search for: centroide

Hello all, I am wondering if there is a way to do a spatial error probit/logit model in R? I can't seem to find it in any of the packages. I can do it in MATLAB with Gibbs sampling, but would like to confirm the results. Ideally I would like to use this model to predict probability of parcel conversion in a future time period. This seems especially difficult in a binary outcome model

Hello, Can someone point me to documentation or ideas on how to calculate the centroids of clusters identified with hclust ? I would like to be able to chose the number of clusters (in the style of cutree) and then get the centroids of these clusters. This seems like a quite obvious task to me, but I haven't been able to put my hands on a relevant command. Thank you, Moritz

Dear WizaRds, although this might be a trivial question to the community, I was unable to find anything solving my problem in the help files on CRAN. Please help. Suppose I have 4 variables and want to use all possible combinations: 1,2 1,3 1,4 2,3 2,4 3,4 for a further kmeans partitioning. I tried permutations() of package e1071, but this is not what I need. Thank you for your help and

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 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

Hi all, I've got clusters and would like to match individual records to each cluster based on a sum of squares deviation. For each cluster and individual, I've got 50 variables to use (measured in the same way). Matrix 1 is individuals and is 25000x50. Matrix 2 is the cluster centroids and is 100x50. The same variables are found in each matrix in the same order. I'd like to

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.

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]]

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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

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

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 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

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

On Sat, Mar 12, 2016 at 04:27:55PM +0530, Richhiey Thomas wrote: > Below I write a raw version of my proposal for Clustering of Search Results > based on our previous mails. Hi, Richhiey. Thanks for putting this together ahead of the formal start of applications, and sharing it with us -- and it's really not too long! Project proposals for something that will last the summer are

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,

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

Dear everybody! In the function hclust, at each stage distances between clusters are recomputed by the Lance-Williams dissimilarity update formula according to the particular clustering method being used. Using "centroid" method, Lance-Williams recurrence formula works properly only for euclidean distance. How is it possible to use properly centroid method with manhattan distance ?

Hey, all, I'm trying to construct a centroid plot using canonical values from a MANOVA. I know that from the summary.manova object you can get Eigenvalues, and the H and E matrices (from SS$Treatment and SS$Residuals), but I am at a loss to get the loadings for the canonical values, nor values for the centroid centers and radii. Is there a package that does this that I am just missing,

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