Displaying 20 results from an estimated 5000 matches similar to: "parallelized version of "by" and "ave""
2011 Oct 10
5
multicore by(), like mclapply?
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)
2012 Mar 26
1
assigning vector or matrix sparsely (for use with mclapply)
Dear R wizards---
I have a wrapper on mclapply() that makes it a little easier for me to
do multiprocessing. (Posting this may make life easier for other
googlers.) I pass a data frame, a vector that tells me what rows
should be recomputed, and the function; and I get back a vector or
matrix of answers.
d <- data.frame( id=1:6, val=11:16 )
loc <- c(TRUE,TRUE,FALSE,TRUE,FALSE,TRUE)
2011 Oct 11
2
SLOW split() function
dear R experts: ?apologies for all my speed and memory questions. ?I
have a bet with my coauthors that I can make R reasonably efficient
through R-appropriate programming techniques. this is not just for
kicks, but for work. for benchmarking, my [3 year old] Mac Pro has
2.8GHz Xeons, 16GB of RAM, and R 2.13.1.
right now, it seems that 'split()' is why I am losing my bet. ?(split
is an
2012 Apr 18
1
multi-machine parallel setup?
Dear R experts:
could someone please point me to a page that explains how to set up
more than 1 machine for library parallel (which is quickly becoming my
favorite!)
my dream setup would be a design where I just pass a list of
hostnames:user:password to my parallel master, and then start R
listener processes on each of my slaves by hand. R would start slave
processes automatically on each slave
2012 Dec 29
1
parallel error message extraction (in mclapply)?
dear R experts---I am looking at a fairly uninformative error in my program:
Error in mclapply(1:nrow(opts), solveme) :
(converted from warning) all scheduled cores encountered errors in user code
the doc on ?mclapply tells me that
In addition, each process is running the job inside try(...,
silent=TRUE) so if error occur they will be stored as try-error
objects in the list.
I looked up
2011 Jul 08
2
manipulating "by" lists and "ave()" functions
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
2019 Apr 05
2
Deep Replicable Bug With AMD Threadripper MultiCore
The following program is whittled down from a much larger program that
always works on Intel, and always works on AMD's threadripper with
lapply but not mclappy. With mclapply on AMD, all processes go into
"suspend" mode and the program then hangs. This bug is replicable on an
AMD Ryzen Threadripper 2950X 16-Core Processor (128GB RAM), running
latest ubuntu 18.04. The R version
2013 May 31
1
R 3.0.1 : parallel collection triggers "long memory not supported yet"
Dear R developers:
...
7: lapply(seq_len(cores), inner.do)
8: FUN(1:3[[3]], ...)
9: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
Selection: .....................Error in sendMaster(try(lapply(X = S, FUN =
FUN, ...), silent = TRUE)) :
long vectors not supported yet: memory.c:3100
admittedly, my outcome will be a very big list, with 30,000 elements, each
containing data frames
2024 Dec 31
1
mclapply hanging occasionally on macos
? Mon, 30 Dec 2024 19:16:11 -0800
Ivo Welch <ivo.welch at gmail.com> ?????:
> useless.function <- function( ) {
> y <- rnorm(3); x <- rnorm(3)
> summary( lm( y ~ x )) ## useless
> NULL
> }
>
> run30 <- function(i) {
> message("run30=", i)
> useless.function()
> }
>
> run30( 0 )
> message("many mc")
2011 Jul 02
5
%dopar% parallel processing experiment
dear R experts---
I am experimenting with multicore processing, so far with pretty
disappointing results. Here is my simple example:
A <- 100000
randvalues <- abs(rnorm(A))
minfn <- function( x, i ) { log(abs(x))+x^3+i/A+randvalues[i] } ?## an
arbitrary function
ARGV <- commandArgs(trailingOnly=TRUE)
if (ARGV[1] == "do-onecore") {
?library(foreach)
?discard <-
2024 Dec 31
1
mclapply hanging occasionally on macos
sequoia, 15.2. R --vanilla : 4.4.2 (2024-10-31). I have the same
basic setup on three macs: a macbook air, a mac pro m1, and a mac mini
m4. The following code is running into a bug on the mac pro m1 and
the mac mini, but works just fine on my macbook air. (of course, it
doesn't do anything useful.) it's replicable!
```
$ R --vanilla
> source("debug.R")
```
and (after
2012 Mar 30
4
list assignment syntax?
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
2012 May 09
2
big quasi-fixed effects OLS model
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
2010 Jan 08
4
fast lm se?
dear R experts---I am using the coef() function to pick off the coefficients
from an lm() object. alas, I also need the standard errors and I need them
fast. I know I can do a "summary()" on the object and pick them off this
way, but this computes other stuff I do not need. Or, I can compute (X'
X)^(-1) s^2 myself. Has someone written a fast se() function?
incidentally, I think
2010 Jun 11
3
lm without error
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
2024 Nov 16
1
[EXT] Mac ARM for lm() ?
Thanks, and all well taken. But are my beautiful GPUs (with integrated
memory architecture) really nothing more than a cooling area for the chip?
On Fri, Nov 15, 2024 at 6:06?AM Martin Maechler <maechler at stat.math.ethz.ch>
wrote:
> >>>>> Andrew Robinson via R-help
> >>>>> on Thu, 14 Nov 2024 12:45:44 +0000 writes:
>
> > Not a direct
2013 Feb 07
4
Hard Stop?
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)
2012 May 31
2
print.data.frame to string?
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)
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2012 Nov 08
3
vectorized uni-root?
dear R experts--- I have (many) unidimensional root problems. think
loc.of.root <- uniroot( f= function(x,a) log( exp(a) + a) + a,
c(.,9e10), a=rnorm(1) ) $root
(for some coefficients a, there won't be a solution; for others, it
may exceed the domain. implied volatilities in various Black-Scholes
formulas and variant formulas are like this, too.)
except I don't need 1 root, but a
2010 Aug 22
2
on abort error, always show call stack?
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