similar to: Kmeans cluster analysis

Hi, I am a beginner user of R. I have a trivial question ? I am almost ashamed I cannot figure it out does not matter how many times I am reading the help. I have a table in .txt format, tab delimited. I can read it with ?read.delim()? with no problems. Afterwards I would like to use boxplot function to see if there are any outliers in the column 5 of my data called TPAH16.ppm In the

Hello again, There is any package which does Z aware (real 3D) interpolations? It can be any method (IDW, kriging or spline) but it should take into consideration not only x and y coordinates for interpolation, but also z coordinate. I looked into different packages but it seems i didn't find the right one. The ultimate goal is to import the output into a GIS (Geographical Information

Hi, I am trying to increase the memory R can use. I am running R under Windows on a machine with 2 GB of physical RAM and 4GB of paged memory. I wrote in the R property windows --sdi --max-mem-size=4094M, but the R itself when it is doing a bayesian modelling (geoR) it stops at 1,096K and i get memory errors because it cannot allocate a new segment of about 500K of memory. I don't have

Hi, Thank you so much for all your rapid answers. I am impressed. What i didn't know was that i have to assign my data to an object to work further on. It was not clear from the help (at least for me) that 'data()' itself is calling data already in R packages. All of you make that clear. Now, if you can suggest any good package to use for identifying outliers it will be great

Hi, I would like to write a little automatic routine in R, but i am a too much of a beginner for that. I will appreciate any help regarding this particular problem. Let?s suppose I have a data.frame with j columns (from 1 to n) and i rows (from 1 to p). I would like to write a procedure which reads every column j (j from 1 to n) and compare each value with the interval [0,1]. If z(i,j) is

Hi everybody, I would like to plot only a part of a data frame, and identify the IDs for all the points with values higher than a certain value. I will try to be more explicit: I did a qqnorm plot of my data. It is obvious from the plot that all the sample quantiles which are in Theoretical Quantiles = or > than 1 belong to another group. To be sure about that I plot the cooks.distance

Hi, Very so often when i am plotting something, doing a histogram, or whatever i am struggling to find out which are the numbers for different colors, palette names, types of lines, symbols, etc. Is there any documentation on line with all these numbers / names and the associated symbol / color??? For example if i am using the command image it uses a palette from red to yellow, with red the

hi y'all, I am wondering if there is any special command, function, package, etc to help me doing a cumulative distribution function, with y-scale - probability scale. I tried the help in R and i got the following answers: cumsum(base) Cumulative Sums, Products, and Extremes ecdf(stepfun) Empirical Cumulative Distribution Function cpgram(ts) Plot

Hi, First of all - thank you for the answers regarding shared library. If i am attempting to translate the library from Linux to Windows - i think i will need some heavy baby-sitting ;-))) It is way out of my league - for now. Meanwhile i have this new question: suppose i have a data.frame with x and y columns and 10 rows, 1 to 10. I also have a variable m (or an array if you like) with 5

Hi Again, First of all thank you for all the responses to my previous query. Your answers were very helpful and I did the job ;-). Now I hope you can answer as quick the following (sorry I am invading you with trivial questions): Let?s use again the following data.frame example: DF <- data.frame(x=rnorm(5), y=rnorm(5)) I want to obtain a new data.frame (or matrix) that contains only n

Hi y'all, First of all many thanks to Christian, Petr and Spencer for their replies. Your answers helped me to learn few more triks since then ;-) Now i have a question relating "aov". When i use aov i end up with 4 plots. How do i "see" the data behind those plots? I know about summary - but this gives me only some statistical info. Also, if i want to identify which

Hi All, A question from a newbie using R 2-5-0 on windows XP. Why is it that kmeans clustering with apparently the exact same parameters behaves so differently between the two following examples : > cl1 <- kmeans(subset(pointsUXO15555, select = c(2:4)), 10) Takes about 2 seconds to deliver a result > cl1 <- clust(subset(pointsUXO15555, select = c(2:4)), k=10,

Hello. Is it possible to choose the distance in the kmeans algorithm? I have m vectors of n components and I want to cluster them using kmeans algorithm but I want to use the Mahalanobis distance or another distance. How can I do it in R? If I use kmeans, I have no option to choose the distance. Thanks in advance, Arnau.

Hi everybody, I am using quite frequently the "robust" package and until now i never had any problems. Actually last time i used it was last Friday very successfully. Anyway, today anytime i want to use the function "fit.models" i get the following error even if i use the example form the help file: data(woodmod.dat) woodmod.fm <- fit.models(list(Robust =

Hi All, I was using the following command for performing kmeans for Iris dataset. Kmeans_model<-kmeans(dataFrame[,c(1,2,3,4)],centers=3) This was giving proper results for me. But, in my application we generate the R commands dynamically and there was a requirement that the column names will be sent instead of column indices to the R commands.Hence, to incorporate this, i tried using the R

Hello, I'd like to classify data with kmeans algorithm. In my case, I should get 2 clusters in output. Here is my data colCandInd colCandMed 1 82 2950.5 2 83 1831.5 3 1192 2899.0 4 1193 2103.5 The first cluster is the two first lines the 2nd cluster is the two last lines Here is the code: x = colCandList$colCandInd y = colCandList$colCandMed m = matrix(c(x, y),

Regarding a previous question concerning the kmeans function I've tried the same example and I also get a strange result (at least according to what is said in the help of the function kmeans). Apparently, the function is disregarding the initial cluster centers one gives it. According to the help of the function: centers: Either the number of clusters or a set of initial cluster

Dear R users, When I run the following code, R crashes: require(cclust) x <- matrix(c(0,0,0,1.5,1,-1), ncol=2, byrow=TRUE) cclust(x, centers=x[2:3,], dist="manhattan", method="kmeans") While this works: cclust(x, centers=x[2:3,], dist="euclidean", method="kmeans") I'm posting this here because I am not sure if it is a bug. I've been searching

Hi there I have been using R to perform kmeans on a dataset. The data is fed in using read.table and then a matrix (x) is created i.e: [ mat <- matrix(0, nlevels(DF$V1), nlevels(DF$V2), dimnames = list(levels(DF$V1), levels(DF$V2))) mat[cbind(DF$V1, DF$V2)] <- DF$V3 This matrix is then taken and a distance matrix (y) created using dist() before performing the kmeans clustering. My query

Hi, I am using kmeans to cluster a dataset. I test this example: > data<-matrix(scan("data100.txt"),100,37,byrow=T) (my dataset is 100 rows and 37 columns--clustering rows) > c1<-kmeans(data,3,20) > c1 $cluster [1] 1 1 1 1 1 1 1 3 3 3 1 3 1 3 3 1 1 1 1 3 1 3 3 1 1 1 3 3 1 1 3 1 1 1 1 3 3 [38] 3 1 1 1 3 1 1 1 1 3 3 3 1 1 1 1 1 1 3 1 3 1 1 3 1 1 1 1 3 1 1 1 1 1 1 3