similar to: Random Variable Generation

I am a new user of R so please bear with me. I have reviewed some R books, FAQs and such but the volume of material is great. I am in the process of porting my current SAS and SVS Script code to Lotus Approach, R and WordPerfect. My question is, can you help me determine the best R method to implement the Hoaglin Outlier Method? It is used in the Appendix A and B of the fo llowing link.

I have a problem with R. I try to compute the confidence interval for my df. When I want to create the plot I have this problem: Error in xy.coords(x, y, xlabel, ylabel, log) : 'x' and 'y' lengths differ. I try this code: library(dplR) df.rwi <- detrend(rwl = df, method = "Spline",nyrs=NULL) write.table(df.rwi,file="rwi.txt",quote=FALSE,row.names=TRUE)

Hello, I try to handle the data using read.csv , zoo and aggregate functions. The data contains NA values. After aggregating monthly data into quarterly data, all data become NA. Is it because I don't properly aggregate the data in the presence of NAs? What can I do? Another problem is that the date in month is presented in Chinese (My OS is in Chinese.) How can I set the default

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

Hi Tanya, > 1) Compile llvm from source and untar the llvm-test in the projects > directory (name it llvm-test or test-suite). Choose to use a > pre-compiled llvm-gcc or re-compile it yourself. I compiled llvm and llvm-gcc with separate objects directories. Platform is x86_64-linux-gnu. > 2) Run make check, report any failures (FAIL or unexpected pass). Note > that you need to

Dear R Users,i am very new to R. I want your help on an issue regarding distance matrix and cluster analysis i had discharge data of 4 rivers(a,b,c,d) in 4 vectors each having 364 values > dput(qmu)structure(list(a = c(0.26, 0.25, 0.25, 0.25, 0.24, 0.23, 0.22, 0.21, 0.21, 0.21, 0.2, 0.19, 0.19, 0.19, 0.19, 0.18, 0.18, 0.18, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17,

G'Day Tanya, Is it too late to bring in the following patches to fix some major brokenness in the AuroraUX tool chain for 2.6? http://llvm.org/viewvc/llvm-project/cfe/trunk/lib/Driver/Tools.cpp?r1=84468&r2=84469&view=diff&pathrev=84469 http://llvm.org/viewvc/llvm-project/cfe/trunk/lib/Driver/Tools.cpp?r1=84265&r2=84266&view=diff&pathrev=84266

I've a matrix like this: 1985 1.38 1.27 1.84 2.10 0.59 3.47 1986 1.05 1.13 1.21 1.54 0.21 2.14 1987 1.33 1.21 1.77 1.44 0.27 2.85 1988 1.86 1.06 2.33 2.14 0.55 1.40 1989 2.10 0.65 2.74 2.43 1.19 1.45 1990 1.55 0.00 1.59 1.94 0.99 2.14 1991 0.92

aa <- (structure(list(X.0.85 = c(-1.02, -1.17, -1.29, -1.39, -1.46, -1.5, -1.52, -1.5, -1.46, -1.39, -1.3, -1.19, -1.07, -0.93, -0.79, -0.65, -0.5, -0.36, -0.22, -0.08, 0.05, 0.18, 0.3, 0.41, 0.52, 0.62, 0.72, 0.81, 0.89, 0.98, 1.05, 1.13, 1.19, 1.25, 1.29, 1.31, 1.31, 1.29, 1.24, 1.16, 1.06, 0.93, 0.77, 0.58, 0.38, 0.16, -0.07, -0.31, -0.89, -1.05, -1.19, -1.31, -1.41, -1.47, -1.51, -1.51,

LLVMers, 2.6 pre-release2 is ready to be tested by the community. http://llvm.org/prereleases/2.6/ If you have time, I'd appreciate anyone who can help test the release. To test llvm-gcc: 1) Compile llvm from source and untar the llvm-test in the projects directory (name it llvm-test or test-suite). Choose to use a pre- compiled llvm-gcc or re-compile it yourself. 2) Run make check,

Dear R-users, We expect to form N homogeneous groups of n features from an experimentation including N*n data. The aim is to prevent group effects. How to do that with R functionalitites ? Does anyone know any way enabling this ? Example : 100 patients are observed. 3 biochemical parameters are mesured for each one (Red and white globules ans glycemia). Patient RG RW

(Probably OT) Has anyone else noticed these flaky (new?) behaviors in Evo since the 5.2 upgrade: - REALLY slow saving messages from inbox to another folder - search capability separated by folder instead of overall (with no option for control) - failure to autocomplete email addresses for known contacts I think that's it. I was wondering if it was just me, or if others had noticed this.

Hello, I have 2 servers I'm synchronizing using rsync, I have a situation where I : 1. rsync from rnd-dev2 to rnd-dev1 2. change the rsynched file on rnd-dev1 3. rsync from rnd-dev2 to rnd-dev1 again 4. File gets overridden on rnd-dev1 over though it has newer change time then file on rnd-dev2. here is the bug(?) reproduction: [root@rnd-dev1 test_rsync]# rsync --version rsync version

Benchmark shows small performance improvement on fusion io device. Before: seq-read : io=1,024MB, bw=19,982KB/s, iops=39,964, runt= 52475msec seq-write: io=1,024MB, bw=20,321KB/s, iops=40,641, runt= 51601msec rnd-read : io=1,024MB, bw=15,404KB/s, iops=30,808, runt= 68070msec rnd-write: io=1,024MB, bw=14,776KB/s, iops=29,552, runt= 70963msec After: seq-read : io=1,024MB, bw=20,343KB/s,

Benchmark shows small performance improvement on fusion io device. Before: seq-read : io=1,024MB, bw=19,982KB/s, iops=39,964, runt= 52475msec seq-write: io=1,024MB, bw=20,321KB/s, iops=40,641, runt= 51601msec rnd-read : io=1,024MB, bw=15,404KB/s, iops=30,808, runt= 68070msec rnd-write: io=1,024MB, bw=14,776KB/s, iops=29,552, runt= 70963msec After: seq-read : io=1,024MB, bw=20,343KB/s,

Folks, I'm trying to get stats from a matrix for each transition from one state to another. I have a matrix x as below. structure(c(0, 2, 2, 2, 0, 0, 0, 1, 1, 1, 1, 2, 2, 1, 1, 1, 0, 0, 2, 2, 0.21, -0.57, -0.59, 0.16, -1.62, 0.18, -0.81, -0.19, -0.76, 0.74, -1.51, 2.79, 0.41, 1.63, -0.86, -0.81, 0.39, -1.38, 0.06, 0.84, 0.51, -1, -1.29, 2.15, 0.39, 0.78, 0.85, 1.18, 1.66, 0.9, -0.94,

Hi, I'm Sorry for any cross-posting. I've reviewed the archives and could not find an exact answer to my question below. I'm trying to generate very large sparse matrices (< 1% non-zero entries per row). I have a sparse matrix function below which works well until the row/col count exceeds 10,000. This is being run on a machine with 32G memory: sparse_matrix <-

To anyone who can help, I have two stupid questions, and one fairly intelligent question Stupid question (1): is there an R function to calculate a factorial of a number? That is...is there a function g(.) such that g(3) = 6, g(4) = 24, g(6) = 720, etc? Stupid question (2): how do you extract the estimated covariance matrix of the random effects in an lme object? Intelligent question

LLVMers, The 2.5 pre-release is available for testing: http://llvm.org/prereleases/2.5/ If you have time, I'd appreciate anyone who can help test the release. Please do the following: 1) Download/compile llvm source, and either compile llvm-gcc source or use llvm-gcc binary (please compile llvm-gcc with fortran if you can). 2) Run make check, send me the testrun.log 3) Run "make

Hi there, We are trying to compile a 64bit version of R (2.5.1) on HP-UX (B.11.23 U ia64), but are running into some problems. This is our configure step: ../configure --prefix=/rnd/homes/lfan/R251 --enable-R-shlib CC="cc" CFLAGS="+z +DD64" CXX="aCC" CXXFLAGS="-b -lxnet +z +DD64" FC="f90" FCFLAGS="+DD64" F77="f90"