similar to: Please help regarding "NaN" in sets package

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

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

Dear R users, I have noted a difference in the merge distances given by hclust using centroid method. For the following data: x<-c(1009.9,1012.5,1011.1,1011.8,1009.3,1010.6) and using Euclidean distance, hclust using centroid method gives the following results: > x.dist<-dist(x) > x.aah<-hclust(x.dist,method="centroid") > x.aah$merge [,1] [,2] [1,] -3 -6

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]]

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.

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

Dear All, I am using a specific approach for my master thesis. In essence, a supervised reclassification is used as an intermediate step to find chemical parameters which are able to reclassify defined groups. These variables will be used in a next step where location and scale estimators of the groups are important. Traditionally linear discriminant analysis is used for reclassification which

hi all. I have a matrix of data with 5 different groups and 20 individual response per group, and about 12 variables collected for each. I want to represent the result in a 2D plot. PCA is not so good because the difference between the groups is not obvious. I have seen, in a recent paper, people doing a MANOVA and representing it in a centroid plot (they used Matlab to do it). I would like

Hi list! I'm computing multivar. distances from a set of centroids to a (large) set of individuals. I'm now just using rbind to create a matrix (x) with the centroid and the individuals, then run as.matrix(dist(x)) and finally select the appropriate columns, as I'm not interested on the distances among individuals. Therefore, this procedure implies a waste of computing time. Is there

I want to calculate the probability that a group will include a particular point using the squared Mahalanobis distance to the centroid. I understand that the squared Mahalanobis distance is distributed as chi-squared but that for a small number of random samples from a multivariate normal population the Hotellings T2 (T squared) distribution should be used. I cannot find a function for

Given a data set with a group factor, I want to translate the numeric variables to their centroid, by subtracting out the group means (adding back the grand means). The following gives what I want, but there must be an easier way using sweep or apply or some such. iris2 <- iris[,c(1,2,5)] means <- colMeans(iris2[,1:2]) pooled <- lm(cbind(Sepal.Length, Sepal.Width) ~ Species,

Hello, I'm Shivang Bansal, a 3rd year Computer Science Engineering undergraduate at Institute of Engineering & Technology in Lucknow, India. This mail is an expression of my interest for Google Summer of Code program of this year. I want to apologize for getting in so late. Actually I would have contacted earlier, but sudden demise of my Grandfather disabled me in doing so. I am

I am trying to use Fisher's z' transformation of the Pearson's r but the standard error does not appear to be correct. I have simulated an example using the R code below. The z' data appears to have a reasonably normal distribution but the standard error given by the formula 1/sqrt(N-3) (from http://davidmlane.com/hyperstat/A98696.html) gives a different results than sd(z). Can

Hello, I have carried out an lda analysis using the lda function of MASS package. I have plotted the LD1xLD2 to represent the data. Now I would like to get the centroids for each group of data and plot it on the LD1xLD2 graph. How can I get the centroid value from the lda object? Best, Dani -- Daniel Valverde Saub? Grup de Biologia Molecular de Llevats Facultat de Veterin?ria de la

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