similar to: A question about using function plot

without having to check the vector element by element? Thanks a lot! Jason ===== Jason G. Liao, Ph.D. Division of Biometrics University of Medicine and Dentistry of New Jersey 335 George Street, Suite 2200 New Brunswick, NJ 08903-2688 phone (732) 235-8611, fax (732) 235-9777 http://www.geocities.com/jg_liao

Hello, I'm new to R, I've been working with it for the last 2 weeks. I am plotting some data and not getting the labels on the x axis I am expecting on my plot. my code reads #hours in the day h <- c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23) #hp is a data frame with a pivot table of 25 columns (label and data for 24 hours) plot(h, as.matrix(hp[1,2:25]),

I have a program which needs to compute squared Euclidean distance between two vectors million of times, which the Rprof shows is the bottleneck. I wondered if there is any faster way than my own simple function distance2 = function(x1, x2) { temp = x1-x2 sum(temp*temp) } I have searched the R-help archives and can not find anything except when the arguments are matrices. Thanks for any

I am excited to learn that Prof. Tierney is bringing to us compiled R. I would like to learn when it will be available. This information will be useful in scheduling some of my projects. Thanks. Jason Jason Liao, http://www.geocities.com/jg_liao Dept. of Biostatistics, http://www2.umdnj.edu/bmtrxweb University of Medicine and Dentistry of New Jersey 683 Hoes Lane West, Piscataway‚ NJ 08854

I recently coded a recursion algorithm in R and ir ran a few days without returning any result. So I decided to try a simple case of computing binomial coefficient using recusrive relationship choose(n,k) = choose(n-1, k)+choose(n-1,k-1) I implemented in R and Fortran 90 the same algorithm (code follows). The R code finishes 31 minutes and the Fortran 90 program finishes in 6 seconds. So the

My coauthor made a new R package GeneLogit (100% R code) which installs ok on R 2.3 on Windows. But when I type library(GeneLogit) it gave the error message Error in library(GeneLogit) : 'GeneLogit' is not a valid package -- installed < 2.0.0? It runs on R 1.9.0 just fine. It seems that others have encountered same problem but no solution is found by googling How can I fix this

I have been using a laptop computer of Pentium III 1.13 Ghz. I heard that AMD's Athlon has excellent floating point capacity. So I bought a Athlon 2200+ laptop yesterday. I expected that new Athlon 2200+ will be twice as fast as the P III 1.13 GB. I ran a R simulation program and the new computer is only 30% faster, in fact slightly slower than a Celeron 1.50 GB laptop. I am very disappointed

I did some serach around. It seems that ROC curve computation is not supported on R. Anyone has some leads? Thanks. Jason ===== Jason G. Liao, Ph.D. Division of Biometrics UMDNJ School of Public Health 335 George Street, Suite 2200 New Brunswick, NJ 08903-2688 phone (732) 235-9748, fax (732) 235-9777 http://www.geocities.com/jg_liao __________________________________________________ Great

I have been comparing R with other languages and systems. One peculiar feature of R is there is no scalar. Instead, it is just a vector of length one. I wondered how much performance penalty this deign cause, particular in situations with many scalars in a program. Thanks. Jason Liao, http://www.geocities.com/jg_liao Associate Professor of Biostatistics Drexel University School of Public

1. allows underscore as part of a variable name 2. Uses C or Java style comments mark. Jason ===== Jason G. Liao, Ph.D. Division of Biometrics University of Medicine and Dentistry of New Jersey 335 George Street, Suite 2200 New Brunswick, NJ 08903-2688 phone (732) 235-8611, fax (732) 235-9777 http://www.geocities.com/jg_liao __________________________________________________ HotJobs - Search

Hello, everyone! I guess no one is still using R on Sun Sparc these days. But our department has a (pretty new) two-CPU Sun server. We recently compiled R as a 64 bit application and expected a performance boost. But it runs 25-30% slower than the 32 bit version of R. Anyone knows why this is so? Thanks! Jason ===== Jason Liao, http://www.geocities.com/jg_liao Dept. of Biostatistics,

Let A be a m by n matrix and b a length n vector. What is the fastest vectorized code for doing for(j in 1:n) A[, j] <- A[, j]/b[j] ? solution 1: t(t(A)/b) solution 2: B <- matrix( rep(b, m), byrow=T, nrow=m ) A/B anything else? I have a program that uses this kind of operation million of times and I appreciate your input. Thanks. Jason Liao ===== Jason G. Liao Department of

First, happy holidays, everyone! Thanks to the R team for bringing out 1.4 before the new year. I have 10000 integer triplets stored in A[1:10000, 1:3]. I would like to find the unique triplets among the 10000 ones with possible duplications. What is the easiest way for this. I know the function unique(), which apply to a vector, not the 10000*3 array in my problem. Thanks in advance. Jason

This is some kind of follow-up to my previous posts. I have further improved the speed of my program 6 times by eliminating all the apply(). It turns out that apply is slow, is slower than direct loop, it is an order slower than a matrix operation alternative. Here is one example. The first apply version runs 19 seconds, the second loop version runs 13 seconds, the third matrix version runs 1

I tried to modify the R function bw.SJ (from the stats package) for my own use. But if I just get the source code and run directly (without any modification), it can no longer find some key functions called within it. I understand this has something to do with searching path which I do not understand well. Can anyone tell me how to modify the source code of an R function and still make it part of

I tried to modify the R function bw.SJ (from the stats package) for my own use. But if I just get the source code and run directly (without any modification), it can no longer find some key functions called within it. I understand this has something to do with searching path which I do not understand well. Can anyone tell me how to modify the source code of an R function and still make it part of

Thanks to those who responded to my inquiry about generalized linear mixed models on R and S-plus. Before I summarize the software, I note that there are several ways of doing statistical inference for generalized linear mixed models: (1)Standard maximum likelihood estimation, computationally intensive due to intractable likelihood function (2) Penalized quasi likelihood or similar

Good morning! I recently implemented a KD tree in JAVA for faster kernel density estimation (part of the code follows). It went well. To hook it with R, however, has proved more difficult. My question is: is it possible to implement the algorithm in R? My impression seems to indicate no as the code requires a complete class-object framework that R does not support. But is there an R package or

If I use the following command to plot points: plot(c(1,2,2,3,3,3), type="p", pch=20, ylab="Y Label", xlab="X Label", xaxt="n") there is a large amount of space between the label "X Label" and the actual x-axis. If I change the xaxt="n" to xaxt="s", the label "X Label" don't move at all. Is there a way to get

Following the setup in Prof.Duncan Murdoch's page, I have successfully compiled the DLL for one Fortran 95 program using Gfortran and got 300 times speed boost. For the second set of fortran programs, However, I have this error message R CMD SHLIB -o jiangang kdtree2.f90 jian.f90 gang.f90 m2c -o jian.o jian.mod make: m2c: Command not found make: *** [jian.o] Error 127 Can anyone