similar to: 'Program Error' dialog box

With the incorporation of the useful hints, my user function now looks like this: userfn <- function() { ... ow <- options("warn") options(warn = 2); ... reg<-try(polr(act~.,data=mm,Hess=TRUE)) ... sumreg<-try(summary(reg)) print(length(sumreg)) print(sumreg) ... options(ow) # reset } The routine userfn() is called multiple times, two of which I happen to know to

Dear R experts: I am running some calculations using polr() in MASS library, and found some differences in results obtained on two different machines (IRIX 6.5, and Linux RH 7.1). It is not clear to me whether this is due to some error in my programming the calculation and how to resolve the differences, if possible. The polr() call is the following:

Not having paid any attention, I discovered that this file grew to be 1048518656 bytes (this on a debian-based Linux system), and filled up the (small) filesystem for /var. Is there a 'normal' size that I should expect for this file ? What could be the reason for the large size? And how could it be best reduced? (There was a suggestion of stopping samba, removing the file, and

I have a konica printer (at IP 10.1.1.28 below) that is broken from samba 3.5.11 to 3.6.6 (in debian-derived Knoppix, from DVD 6.7.1 to DVD 7.0.4). The printer is configured in the following command line sequence: lpadmin -p konica -v smb://10.1.1.28/C552 -P /usr/share/ppd/Driver-konica-C552/CUPS1.2/English/KOC652UX.ppd cupsenable konica cupsaccept konica /etc/init.d/cups

Within an user function, how are the warnings from an R function be trapped (such that some proper actions can be taken)? 'last.warning' is returned only at the top level. Pointers are appreciated. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

I have used plor() to model a rather large 3-category dataset (~1500 data points, ~15 independent variables); from the resulting model (with a deviance slightly below the residual degrees of freedom), the training data are placed in only the two extreme categories. Though the result appears to indicate that there's only a relative 'narrow' bin for the medium group, [and when the

For R-1.4.0, I have been able to build R.bin with -O2 flag. This however did not yield any significant speed-up for my code. I then tried building the binary for 64 bit, but had not been able to configure the build; none of the suggestions that I can find in the r-help search worked for me. I'm looking for any other suggestions -- TIA mod:>> uname -a IRIX64 mod 6.5 10100655 IP35

I'm running polr() and getting warning messages from glm.fit(). It seems reasonable to use glm.control() to turn on the trace and follow what glm.fit() does when called by polr(); or is it? glm.control(maxit=10, trace=TRUE) polr(act~., data=mm) The glm.control() sets the trace TRUE, but there's no change in the output from polr(). Many thanks in advance for any help/pointers.

Are there more detailed documentations for writing fortran routines than "doc/manual/R-exts.html", or some examples of such routines available? Thanks in advance for any help/pointers. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

To R users: I am running an R program with .Fortran() calls, and discovered that the memory, as reported by gc(), continues to grow with new loop iterations. I hadn't been able to find information on how to 'destroy' objects in earlier loops to keep the program size from continuous growth; calling rm() to remove some what I thought to be large arrays didn't seem to help. Any

To R users: I am running an R program with .Fortran() calls, and discovered that the memory, as reported by gc(), continues to grow with new loop iterations. I hadn't been able to find information on how to 'destroy' objects in earlier loops to keep the program size from continuous growth; calling rm() to remove some what I thought to be large arrays didn't seem to help. Any

Dear Mao Jianfeng, "r-help-owner" is not the place for help, but: r-help at r-project.org (CC-ed here) In any case, strsplit() does the job, i.e.: > unlist(strsplit("BCPy01-01", "-")) [1] "BCPy01" "01" You can work with the whole variable, like: splitpop <- strsplit(df1$popcode, "-") then access the first part with >

Dear Ruser's I ask for helps on how to substitute missing values (NAs) by mean of the group it is belonging to. my dummy dataframe is: > df group traits 1 BSPy01-10 NA 2 BSPy01-10 7.3 3 BSPy01-10 7.3 4 BSPy01-11 5.3 5 BSPy01-11 5.4 6 BSPy01-11 5.6 7 BSPy01-11 NA 8 BSPy01-11 NA 9 BSPy01-11 4.8 10 BSPy01-12 8.1 11 BSPy01-12 6.0 12

Dear R-help listers, I am new. I just want to get helps on how to plot two histograms overlapped in the same plane coordinate. What I did is very ugly. Could you please help me to improve it? I want to got a plot with semi- transparent overlapping region. And, I want to know how to specify the filled colors of the different histograms. I also prefer other solutions other than ggplot2. Many

Hi, R-listers, Failed, when I tried to merge df1 and df2 by "codetot" in df1 and "codetoto" in df2. I want to know the reason and how to merge them together. Data frames and codes I have used were listed as followed. Thanks a lot in advance. df1: popcode codetot p3need BCPy01-01 BCPy01-01-1 100.0000 BCPy01-01 BCPy01-01-2 100.0000 BCPy01-01 BCPy01-01-3 100.0000 BCPy01-02

Hello dear R-users, I have a tricky problem of data manipulation with R. Although it seems very easy, I can not solve it by myselves. The problem is a variable in my dataset is usually be readed as "factor" (variable mode), however I want it be a "numeric" one. I want to know how can I define an variable/column in a dataframe as numeric mode, after I imported a large

Dear R-listers, There is my data and my codes to create a plot. I want to know why there are two small circles in the upper right and lower left of the plot respectively. Could you please share your experience or advice with me? # dummy data factor<-rep(c("Alice","Jone","Mike"),each=100) factor<-factor(factor) traits<-c(rnorm(100, mean=1, sd=1), rnorm(100,

Dear R-listers, I am new to R in linux. And, I am now confused by updating R in Ubuntu 9.10 linux system. I would like to seek some advice on that problem. I have done what are listed in CRAN linux installation guide: 1. add an entry like "deb http://cran.cnr.berkeley.edu/bin/linux/ubuntu karmic/" in /etc/apt/sources.list file in my computer. 2. To install the complete R system, use

Dear R-listers, I do clustering on tens of individuals by thousands of traits. I have known the assignment of each individual. I want to classify the individuals by randomly resampling different subsets of the traits, for example, randomly resampling 100 traits for 100 times, then 200 traits for 100 times, then 300 traits for 100 times, ,,,,,,. By each subset of traits, I do clustering of the

Dear Ruser's I want to substitute each "NA" by the group mean of which the "NA" is belonging to. For example, substitute the first record of traits "NA" by the mean of "BSPy01-10" in the dummy dataframe. I have ever tried to solve this problem by using doBy package. But, I failed. I ask for the advice on how to use "lapplyBy" function of