster package on R 3.0.1 and on R 2.14.1. Indeed, the clusterings obtained are different whereas I ran exactly the same code. I quickly looked at the source code of the function and I discovered that there was an important change: agnes() in R 2.14.1 used a FORTRAN code whereas agnes() in R 3.0.1 uses a C code.

I haven't read the book, but could anyone explain more about this parameter? help(agnes) says that ac measures the amount of clustering structure found. From the definition given in help(agnes.object), however, it seems that as long as the dissimilarity of the merger in the final step of the algorithm is large enough, the ac value will be close to 1. So what does ac really mean? Thank

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 compiled R 2.1.0 under Windows XP SP2 as a preliminary to rebuilding a custom package for use with R 2.1.0. The compile completed successfully, and I was able to run demo(graphics) successfully. But make check and make check-recommended fail. > version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 2 minor 1.0 year 2005

Hi, I'd like to plot a tree with images of molecular structures instead of labels (words). I think this is possible because someone who worked in my office before I arrived did this. However I'm not sure if this person made the image manually or plotted it only with R. Thanks in advance for your help. -- View this message in context:

I'm fairly new to the silhouette functionality in the cluster package, so apologize if I'm asking something naive. If I run the 'agnes(ruspini)' example from the silhouette section of the cluster package vignette, and assign colours to clusters, two clusters have what appear to be incorrect colours in the silhouette plot. library(cluster) data(ruspini) ar<- agnes(ruspini)

Hi all, I am creating dendrograms using agnes and was wondering if it is possible to add color to the leaves (and just the leaves). For example, in the documentation, they have an example using the "votes.repub" data set. If I wanted to make the word "Washington" green (and only Washington), is it possible and if so how? I can apply "summary" to the object

I'm clustering rather large data sets and would like to cut the dendrograms to get a better view of specific components. I calculate the dissimilarity matrix using daisy() because I have a mixture of variable types: factors, ordered factors and numerical variables. If I want one dendrogram, I use agnes() for the agglomerative nesting and pltree() to draw the dendrogram. That way, I get the

Sorry for the repetition, unless I've got bad deja vu this questions been asked before but I couldn't turn up an answer on CRAN. Is there already any code in existence for local dependence measures such as Moran's I or Getis and Ord G? Also, S-Plus has a number of interstingly named Cluster Algorithms based on some previous stand-alone fortran algorithms (agnes, daisy etc.) which

Dear R-user, I am trying to use the function 'parLapply' from the 'snow' package which is supposed to work the same wys as 'lapply' but for a parallelized cluster of computers. The function I am trying to call in parallel is 'dudi.pca' (from the 'ade4' package) which performs principal component analyses. When I call this function on a list of

Dear R users, I am mailing you about the graphical output of silhouette (cluster package) From the example of silhouette in help(silhouette): > ar <- agnes(ruspini) > si3 <- silhouette(cutree(ar, k = 5), # k = 4 gave the same as pam() above + daisy(ruspini)) > plot(si3, nmax = 80, cex.names = 0.5) from which one may conclude that group 1 is composed by