similar to: best way to plot a evolution in time

Hi everyone, I made this set of boxplots that would show me the widths of some sites broken up by some chromosome, but I don't know how to make it indicate the number of data points that created the boxplot. How do I do that? boxplot(data$site~data$chr,varwidth='TRUE') -- View this message in context: http://r.789695.n4.nabble.com/boxplot-width-tp2266805p2266805.html Sent from the

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 <-

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, 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

Hi, I'm having some difficulties formulating this question. But what I want, is to extract the options associated with a parameter for a function. e.g. method = c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN") in the optim function. So I would like to have a vector with c("Nelder-Mead", "BFGS", "CG",

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:

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

Hi, Few days ago I have asked about spatial join on the minimum distance between 2 sets of points with coordinates and attributes in 2 different data frames. Simon Knapp sent code to do it when calculating distance on a sphere using lat, long coordinates and I've change his code to use Euclidian distances since my data had UTM coordinates. Typically one data frame has around 30 000 points

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]]

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

Hello devs, I have filled out the repo link on TRAC as suggested. I'll also keep the journal updated on TRAC from now on. I am almost done with defining all the base classes required for the clusterer and have started coding the euclidian distance metric. This should be completed by tomorrow after which I'll be spending one day to test and make sure everything functions as expected, so

Dear R users I have the matrix of the centres of some clusters, e.g. 20 clusters each with 100 dimentions, so this matrix contains 20 rows * 100 columns numeric values. I have collected new data (each with 100 numeric values) and would like to keep the above 20 centres fixed/'unmoved' whilst just see how my new data fit in this grouping system, e.g. if the data is close to cluster 1

Hello R-devel, Best wishes in the new year. I am writing to kindly request new R function so NA_real_ can be more easily detected. Currently if one wants to test for NA_real_ (but not NaN) then extra work has to be done: `is.na(x) & !is.nan(x)` Required functionality is already at C level so to address my request there is not that much to do. Kevin Ushey made a nice summary of current R C api

Dear all, I've sent this question 2 days ago and got response from Sarah. Thanks for that. But unfortunately, it did not really solve our problem. The main issue is that we want to use our own (manipulated) covariance matrix in the calculation of the mahalanobis distance. Does anyone know how to vectorize the below code instead of using a loop (which slows it down)? I'd really appreciate

Hi All, I have conducted the following survival analysis which appears to be OK (thanks BRipley for solving my earlier problem). > surv.mod1 <- survreg( Surv(timep1, relall6)~randgrpc, data=Dataset, dist="weibull", scale = 1) > summary(surv.mod1) Call: survreg(formula = Surv(timep1, relall6) ~ randgrpc, data = Dataset, dist = "weibull", scale = 1)

"nana" is meant to express "NA, really NA". Your suggestion sounds good. On Thu 2 Jan, 2020, 3:38 AM Pages, Herve, <hpages at fredhutch.org> wrote: > Happy New Year everybody! > > The name (is.nana) doesn't make much sense to me. Can you explain it? > > One alternative would be to add an extra argument (e.g. 'strict') to > is.na(). FALSE by

Dear CSAG R users, I will be glad if someone can point out what I am doing wrong or not doing at all in this. I am trying to write out netcdf file in R. I have 26 time step but only the first time step is written. For example: >library(ncdf) >path <- '/home/work/' >forecast <- open.ncdf(paste(path,'cam.1980.2005.nc',sep="")) > fore <-

Dear CSAG R users, I will be glad if someone can point out what I am doing wrong or not doing at all in this. I am trying to write out netcdf file in R. I have 26 time step but only the first time step is written. For example: >library(ncdf) >path <- '/home/work/' >forecast <- open.ncdf(paste(path,'cam.1980.2005.nc',sep="")) > fore <-

Dear LLVMers, we, at UFMG, have been building a large collection of compilable benchmarks. Today, we have one million C files, mined from open-source repositories, that compile into LLVM bytecodes (and from there to object files). To ensure compilation, we perform type inference on the C programs. Type inference lets us replace missing dependencies. The benchmarks are available at: