similar to: Adding variables

Hi, I am unable to do something fairly simple. I have matrices called Table1,..., Table10. I want to be able to print them using a loop. So I wrote: ################## for (i in 1:10){ disp<-paste("Table", i, sep="") eval(parse(text=disp)) } ################## but this produces no output. Any ideas?

Hi, I would like to compute a goodness-of-fit statistic for one data series against a t-distribution, and obtain the quantiles of the distribution of the statistic with given degrees of freedom. I wonder if this is implemented in a package. I know that the critical values have to be computed for every distribution, and this requires numerical integration typically. I would prefer if I could get

Hi Berend, I see you are one of the contributors to the rbecnhmark package. I am sorry that I am bothering you again. I have tried to run your code (slightly tweaked) involving the benchmark function, and I am getting the following error message. What am I doing wrong? Error in benchmark(d1 <- s1(df), d2 <- s2(df), d3 <- s3(df), d4 <- s4(df), : could not find function

Hi, I was wondering if there was an R package or routines for the Dantzig Selector (Candes & Tao, 2007). I know Emmanuel Candes has Matlab routines to do this but I was wondering if someone had ported those to R. Thanks, T ---Reference--- @article{candes2007dantzig, title={{The Dantzig selector: statistical estimation when p is much larger than n}}, author={Candes, E. and Tao, T.},

Dear R-users, I would like to create a new data frame composed of 2 columns of another data frame. But it does not give me what I want... > casesCNST[1:10,] case X1 X2 X3 X4 expected 1 A1 0 0 0 0 E 2 A2 0 0 0 1 C 3 A3 0 0 0 2 C 4 A4 0 0 0 3 C 5 A5 0 0 0 4 C 6 A6 0 0 1 0 C 7 A7 0 0 1 1 C 8

Hi, all. Does anyone know how to profile R interpreter? I've tried gprof but it doesn't work. Thanks. Xiaoming [[alternative HTML version deleted]]

Dear R People: Could someone recommend a good reference for Rprofing, please? Thanks, Erin -- Erin Hodgess Associate Professor Department of Computer and Mathematical Sciences University of Houston - Downtown mailto: erinm.hodgess at gmail.com

Dear All, I need to do some very basic R profiling, something along the lines of: run this whole script five times and return the average completion time. I do not want (at this stage) delve into the details of the percentage of the time spent in which function and doing what. Which tools should I use? Any recommendation is welcome. Best Regards Lorenzo

Hello, I would appreciate if someone could help me resolve the following: 1. df1[!is.na( X1 | X2 | X3 | X4 | X5),][,1:5] # This does not work 2. Is these message harmful? The following object(s) are masked from 'df1 (position 3)': X1, X2, X3, X4, X5 Thanks, Pradip Muhuri #Reproducible Example set.seed(5) df1<-data.frame(matrix(sample(c(1:10,NA),100,replace=TRUE),ncol=5))

Hi, all, How to get a time as a number? While script is running, I want to print the elapsed time something like TIME_AS_SECOND() in the following script inittime <- TIME_AS_SECOND() for (i in 1:100){ do_something(i) curtime <- TIME_AS_SECOND() elapsedtime <- curtime-inittime print(paste(i, elapsedtime)) } Thanks in advance, Hyunchul [[alternative HTML version

If x is a (long) vector and n << length(x), what is a fast way of finding the top-n values of x? Some suggestions (calculating the ratio of the two top values): library("rbenchmark") set.seed(1); x <- runif(1e6, max=1e7); x[1] <- NA; benchmark( replications=20, columns=c("test","elapsed"), order="elapsed" , sort = {a<-sort(x,

I am working on a project that requires me to do very large factorial evaluations. On R the built in factorial function and the one I created both are not able to do factorials over 170. The first gives an error and mine return Inf. Is there a way to have R do these larger calculations (the calculator in accessories can do 10000 factorial and Maple can do even larger) -- View this message in

Rprof seems to ignore all time spent inside system() calls. E.g., this simple example actually takes about 10 seconds, but Rprof thinks the total time is only 0.12 seconds: > Rprof("sleep-system.out") ; system.time(system(command="sleep 10")) ; Rprof(NULL) user system elapsed 0.000 0.004 10.015 > summaryRprof("sleep-system.out")$by.total

Dear all, I have a big problem: - I got two matrices, A and B - A shows identifies the value of B, however the values of B must be summed - For instance, 1 1 2 2 2 2 1 1 gives matrix a 3 4 2 1 1 1 2 2 gives matrix b Now the result for the value 1 would be 7 4 which are the rowsums of the values of matrix B given that matrix A has the value 1. How can I do this automatically? I

This looks like a bug? Unable to use scan(pipe()) while profiling. I have no idea whether this version of R violates the "do not use `Rprof' in an executable built for profiling" warning in ?Rprof. Thanks -Don > version _ platform powerpc-apple-darwin1.4 arch powerpc os darwin1.4 system powerpc, darwin1.4 status Patched major 1 minor 3.1 year

Dear all, Indexing matrices from the Matrix package with [i,j] seems to be very slow. For example: library(rbenchmark) library(Matrix) mm <- matrix(c(1,0,0,0,0,0,0,0), nr=20, nc=20) MM <- as(mm, "Matrix") lookup <- function(mat){ for (i in 1:nrow(mat)){ for (j in 1:ncol(mat)){ mat[i,j] } } } benchmark(lookup(mm), lookup(MM),

Hello, This is about the new "line profiling" feature in R 3.0.0. As I was testing it, I find the results somewhat disappointing so I'd like to get your opinion. I put some poorly written code in a test.R file, here are the contents: double <- function(x) { out <- c() for (i in x) { out <- c(out, 2*i) # line 4 } return(out) } Then this how I source the file

Dear Rs: Hi, I am trying to read a big text file (nrows=243440, ncols=144). It seems the computational time of all the read methods (scan,readtable,read.delim) is not linear to the number of rows I want to read in: things became really slow once I tried to read in 100000 lines compare to 10000 lines). If I am reading the profiling result right, I guess scan wouldn't help either. My

Hi, I have a vector "v" and would like to find the number of occurrence of element "x" in the same. Is there a way other than, sum(as.integer(v==x)) or length(which(x==v)) to do the this. I have a huge file to process and do this. Both the above described methods are pretty slow while dealing with a large vector. Please have your comments. Praveen

Hi folks, I'm attempting to use the EMD package to analyze some neuroimaging data (timeseries with 64 channels sampled across 1 million time points within each of 20 people). I found that processing a single channel of data using EMD::emd() took about 8 hours. Exploration using Rprof() suggested that most of the compute time was spent in EMD::extrema(). Looking at the code for EMD:extrema(),