similar to: help in lme

Dear list members, the following hlm was constructed: hlm <- groupedData(laut ~ design | grpzugeh, data = imp.not.I) the grouped data object is located at and can be downloaded: www.anicca-vijja.de/lg/hlm_example.Rdata The following works: library(nlme) summary( fitlme <- lme(hlm) ) with output: ... AIC BIC logLik 425.3768 465.6087 -197.6884 Random effects:

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

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"

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,

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 <-

If an array has missing values in different rows, plotting using the formul= a=20 interface can produce errors. Example: fake.data <- matrix(rep(-100:100, 4), ncol =3D 4) par(mfrow =3D c(1,2)) boxplot(fake.data ~ col(fake.data)) abline(h =3D 0, lty =3D 2) boxplot(as.data.frame(fake.data)) abline(h =3D 0, lty =3D 2) ##### Add the missing data fake.data[190:200, 1] <-

Ich habe in R eine Version von SIR gefunden und ausprobiert. Leider kann diese multivariate Responses nicht verarbeiten. Gibt es in R eine ausgefeilte Version von SIR?

Hi R usrs, I want to build R-2.10.0 on HP-UX, but I got following error message: ld: Unsatisfied symbol "zgemm" in file CHOLMOD.a[cholmod_l_super_numeric.o] ld: Unsatisfied symbol "zgemv" in file CHOLMOD.a[cholmod_l_super_solve.o] ld: Unsatisfied symbol "zherk" in file CHOLMOD.a[cholmod_l_super_numeric.o] ld: Unsatisfied symbol "ztrsm" in file

Hi, I have a (correlation) matrix and I want to select a subset of its cells depending on the combination of their column and row names. This illustrates my problem: mtrx <- matrix(c(1,2,3,4,5,6,7,8,9), nrow=3, ncol=3, dimnames = list(c('c132','c432', 'c233'), c('r132','r233', 'r432')))> mtrx r132 r233 r432c132 1 4 7c432 2

Hi, I recently ran into the problem that I needed a Siegel-Tukey test for equal variability based on ranks. Maybe there is a package that has it implemented, but I could not find it. So I programmed an R function to do it. The Siegel-Tukey test requires to recode the ranks so that they express variability rather than ascending order. This is essentially what the code further below does. After the

To avoid angry replies, let me first say that I know that the use of Type III sums of squares is controversial, and that some statisticians recommend instead that significance be judged using the non-marginal terms in the ANOVA. However, given that type III SS is also demanded by some… is there a function (equivalent to drop1 for lm) to obtain type III sums of squares for mixed models using the

Hi All, Sorry if this is a repost (a quick browse didn't give me the answer). I wonder if there are packages that can do the feature selection and classification at the same time. For instance, I am using SVM to classify my samples, but it's easy to get overfitted if using all of the features. Thus, it is necessary to select "good" features to build an optimum hyperplane (?).

Dear List As a fairly new R programmer I seem to have run into a strange problem - probably my inexperience with R After reading and merging successive files into a single data frame, I find that order does not sort the data as expected. I have multiple references in each file but each file refers to measurement data obtained at a different time. Here's the code library(reshape) #

Is there a strong reason why this simple code: for(rnd = 0; rnd < Nrnd - 1; ++rnd) { // round(inv_rnd, b1, b0, kp); for (iter = 0; iter < 4; ++iter) { round_i(inv_rnd, b1, b0, kp, iter); } l_copy(b0, b1); kp -= nc; } Produces the complicated control flow logic in the attached CFG? If I unroll the loop I no

I'm working with a 350MB CSV file on a server that has 3GB of RAM, yet I'm hitting a memory error when I try to store the data frame into a survey design object, the R object that stores data for complex sample survey data. When I launch R, I execute the following line from Windows: "C:\Program Files\R\R-2.9.1\bin\Rgui.exe" --max-mem-size=2047M Anything higher, and I get an

How do I cite a package (not R itself - I know how to do that)? Any thoughts or links? Many thanks in advance? Hank Stevens Dr. Martin Henry H. Stevens, Assistant Professor 338 Pearson Hall Botany Department Miami University Oxford, OH 45056 Office: (513) 529-4206 Lab: (513) 529-4262 FAX: (513) 529-4243 http://www.cas.muohio.edu/botany/bot/henry.html http://www.muohio.edu/ecology/

Dear list, I have a problem with a toy example: mtrx <- matrix(c(1,1,0,1,1,1,0,1,1,0,0,1), nrow=3) rownames(ma) <- letters[1:3] I would like to determine which is the minimum combination of rows that "covers" all columns with at least a 1. None of the rows covers all columns; all three rows clearly covers all columns, but there are simpler combinations (1st and the 3rd, or 2nd

Hello All, I have a question regarding how glmmPQL should be specified. Which of these two is correct? summary(fm.3 <- glmmPQL(cbind(response, 100 - response) ~ expt, data = data.1, random = ~ 1 | subject, family = binomial)) summary(fm.4 <- glmmPQL(response ~ expt, data = data.2, random = ~ 1 | subject, family =

Dear R Help Community, I have a question and an answer (based on reading this forum and online research), but I though I should share both since probably there's a much better way to go about my solution. My question is specifically about how to best visualise multiple response contingency tables. What I mean by 'multiple response' is that the total number of responses per row of a