similar to: R RPMS and HDF5

HDF5 is a high quality data storage format from NCSA (http://hdf.ncsa.uiuc.edu/index.html) Marcus Daniels of the Swarm Development Group has made available an R library called hdf5 which allows loading and saving of data in hdf5 format. The source for his library is here: ftp://ftp.swarm.org/pub/swarm/src/testing/hdf5_1.4.7.tar.gz I prefer to use RPMs to maintain software across many linux

Dear everybody: HDF5, a dataset formatting library, and R that is built with it, have been uploaded to ftp://ftp-fis.iarc.fr/pub/incoming. The R RPM uses the spec file created by Martyn Plummer as a starting point, and makes only a few trivial changes. -rw-rw-r-- 1 ftp 100 3416612 Oct 6 17:45 R-base-with-hdf5-0.65.1-1.i386.rpm -rw-rw-r-- 1 ftp 100 2300453 Oct 6 17:48

Running R 1.1 on RedHat Linux 6.2. I need to write a shell script that goes through a bunch of directories of simulation output, creating summary files that have the mean and standard deviation of the variables found in the data files in each directory. I've got the R code doing almost the right thing. It reads in data, then gets the mean and standard deviation for the numeric variables,

I'm trying to build an RPM for R 65 that has support for the hdf5 data format. In the past this was easy because I used the SPEC file from your SRPM. Now in the CRAN sites I find no RPM for 0.65.0 and there is some monkey business with a patch for LIBINDEX that I can't seem to get straight in my effort to graft the 0.64.0 spec file onto the 0.65.0 release of R. I was able to edit the

Using RedHat Linux 7.0, R-1.2.2, R-hdf5-1.2 library, I want to load a dataset, do some stuff with it, then erase its objects, get an other, repeat. My friend wrote a function which tried to clear away all the objects. At the end, it uses rm() to remove objects. This is the same way we do it interactively, from the R prompt: testLoadSeveralHDF <- function(numFiles) { for (i in

Dear Friends in R: On RedHat linux, R-1.1, I've gotten far enough to create the graph that shows on the screen with 3 lines, but I have some trouble. Here is the way I created the three "overlaid" graphs: data<-read.table("DataCulture0",header=T,as.is = TRUE) attach(data) tmp1<-plot(acquaint~T,type='l', ylim=c(0,1),ylab="average

I'm working up a set of small working examples in R to show what various distributions are and the beauty of the central limit theorem. Those example programs are in this directory: http://lark.cc.ukans.edu/~pauljohn/R/ExampleCode/ You can feel free to use those if you want, or you can send me other small working example code for R. The key here is small, self contained things that

I have made a plot with a legend and on the screen it looks fine, but when I save as jpg or pdf, or print, the legend box is too small, it cuts through the words on the right side. I put an example here: http://lark.cc.ukans.edu/~pauljohn/R/apdftest.pdf Is there a work around? -- Paul E. Johnson email: pauljohn at ukans.edu Dept. of Political Science

I'm getting some abuse for asking about R-Gnome and I don't think it is fair. I did not create that project, I was just asking how it was going and whether that other project would help out. Now I'm getting some email that says some of you don't want a GUI interface because you think interfaces like that are inconvenient. OK. I agree, in most cases. I think GUIs for model

I've seen XGobi come up in here from time to time, and I wonder what is it, what is it for, do I need it? How about throwing me a bone here! -- Paul E. Johnson email: pauljohn at ukans.edu Dept. of Political Science http://lark.cc.ukans.edu/~pauljohn University of Kansas Office: (785) 864-9086 Lawrence, Kansas 66045 FAX: (785)

You may want to take a look at a paper by Julia Kelsall and Scott Zeger in JRSS(C) - 1999, pp. 331-344. This paper describes a frequency domain approach to log-linear regression modeling of poisson-distributed count data, accounting for correlation and over-dispersion. There are also some S functions available to implement the methodology. Ravi. -----Original Message----- From: pauljohn at

I have trouble understanding the R list object and how to add objects and get them out. I''m trying to collect snapshots with recordPlot and replay them. Got great help from everybody here yesterday on my histogram function, works fine to make the pictures: histomatic <- function (s1,s2,s3,var){ if (is.numeric (s2[[var]])) { par(mfrow=c(3,1)); rz <- range(s1[[var]],

Remember that thread a long time ago about how to get R graphics into Ms Word? I'm looking at it now because my coauthor is a Windows User, I use RedHat linux. I output some eps files from R. I found one relatively painless route to get these figures into a Word document: View in GSview, copy to clipboard, then paste into document. I've not seen this one mentioned, and it has the

For 2 or 3 stage least squares, what do you R folks do? Follow-up question. My student wants to estimate this. 2 variables are governed by a system of difference equations. His theory is like so. Y_t and X_t are state variables, we want estimates for a, g, b, and h. X_(t+1) = 1 + a X_t + (a/K)* (X_t)^2 - g Y_t X_t Y_(t+1) = b Y_t + h* X_t * Y_t K is perhaps something to estimate, but it

Today I wanted to experiment with different distributions an to see the hazard rates they imply. So I eventually ended up with this, which uses the hist object's handy $density and the cumsum function in R: x <- c(rweibull(21000,0.5,0.7)) #create "breaks" vector to go into histogram #need last break bigger than max(x) y <- seq(0,max(x)+2) histx <-

I just heard a talk about a semi-parametric model. I was quite excited by the idea. This model is fitted y= xB + g(z) + e where x is a data matrix, B a column vector, z is another data matrix, and g is a smooth model fitted by a Kernel Smoothing regression (I got the idea any smoother would do as well). The speaker said that when z is considered as a "control" variable, and there is

I got great, quick advice here earlier today. One little hitch was that the pattern in list.files wants a regular expression, and *.dat was grabbing all files with .dat in them, not ones ending in .dat. This code creates a *.dat.summary file with the mean and std. deviation. of the numeric variables for every *.dat file in the current directory. myDat<-list.files(pattern="*.dat$")

On Linux (RH7.1) with R installed from CRAN rpms: I have many files with miscellaneous names *.dat. I would like to successively open each one, run some functions on them, and then write some output numbers to *.summary. I have succeeded in doing this when I type in each file name individually for the data input, but have not found a way to search files in a directory and grab the *.dat. If

I have RedHat linux 9 with R 1.8. I'm estimating models with multinom with a dependent variable that has 3 different values. Sometimes the models run fine and I can understand the results. Sometimes when I put in another variable, I see an indication that the estimation did work, but then I can't get the summary method to work. It's like this: > votemn1 <-

I've used the HDF5 library to bring some data into R. THe verbose output looks like this: > hdf5load("hdfGraphWed_Mar_16_13_33_37_2005.hdf",load=T,verbosity=1,tidy=T) Processing object: cprSeats ...... which is a Group Processing object: Seats 0 ...... its a dataset......Finished dataset Processing object: Seats 1 ...... its a dataset......Finished dataset Processing object: