similar to: how to extract options for a function call

the first line of dist() says if (!is.na(pmatch(method, "euclidian"))) shouldn't that be "euclidean" ? --------------------- R version 2.5.1 (2007-06-27) i486-pc-linux-gnu locale:

Hi All, Just want to make sure, in function "bclust", do the following argument only have one option? argument "dist.method" has one option "Euclidian"; argument "hclust.method" has one option "average"; argument "base.method" has one option "kmeans". Thank you! [[alternative HTML version deleted]]

I got a vector of probabilities like, probs<-c(0.001,0.5,0.02,1,.....) Is there any nice and easy builtin function to get the number of occurences within some specified probabality range. Like with 2% it would be occur[1] = sum(probs[probs>0&probs<0.02]) occur[2] = sum(probs[probs>0.02&probs<0.04]) ... occur[50] =sum(probs[probs>0.09] & probs<1) (If it was a

Hi I got a script that works on file with the extension ".qed" that are al located in a folder '~/works/' Is there are R function that will fetch all the filenames from the works subdir, similar to 'ls ~/works/*.qed' Thanks in advance

Hi, I am new to clustering and was wondering why pvclust using "maximum" as distance measure nearly always results in p-values above 95%. I wrote an example programme which demonstrates this effect. I uploaded a PDF showing the results Here is the code which produces the PDF file: ------------------------------------------------------------------------------------- s <-

Dear All, I am having trouble in using R function "constrOptim" to do constraint optimization. It seems that "constrOptim" calls function "optim" when it does the optimization, and "optim" allows us to set "method" to be "SANN" if we want to use simulated annealing. In "optim", the function allows us to set gradient to be

I think SANN method in optim() is failing to report that it has not converged. Here is an example genrose.f<- function(x, gs=NULL){ # objective function ## One generalization of the Rosenbrock banana valley function (n parameters) n <- length(x) if(is.null(gs)) { gs=100.0 } fval<-1.0 + sum (gs*(x[1:(n-1)]^2 - x[2:n])^2 + (x[2:n] - 1)^2) return(fval) }

Hello, Thanks James for the reply. That cleared a few things out. Apologies for replying late because of exams going on. I was going through the previous clustering API to understand how it worked and it seems like the the approach for construction of the termlists which are used for distance metrics use TF-IDF weighting with cosine similarity, which is very similar to the approach I would need

Hi Everyone, I'm trying to minimize a function using constrOptim with the simulated annealing method SANN. If I understand constrOptim well, it basically passes most of its arguments to optim while somehow enforcing the constraints. My problem is, that since SANN does not need gradients, when using optim with SANN, the gr argument of optim is used to specify a function to create the next

Hi, I'm trying to build a thesaurus that will sensible values for rare words. I suspect the best algorithm to use is k-means although I'm not sure about that -- I would have preferred a k dimensional space with a binary cluster in each dimension so a word can belong to 0..k clusters, but I digress... I can measure the strength of correlation between words fairly easily by counting

Is there a function that calculate the mahalanobis distance in R . The dist function calculates "euclidean"', '"maximum"', '"manhattan"', '"canberra"', '"binary"' or '"minkowski"'. Thanks ../Murli

Hi all I am trying to solve a combinatorial optimization problem. Basically, I can reduce my problem into the next problem: 1.- Given a NxN grid of points, with some values in each cell 2.- Find the combination of K points on the grid such that, the maximum mean value is obtained I took the Travel SalesMan problem example in ?optim documentation. I am not sure if I have understood correctly

Dear R users, I am running a maximum likelihood model with optim. I chose the simulated annealing method (method="SANN"). SANN is not performing bad, but I guess it would be much more effecive if I could set the `parscale' parameter. The help sais: `parscale' A vector of scaling values for the parameters. Optimization is performed on `par/parscale' and these

Dear R community, I am analyzing plant communities with the function mvpart, using a dissimilarit matrix as input. The matrix is calculated with the funtion gdist. fit <- mvpart(gdist (ba12[,18:29], meth="maximum", full=TRUE, sq=F) ~ beers + slope_dem + elev_dem+ plc_dem + pr_curv+ +curv+max_depth+doc_rocks+ abandon+land_use+ca_old, data=ba12, xv="p") This

Hello List I'm working on a combinatoric problem in which the object is to minimize the badness() of a vector. I think this class of problem is only soluble by optim() using method=SANN. The badness() of anything is >= 0, and when I've found a solution with zero badness, I want optim() to stop (carrying on beyond zero badness cannot improve the solution). Efficiency is crucial here.

Hello: I'm trying to use Rcmd SHLIB to compile a single file, sann.c, to get sann.dll. I was able to get make libR.a to work, after going into MkRules and changing the line DLLTOOL=$(BINPREF)dlltool -k --as $(AS) to read DLLTOOL=C:/MINGW-1.1/bin/dlltool -k --as $(AS) But now I get: C:\rw1051\src\gnuwin32>Rcmd SHLIB sann.c make: make: Command not found make: *** [libR] Error 127

Hello, I''m trying to organize a Bay Cities Ruby Group in Southern California. This would include the cities of Redondo Beach, Manhatten Beach, Hermosa Beach, Torrance, Gardena, Lomita, etc. If you are interesting in joining a group that would be like other ruby groups where we discuss tricks, technique, theory, etc. and help each other, then please join the mailing list:

As an alternative to polling the client, as Ryan describes, you could consider piggy-backing the status updates on the back of other ajax responses. Which way you go depends entirely on the nature of your app, in particular: 1. how frequently it generates ajax traffic anyway 2. how long the server-side process is going to take If the server-side process takes, say, 20 seconds, polling is a

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

I'm working on a project related to document clustering. I know that R has clustering algorithms such as clara, but only supports two distance metrics: euclidian and manhattan, which are not very useful for clustering documents. I was wondering how easy it would be to extend the clustering package in R to support other distance metrics, such as cosine distance, or if there was an API for