similar to: Money

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

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

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

dear R wizards: I have a large data frame, a million rows, 40 columns. In this data frame, there are some (about 100,000) rows which I want to recompute (update), while I want to leave others just as is. this is based on a condition that I need to compute, based on what is in a few of the columns. what is the right R way to do this? I could subset out the rows that I want to recompute into a

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

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

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

Dear R-Help group: I have been tinkering with how I want my personal standard library functions to look like. They are not designed to be professional and heavyweight, but lightweight. There are probably dozens of little bugs, because I don't know or have not properly taken care of a variety of internal R code issues. still, I like how this ended up, and there is no learning curve, so I

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---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

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 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

Dear R experts: I searched cran (and r-help) for "nash equilibrium" and "game" but nothing stuck out. has someone written a numerical nash optimizer for two players? player a has choices x1,x2,x3,... and cares about (maximizes) pa(x1,x2,x3,...,y1,y2,y3) player b has choices y1,y2,y3,..., and cares about (maximizes) pb(x1,x2,x3,...,y1,y2,y3) I can tune it to my problem, but

Dear R wizards: is there a clean way to assign to elements in a list? what I would like to do, in pseudo R+perl notation is f <- function(a,b) list(a+b,a-b) (c,d) <- f(1,2) and have c be assigned 1+2 and d be assigned 1-2. right now, I use the clunky x <- f(1,2) c <- x[[1]] d <- x[[2]] rm(x) which seems awful. is there a nicer syntax? regards, /iaw ---- Ivo Welch

dear R experts---is there a function that prints a data frame to a string? cat() cannot handle lists, so I cannot write cat("your data frame is:\n", df, "\n"). regards, /iaw ---- Ivo Welch (ivo.welch@gmail.com) [[alternative HTML version deleted]]

quick programming questions. I want to "turn on" more errors. there are two traps I occasionally fall into. * I wonder why R thinks that a variable is always defined in a data frame. > is.defined(d) [1] FALSE > d= data.frame( x=1:5, y=1:5 ) > is.defined(d$z) [1] TRUE > is.defined(nonexisting$garbage) [1] TRUE this is a bit unfortunate

in a long program, I ran into ?*** caught segfault *** address 0xdc3f9b48, cause 'memory not mapped' Traceback: ?1: rep.int(seq_len(nx), rep.int(rep.fac, nx)) ?2: rep.int(rep.int(seq_len(nx), rep.int(rep.fac, nx)), orep) ?3: expand.grid(seq_len(nx), seq_len(ny)) ?4: merge.data.frame(d, ss) ?5: merge(d, ss) ?6: valid.range(opt) ?7: eval.with.vis(expr, envir, enclos) ?8: eval.with.vis(ei,

Dear R Wizards---is it possible to get R to show its current call stack (sys.calls()) upon an error abort? I don't use ESS for execution, and it is often not obvious how to locate how I triggered an error in an R internal function. Seeing the call stack would make this easier. (right now, I sprinkle "cat" statements everywhere, just to locate the line where the error appears.) Of