similar to: fast lm se?

this is not an important question, but I wonder why lm returns an error, and whether this can be shut off. it would seem to me that returning NA's would make more sense in some cases---after all, the problem is clearly that coefficients cannot be computed. I know that I can trap the lm.fit() error---although I have always found this to be quite inconvenient---and this is easy if I have only

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---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

As an end-user, I wonder about Revolution R. Is the relationship between Revolution R and the R community at-large a positive one? Do the former contribute to the development efforts of the latter? Is there a competitive aspect? is their forum competitive with r-help? any other thoughts? (most of all, I simply hope that they help some of the many helpful experts on this forum, who have

dear R wizards: here is the strange question for the day. It seems to me that nrow() is very slow. Let me explain what I mean: ds= data.frame( NA, x=rnorm(10000) ) ## a sample data set > system.time( { for (i in 1:10000) NA } ) ## doing nothing takes virtually no time user system elapsed 0.000 0.000 0.001 ## this is something that should take time; we need to add 10,000

dear R wizards:? I am wrestling with reshape.? I have a long data set that I want to convert into a wide data set, in which rows are firms and columns are years. > summary(rin) firm fyear sim1 Min. :1004.00 Min. :1964.0 Min. : -1.00000 1st Qu.:1010.00 1st Qu.:1979.0 1st Qu.: -0.14334 Median :1016.00 Median :1986.0 Median : 0.00116 Mean

dear R group: I have the probably fairly common problem that I would like to have one code.R file do different things if it is invoked from a symbolic link, which should be easy to uncover. $ ln -s code.R code-0.R $ ln -s code.R code-1.R $ R CMD BATCH code-1.R what needs to be in code-1.R to put code-1.r into a character vector? help appreciated. regards, /ivo welch PS : I read

dear R community: i have been looking but failed to find the following: is there a function in R that updates a plain OLS lm() model with one additional observation, so that I can write a function that computes recursive residuals *quickly*? PS: (I looked at package strucchange, but if I am not mistaken, the recresid function there takes longer than iterating over the models fresh from

dear R wizards---more ignorance on my part, exacerbated by too few examples in the function documentations. > d <- data.frame( id=rep(1:3,3), x=rnorm(9), y=rnorm(9)) Question 1: how do I work with the output of "by"? for example, > b <- by( d, d$id, function(x) coef(lm( y ~ x, data=x ) )) > b d$id: 1 (Intercept) x 0.2303 0.3618

Dear R wizards: I decided to take the advice in the R data import/export manual and want to learn how to work with SQL for large data sets. I am trying SQLite with the DBI and RSQLite database interfaces. Speed is nice. Alas, I am struggling to find a tutorial that is geared for the kind of standard operations that I would want in R. Simple things: * how to determine the number of rows in a

Dear R experts---I am struggling with memory and speed issues. Advice would be appreciated. I have a long data set (of financial stock returns, with stock name and trading day). All three variables, stock return, id and day, are irregular. About 1.3GB in object.size (200MB on disk). now, I need to merge the main data set with some aggregate data (e.g., the S&P500 market rate of return,

dear R wizards: an operation I execute often is the deletion of all observations (in a matrix or data set) that have at least one NA. (I now need this operation for kde2d, because its internal quantile call complains; could this be considered a buglet?) usually, my data sets are small enough for speed not to matter, and there I do not care whether my method is pretty inefficient (ok, I

dear readers---I struggled with how to do nice fixed-effects regressions in large economic samples for a while. Eventually, I realized that nlme is not really what I needed (too complex), and all I really wanted is the plm package. so, I thought I would share a quick example. ################ sample code to show fixed-effects models? in R # create a sample panel data set with firms and years

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--- t <- 1:30 f <- function(t) { cat("f for", t, "\n"); return(2*t) } g <- function(t) { cat("g for", t, "\n"); return(3*t) } s <- ifelse( t%%2==0, g(t), f(t)) shows that the ifelse function actually evaluates both f() and g() for all values first, and presumably then just picks left or right results based on t%%2.

Dear R experts--- after many years, I am planning to give in and write my first R package. I want to combine my collection of collected useful utility routines. as my guide, I am planning to use Friedrich Leisch's "Creating R Packages: A Tutorial" from Sep 2009. Is there a newer or better tutorial? this one is 4 years old. I also plan on one change---given that the

dear r experts---Is there a multicore equivalent of by(), just like mclapply() is the multicore equivalent of lapply()? if not, is there a fast way to convert a data.table into a list based on a column that lapply and mclapply can consume? advice appreciated...as always. regards, /iaw ---- Ivo Welch (ivo.welch at gmail.com)

is it possible to throw a stop() that is so hard that it will escape even tryCatch? /iaw ---- Ivo Welch (ivo.welch at gmail.com)

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]]

dear R experts: has someone written a function that returns the results of by() as a data frame? ??of course, this can work only if the output of the function that is an argument to by() is a numerical vector. presumably, what is now names(byobject) would become a column in the data frame, and the by object's list elements would become columns. it's a little bit like flattening the by()