similar to: Help Documentation (PR#6714)

Dear all,=20 without taking sides here, I see two major advantage of keeping the redundancy in any documentation minimal. First, it makes the maintanance of the documentation as simple as possible. This in turn, minimizes the risk for getting inconsistent documentation in new updates. Otherwise, someone has to have a really good overview and know where to update when, say, one default argument is

hi henrik (all): A better solution would be to have levels: set.help(level="beginner"), which then provides expanded explanations. However, I do not think this is necessary: For the most part, the online R docs are great. It is not more detailed explanations that beginners crave. My primary wishes arise as I stumble onto a need, and then wish for a few more examples of different

I think many people share your view and are aghast at the reception that some well-intentioned posts receive. There have been past discussions on this and many people feel the way you and I do. Just to head off another round, let me acknowledge that there appears to be multiple viewpoints and although hard to believe by myself, there actually is a contingent that views what I see as

Ivo, Let me address your points in reverse order: 1. There is a `wishlist' category for bug reports, which I guess you've overlooked. 2. There is also a `Contributed Documentation' section on the R web site, which you can submit your contribution. As well, there are a few introductory level documents there already that you might be interested. 3. I must repectfully disagree about

ladies and gents: I have posted a couple of simple questions recently. As often happens to novices, the information was there somewhere, even in front of my eyes, and I just did not see it. I looked in docs that seemed to me to be the right place for this particular information, but did not find it. There is no question: mea culpa, and everything is documented somewhere in R. (Worst

On 28 Mar 2004 at 16:52, ivo welch wrote: > > ladies and gents: > > I have posted a couple of simple questions recently. As often happens . . . > I was told by Brian to stop sending such suggestions, in order not to > clutter the R bug report list. OK, I can save my time; I just wanted He told you stop sending them to R-bugs! When you get a little more experience, send them

For the question at the end, try barplot with the horiz=TRUE argument. Date: Tue, 24 Feb 2004 09:30:21 -0500 From: ivo welch <ivo.welch at yale.edu> To: <r-help at stat.math.ethz.ch> Cc: <r-help-owner at stat.math.ethz.ch>,ivo welch <ivo.welch at yale.edu> Subject: [R] Suggestions ?!?! hi chaps: * I have some suggestion, the first of which is about

dear R experts---I have been experimenting with the foreach package (with doMC) for a while. my first impression is that it is a very easy way to acquire parallel processing capabilities. (thanks, revolution R.) the only two gotchas were about installation (it required an exit and restart), and the precedence order of the foreach (higher than '+', I think), but once I understood this,

Dear R developers: R is supposed to be slow for iterative calculations. actually, it isn't. matrix operations are fast. it is data frame operations that are slow. R <- 1000 C <- 1000 example <- function(m) { cat("rows: "); cat(system.time( for (r in 1:R) m[r,20] <- sqrt(abs(m[r,20])) + rnorm(1) ), "\n") cat("columns: "); cat(system.time(for (c

Full_Name: ivo welch Version: 1.8.1 OS: linux Submission from: (NULL) (130.132.33.212) please define aliases for the 5 essential data set operations delete row insert row delete column insert column rename column these are too difficult for novices as-is now, and the cost for doing this for you would be very low. if need be, define hints only, such as dataset.del.row() <-

for the googleable r-help archives, I thought I would post what I wrote into my .Rprofile to automatically set some system information. the most relevant aspect is the determination of mc.cores. this is useful when users want to use the parallel package options(uname= system("uname", intern=TRUE)) options(os= if (getOption("uname")=="Darwin") "osx"

I sent this privately to ivo welch yesterday, and he thinks it might be useful to someone else as well. Since I'm on a Mac the screen device is quartz(): > quartz() > plot( c(0,1), c(0,1) ); > text( 0.5, 0.5, "\u2113" ) # and then File/Save As/ -- David. Begin forwarded message: > From: David Winsemius <dwinsemius at comcast.net> > Date: August 17,

dear R wizards: X is factor with 20,000*20=800,000 observations of 20,000 factors. I.e., each factor has 20 observations. y is 800,000 normally distributed data points. I want to see how much R^2 the X factors can provide. Easy, right? > lm ( y ~ X) and > aov( y ~ X) Error: cannot allocate vector of size 3125000 Kb is this computationally infeasible? (I am not an expert, but

dear R experts---I am struggling with the requirements to prepare my files for my printers. I am printing in 2/2 format, which means cyan and black for me, which they take from my color-separated pdf files. R comes into play, because it produces all the figures that are embedded in my book (pdflatex). now, TeX has no problems producing CMYK files. However, R produces RGB files (for

Dear R experts--- Has anyone written parallel versions of "by" (i.e., mcby) and "ave" (i.e. mcave) ? I did ask a question like this a year ago, and then the answer was no. for those who are googling the group for the answer to this question, in the meantime, the poor man's version of "by" is mclapply( split( ds, factor ), FUN ) I don't know the poor

Dear R experts--- I doubt that someone has already solved my problem, but I thought I would ask quickly, just in case someone has. Let' say I start with a (flattened panel) model that says y[i] = x[i] + b*(T-x[i]) easy enough---this is just a linear model. I could also make this a fixed-effects model if I change T to T[fmid], where fmid is the firm's id. I know I can do this

dear R experts---now I have a case where I want to estimate very large regression models with many fixed effects---not just the mean type, but cross-fixed effects---years, months, locations, firms. Many millions of observations, a few thousand variables (most of these variables are interaction fixed effects). could someone please point me to packages, if any, that would help me estimate such

dear R experts---quick question. I need to estimate a model that looks like y = (b*T+d*T^3) + (1-b-3*d*T^2)*x + (3*d*T)*x^2 + (-d)*x^3 I only have three parameters. Is nls() the right tool for the job, or is there something faster/better? /iaw ---- Ivo Welch (ivo.welch@brown.edu, ivo.welch@gmail.com) [[alternative HTML version deleted]]

Dear R experts: this is probably correct behavior, but I do want to point out that it is unexpected to someone not too well versed: > test=factor("A","B","C","A") > ifelse(test=="A", as.factor("A"), test) [1] 1 2 3 1 ok, my factor was just coerced into integers, even though I have a logical vector as my condition and factors as

I have a set of functions that I always load on startup. for example, there is my now infamous "is.defined()" function. I would like to add some documentation for these functions, so that I can do a ?is.defined inside R. The documentation tells me how to mark up Rd files is very good, but I wonder how one installs them for access by the R executable (on OSX for me). Do I drop