similar to: Forcing a refresh in R for Windows

I'm running into problems with set.seed--maybe I'm misunderstanding something. I'm running R 1.5.1 on Windows 2000. I'm basically trying to capture the random seed so that I can reproduce a simulation if it's necessary later. Using set.seed, I can certainly get reproducible results, but not the results I get on the first pass. Here's an example: # Generate a random

Prof. Ripley has provided very helpful information regarding X-axis labeling with POSIXct-class dates. Now I've run into another bit of problematic behavior. I'm running R 1.3.0 on Windows NT 4.0. When I create a plot with the optional argument xaxt = "n", and the X axis is generated from dates of class POSIXct, the X axis is plotted. Obviously, I'd like to prevent the X

I'm pleased to announce that my employer, Becton Dickinson, has an open position for software implementation in the Matlab and S languages. If interested, send a cover letter and resume (or CV) to Dr. Richard Moore at richard_moore at bd.com. If you have questions, contact myself (at jim_garrett at bd.com) or Dr. Moore. Below find a position description. Jim Garrett Becton Dickinson

I have a series of datasets, each containing pH measurements and manufacturing dates, and each dataset pertains to a different manufactured product. I'm trying to create a series of plots of pH measurements by date, but the default X-axis labeling behavior is not giving adequate results in this particular case, and I can't figure out how to persuade R to come up with something more

I'm looking into making R functions available from a Visual Basic application. Venables and Ripley point out in _S Programming_ that this is possible with automation via DCOM and by linking to R DLL(s) at compile time. I'm intrigued by the latter possibility, and would appreciate information anyone might be able to provide, especially: 1. Do I need to link to R.dll only or would I need

I'm learning to use R/Sweave/LaTeK to write my stat reports. Is there a way to have an executive summary in the beginning while still having the analysis code embedded? For example, a study has three independent objectives so I want my report to have three sections. Ideally the code chunk that answers the first objective would be embedded in the first section and so on.

All, I have long variable names that are being fed through splom (R 1.8.1). I'd like to resize the text printed on the diagonals to better display the names (unless anyone can suggest another approach - creative use of varnames). I've looked at the code, R-Help, ?splom and the Trellis User's Guide to no avail. Any suggestions? Thanks in advance, Max Kuhn, Ph.D. Becton Dickinson

I'm using R 1.9.1 on suse server v9 enterprise with the gee package version 4.13-10. I have code that runs in an automated script. It uses the gee function from the gee package. The script is run quite often without error. I have a problem where the script locks up R when calling this function (it starts execution and never finishes). I was able to track down the offending data and determine

Hi I am looking to perform a discrete mean-variance optimisation, specifically to maximise the ratio of portfolio mean over portfolio standard deviation for a portfolio of several hundred stocks through discrete position size holdings in each stock, where all position sizes must be elements of a small finite set of integer amounts which include zero. I don't think any of the standard R

Hi I am looking to perform a discrete mean-variance optimisation, specifically to maximise the ratio of portfolio mean over portfolio standard deviation for a portfolio of several hundred stocks through discrete position size holdings in each stock, where all position sizes must be elements of a small finite set of integer amounts which include zero. I don't think any of the standard R

I couldn't resist tossing in my two-cents' worth on this, because R has some language features that allow you to use efficient optimization routines in a straightforward, elegant way. I'm particularly enthusiastic about this because I have suffered through other languages, which made this approach either painful or impossible, depending on the problem. Thanks to the R development

Reshape2 is a reboot of the reshape package. It's been over five years since the first release of the package, and in that time I've learned a tremendous amount about R programming, and how to work with data in R. Reshape2 uses that knowledge to make a new package for reshaping data that is much more focussed and much much faster. This version improves speed at the cost of functionality,

Reshape2 is a reboot of the reshape package. It's been over five years since the first release of the package, and in that time I've learned a tremendous amount about R programming, and how to work with data in R. Reshape2 uses that knowledge to make a new package for reshaping data that is much more focussed and much much faster. This version improves speed at the cost of functionality,

Reshape2 is a reboot of the reshape package. It's been over five years since the first release of the package, and in that time I've learned a tremendous amount about R programming, and how to work with data in R. Reshape2 uses that knowledge to make a new package for reshaping data that is much more focussed and much much faster. This version improves speed at the cost of functionality,

Reshape2 is a reboot of the reshape package. It's been over five years since the first release of the package, and in that time I've learned a tremendous amount about R programming, and how to work with data in R. Reshape2 uses that knowledge to make a new package for reshaping data that is much more focussed and much much faster. This version improves speed at the cost of functionality,

plyr is a set of tools for a common set of problems: you need to __split__ up a big data structure into homogeneous pieces, __apply__ a function to each piece and then __combine__ all the results back together. For example, you might want to: * fit the same model each patient subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise transformations

plyr is a set of tools for a common set of problems: you need to __split__ up a big data structure into homogeneous pieces, __apply__ a function to each piece and then __combine__ all the results back together. For example, you might want to: * fit the same model each patient subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise transformations

I'm trying to write a function that has a dataframe as an argument. Within the function, I call groupedData to create a grouped-data object from the dataframe argument. However, the groupedData function doesn't seem to see the data. I'm guessing it's getting the argument name rather than its value, but I'm not positive. My system: R 1.3.1 on WinNT 4.0 (Pentium II), nlme

I'm having trouble loading the package SparseM in R 1.8.1, OS = Windows 2000. Installing appeared to go well; I saw no error messages, html documentation was installed, and "installed.packages()" lists SparseM among the installed packages. When I try to load the library, however, I get the following: > library(SparseM) Error in slot(mlist, "argument") : Can't get

>From http://shorewall.net/ProxyARP.htm (and the Setup Guide): > A word of warning is in order here. ISPs typically configure their > routers with a long ARP cache timeout. If you move a system from > parallel to your firewall to behind your firewall with Proxy ARP, it > will probably be HOURS before that system can communicate with the > internet. You can call your ISP and ask