similar to: agglomerative coefficient in agnes (cluster)

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

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 there, is there any R-function for k-nearest neighbour agglomerative hierarchical clustering? By this I mean standard agglomerative hierarchical clustering as in hclust or agnes, but with the k-nearest neighbour distance between clusters used on the higher levels where there are at least k>1 distances between two clusters (single linkage is 1-nearest neighbour clustering)? Best regards,

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")

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

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.

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 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'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:

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

Hello! I created a distances matrix for 13 objects using daisy (see the attached file). I am trying to clusteranalyse it using agnes but it's not working. What might be the problem: mydistances<-read.csv("Results of daisy.csv") mycluster<-agnes(mydistances, method="ward") I am getting: Error in agnes(mydistances, method = "ward") : NA/NaN/Inf in foreign

Dear all, I am no stat wiz and I am just trying to use the AGNES algorithm at my very modest level of statistical of understanding. I have difficulties understanding the ouput from AGNES. My question is: how to interpret the output, especially how do you I know which cluster solution is the best? In SPSS, an Agglomeration Schedule table is produced and I used to look at the biggest jump between

Dear R Users: I am new to R and I am trying to do a cluster analysis on a single continuous variable using the Agnes [Agglomerative Nesting (Hierarchical Clustering) ] in the Package ‘cluster’. I was able to apply this clustering method to my data: ward1 <- Agnes(balances, diss= FALSE, metric = "euclidean", stand = TRUE, method = "ward", keep.diss =TRUE, keep.data =

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

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

First off, thanks for the great library. Is there a way setup expects with this: runtime_options[:property] = properties_option_string or do i have to use: runtime_options.store(:property, properties_option_string) which results in this: runtime_options.expects(:store).with(:property, ''prop1=1'') Thanks for your help. jeremy

Hey group, I have a problem of drawing dendrogram as the result of my program written in C. My algorithm is a approximation algorithm for single linkage method. AS a result I will get the following data: [Average distance] [cluster A] [cluster B] For example: 42.593141 1 26 42.593141 4 6 42.593141 123 124 42.593141 4 113 74.244206 1 123 74.244206 4 133 74.244206 1 36 So far I have used C to

is that 'x' is level or trend stationary. I did this > s<-rnorm(1000) > kpss.test(s) KPSS Test for Level Stationarity data: s KPSS Level = 0.0429, Truncation lag parameter = 7, p-value = 0.1 Warning message: p-value greater than printed p-value in: kpss.test(s) My question is whether p=0.1 is a good number to reject N0? On the other hand, I have a

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