similar to: read.table( ... comment.char="#") truncated my data

I've just discovered the recent addition of the comment.char arg to scan, a useful feature no doubt but the default value of # rather than "" breaks my code which looks for # as a delimeter in a file and provides a messy incompatability with earlier versions of R and with Splus. Is there any chance that this default could be changed to ""? -- Steve

Please, I would like to know how to generate a truncated multivariate normal distribution k - dimensional, X ~ NT(mu, Sigma), where the elements of X to be non-negative (except the first), and the first dimension is strictly larger than zero. Example: X ~ NT_2(mu, Sigma), where mu=c(0.5, 0.5) and Sigma=c([120, 191], [191,154]), with X_1>0 and X_2>=0 Could anybody help

Anybody who did a 3D bar Graph in r? Thanks Fairouz Makhlouf -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch

PR #7690 points out that if the confidence intervals (+/-1.58 IQR/sqrt(n)) in a boxplot with notch=TRUE are larger than the hinges -- which is most likely to happen for small n and asymmetric distributions -- the resulting plot is ugly, e.g.: set.seed(1001) npts <- 5 X <- rnorm(2*npts,rep(3:4,each=npts),sd=1) f <- factor(rep(1:2,each=npts)) boxplot(X~f) boxplot(X~f,notch=TRUE) I can

If so, I''ve had trouble finding it. Can anyone help?

Dear all, I have the following output generated by linear regression. Since there is only one regression intercept and one slope for one set of data, what is the meaning of std. error for intercept and that of slope? Thanks in advance. Sincerely, Minghua > data(thuesen) > attach(thuesen) > lm(short.velocity~blood.glucose) Call: lm(formula = short.velocity ~ blood.glucose)

Dear List, I'd like to run a discriminate analysis on a data set, but have no idea how to go about this in R. I have attempted to locate info in the manuals, but may not be consulting the right sections or documents. Can anyone point me to appropriate documentation if such exists. Many thanks, David S. David White sdavidwhite at bigfoot.com Columbus, Ohio

Here's a nasty one. The following has caused a segmentation fault and possibly also a bus error. fred <- 1:6 dim(fred) <- 6 dimnames(fred) <- list(LETTERS[1:6]) mm <- matrix(1:12, 2, 6) mm %*% fred # segmentation fault here In the case without the dimnames assignment the result is OK. Cheers, Jonathan. --please do not edit the information below-- Version: platform =

I have a general style question about R coding. Suppose I'm writing a function (foo1) that calls other functions (foo2, foo3, ...) which have complicated argument lists (e.g. optim(), plot()), _and_ I may be calling several different functions in the body of foo1. Since foo2 and foo3 have different sets of arguments, I can't just use "..." ; I did write some code a while ago

R help list subscribers, I am a new user of R. I am attempting to use R to explore a set of equations specifying the dynamics of a three trophic level food chain. I have put together this code for the function that is to be evaluted by LSODA. My equations Rprime, Cprime, and Pprime are meant to describe the actual equation of the derivative. When I run LSODA, I do not get the output that

All R experts, How do I fit a dynamic Random effects model with a binary dependent variable in R Thanks JCM [[alternative HTML version deleted]]

Dear R-help, Can some one tell me how to do the following (if it's possible)? Suppose I have a function like this: f <- function(x, y, ...) { ## some code g(x, y, ...) ## some more code } The problem is that g() may not understand everything that comes through in "...". Is there a way to delete some component of "..." and then pass it to g()? Here's

Dear RHelpers, Does anyone know of any R code to perform bivariate Poisson regression (including random effects)? Best wishes Simon Simon D.W. Frost, M.A., D.Phil. Department of Pathology University of California, San Diego Antiviral Research Center (Formerly: UCSD Treatment Center) 150 W. Washington St., Suite 100 San Diego, CA 92103 USA Tel: +1 619 543 8080 x275 Fax: +1 619 298 0177 Email:

Any better (more efficient, built-in) ideas for computing coef[1]+coef[2]*x+coef[3]*x^2+ ... than polynom <- function(coef,x) { n <- length(coef) sum(coef*apply(matrix(c(rep(x,n),seq(0,n-1)),ncol=2),1,function(z)z[1]^z[2])) } ? Ben -- 318 Carr Hall bolker at zoo.ufl.edu Zoology Department, University of Florida http://www.zoo.ufl.edu/bolker

I'm trying to get a grasp of maximum-likelihood estimation and would like to find a package that performs mle (hopefully a simple example). It seems as if there are plenty of packages that make use of different types of likelihood estimators, but none are of a simple, "newbie" type. Does anyone have a suggestion for which package would be the best for a mle example? Thanks, Jeff.

I'm probably missing something very basic here, but: I've written some C code that I load into R dynamically. In the course of this C code, I generate some multinomial random deviates. I initially used the publically available "randlib" library, which also implements its own random number generator and binomial deviates (which are used to generate the multinomial deviates).

How do I add error bars to an interaction plot of means? Thanks

Hey, all I am going to plot several 3-dimensional ellipsoids in R. For example, A is such a ellipsoid with mean u=[1 1 1]', covariance matrix C=[1 0 .5;0 1 .6;.5 .6 1]; So how can I plot such object in 3D space? Is there some function to achieve this? Thanks. Fred

I have two CSV files (exported from Excel), say file1 and file2. The have the same number of rows, and each has several columns, with names on the first line; and some of the columns in file1 are repeated in file2. Using the "foreign" package, I can read these in separately to dataframes say d1 and d2 with > d1<-read.csv("file1") >

optim ignores misspelled control parameters, so that trying to set (e.g.) "maxint=1000" in the control argument silently does nothing. The patch below (watch out for line breaks! also posted at http://www.zoo.ufl.edu/bolker/optim_patch.R , and http://www.zoo.ufl.edu/bolker/optim_new.R) adds three lines to optim.R that issue a warning if any names of elements of "control" fail