similar to: Error in hclust?

Dear UseRs,i want to find centroid of clusters, which i generated by hclust. Is there a way doing that? i took mean to elements in each cluster to get centroid but i am not sure if i am right? thanks in advanceeliza [[alternative HTML version deleted]]

Dear R users, I'm using Ecdf (Hmisc library) to plot four cdf in a same graphic. In this graphic I also plot the 0.99 quantile for these cdf. I successfully plot cdfs using different types of line to distinguish them, but I can't determine the type of lines showing 0.99 quantile. Is there a way to assign different line types for quantile lines in Ecdf plot? Best regards, -- Mateus da

Hello, a. when I use hclust with the methods media, centroid, and mcquitty, and plot the results, the dendrograms have lines that are crossing each other. Is this ok? b. My next question refers to plot.dendrogram: How can I use parameters as "hang" or "cex" here? E.g. for st <- as.dendrogram(subtreeshc[[x]]) I would like to have something like this, where cex and hang

Gives strange results. I get 'weird' dendrograms with canberra / binary distance metric and median / centroid cluster methods. Is this just my data? Dan

I use the following code to scan a (limited) parameter space of clustering strategies ... data <- read.table(... dataTranspose <- t(data) distMeth <- c("euclidean", "maximum", "manhattan", "canberra", "binary" ) clustMeth <- c("ward",

When using function heckit() from package ‘sampleSelection’, is there anyway to make t-tests for the coefficients using robust covariance matrix estimator? By “robust” I mean something like if a had an object ‘lm’ called “reg” and then used: > coeftest(reg, vcov = vcovHC(reg)). I’m asking this because in Stata we could use function heckman and then use vce option “robust”. We could do the

Hi, How can I accomplish this in R. Example: I have the following data.frame: data <- data.frame(x=c(1,2,3,4,5,6,5,3,7,1,0,4,8),y=c(1,2,1,2,2,2,1,1,1,2,2,2,2),z=c(5,8,4,3,4,1,6,3,3,6,3,5,7)) Supposing that data$y is a factor, I would like to find the Spearman correlation between data$x and data$z indexing it by data$y. To be more specific, I want to find two correlations: between x and z

Dear R community, I would be greatful if somebody could shed light on the following. I have created a set of 6 points to check how centroid agglomeration works in cluster analysis: > Y <- data.frame(x=c(-1,1,1,-1,10,12),y=c(1,1,-1,-1,0,0)) It is quite intuitive to understand that the last clusters to be joined will be {1,2,3,4} with {5,6}. Now, the centroid for the first cluster has

plot.hclust: Setting up a window for a dendrogram assumes the first link is the shortest and the last is the longest. This is not always the case when the clustering was done with hclust, method="median" or method="centroid", and the dendrogram sometimes doesn't fit within the window. I propose the fix listed below. src/main/ --- plot.c Wed Sep 17 01:03:39 2003 +++

Helo there. I am using kmeans of base package to cluster my customers. As the results of kmeans is dependent on the initial centroid, may I know: 1) how can we specify the centroid in the R function? (I don't want random starting pt) 2) how to determine the optimal (if not, a good) centroid to start with? (I am not after the fixed seed solution as it only ensure that the

There seems to be a bug in hclust (package mva) for clustering methods 'median' and 'centroid'. I have written a clustering program in C and discovered that the results for 'median' differ from those of hclust in R. I used a third program, written by someone else in Pascal, and that program agrees with the output of my program. I found yet another clustering program that

Hi, suppose that I have the following data.frame: cnae4 cnpj 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Y 24996 10020470 1 1 2 12 16 21 17 51 43 19 183 24996 10020470 69 91 79 92 91 77 90 96 98 108 891 36145 10020470 0 0 0 0 2 83 112 97 91 144 529 44444 10023333 5 20 60 0 0 0 0 5 20 1000 1110 I would like to create a new variable X that indicates which

I have obtained shapefiles for Indian states from here: http://www.maptell.com/index.php?option=com_remository&Itemid=159&func=fileinfo&filecatid=115&parent=category Problem: I want to extract centroid coordinates for each State, but there is some coding problem with the shapefiles that prevents this. #Code: #After extracting the shapefiles from the india_state.zip file, then:

Dear Help List, Thanks in advance for reading...I hope my questions are not too ignorant. I have an experiment looking at evolution of wing size [centroid] in fruitflies and the effect of 6 different experimental treatments [treatment]. I have five replicate populations [replic] in each treatment and have reared the flies in two different temperatures [cond] to assay the wing size, making

Hello, I need to calculate the centroids of some spatial polygons that I have placed into a Polygons-class object. Is the labeling point in the Polygons-class the centroid of the polygon? Thank you for your help.

Does anyone know how hclust (stats) calculates centroid linkage if only a distance matrix can be used as the input? ...Tao

Mateus, here is a more complex (working !) example : vfs objects = recycle recycle:name = .recycle ; max-size (in bytes) of files allowed in the recycle bin recycle:maxsize = 2000000 ; keep directory trees ? recycle_keeptree = True ; files to exclude from the bin recycle:exclude = *.tmp *.temp *.swp ; root dirs to exclude from the bin recycle:exclude_dir = tmp ; include file versionning in the

Hi, I'm trying to make a partial plot with package randomForest in R. After I perform my random forest object I type partialPlot(data.rforest, pred.data=act2, x.var=centroid, "C") where data.rforest is my randomforest object, act2 is the original dataset, centroid is one of the predictor and C is one of the classes in my response variable. Whatever predictor or response class I

Hey Parth, Thanks for the reply. I am considering implementing a cosine distance metric too, along with euclidian distance because of the dimensionality issue that comes in with K-Means and euclidian distance metric. That does help when we deal with sparse vectors for documents. The particular problem I'm having is representing centroids in an efficient way. For example, when we find the mean

Hello, I've been working on the KMeans clustering algorithm recently and since the past week, I have been stuck on a problem which I'm not able to find a solution to. Since we are representing documents as Tf-idf vectors, they are really sparse vectors (a usual corpus can have around 5000 terms). So it gets really difficult to represent these sparse vectors in a way that would be