similar to: package load altering RNG state

>>>>> Henric Winell <nilsson.henric at gmail.com> >>>>> on Tue, 7 Feb 2017 13:37:42 +0100 writes: > Hi, On 2017-02-07 13:12, Benjamin Tyner wrote: >> Hello >> >> When loading a package, I'm wondering if it's frowned >> upon for the package to alter the state of the random >> number

Hi, On 2017-02-07 13:12, Benjamin Tyner wrote: > Hello > > When loading a package, I'm wondering if it's frowned upon for the > package to alter the state of the random number generator? I guess not, > since the parallel package does it? Surprisingly it is not frowned upon, but it *is* a nuisance. I brought it up a couple of years ago

The last line of the example in droplevels' manual page seems to be incorrect to me. I think it should read: "table(droplevels(aq$Month))". Amazingly (I don't understand) both variants seem to produce the same result (R 3.3.3): --- > aq <- transform(airquality, Month = factor(Month, labels = month.abb[5:9])) > aq <- subset(aq, Month != "Jul") >

Marius Hofert-4------------------------------ > Den 2015-10-09 kl. 12:14, skrev Martin Maechler: > I think so: the code above doesn't seem to do the right thing. Consider > the following example: > > > x <- c(1, 1, 2, 3) > > rank2(x, ties.method = "last") > [1] 1 2 4 3 > > That doesn't look right to me -- I had expected > > >

Hello, Inline. Em 12-04-2017 16:40, Henric Winell escreveu: > (Let's keep the discussion on-list -- I've added back R-devel.) > > On 2017-04-12 16:39, Ulrich Windl wrote: > >>>>> Henric Winell <nilsson.henric at gmail.com> schrieb am 12.04.2017 >>>>> um 15:35 in >> Nachricht <b66fe849-bb8d-f00d-87e5-553f866d57e0 at gmail.com>:

Hi, I've implemented parallelization in one of my packages using the 'parallel' package -- many thanks for providing it! In my package I'm importing 'parallel' and so added it to the DESCRIPTION file's 'Import:' tag and also added a 'importFrom("parallel", ...)' statement in the NAMESPACE file. Parallelization works nicely, but my package

Hi, I ran into a problem where I actually need rank(, ties.method="last"). It would be great to have this feature in base and it's also simple to get (see below). Thanks & cheers, Marius rank2 <- function (x, na.last = TRUE, ties.method = c("average", "first", "last", # new "last" "random", "max",

After installing R-3.4.0 I ran 'make check' which halted here: $ > tail reg-tests-1d.Rout.fail -n 16 > ## format()ing invalid hand-constructed POSIXlt objects > d <- as.POSIXlt("2016-12-06"); d$zone <- 1 > tools::assertError(format(d)) > d$zone <- NULL > stopifnot(identical(format(d),"2016-12-06")) > d$zone <- "CET" # =

After installing R-3.4.0 I ran 'make check' which halted here: $ > tail reg-tests-1d.Rout.fail -n 16 > ## format()ing invalid hand-constructed POSIXlt objects > d <- as.POSIXlt("2016-12-06"); d$zone <- 1 > tools::assertError(format(d)) > d$zone <- NULL > stopifnot(identical(format(d),"2016-12-06")) > d$zone <- "CET" # =

------------------ >>>>> Henric Winell <[hidden email]> >>>>> on Wed, 21 Oct 2015 13:43:02 +0200 writes: > Den 2015-10-21 kl. 07:24, skrev Suharto Anggono Suharto Anggono via R-devel: >> Marius Hofert-4------------------------------ >>> Den 2015-10-09 kl. 12:14, skrev Martin Maechler: >>> I think so: the code above

Dear all, I had a R segmentation fault, and then invoked debug mode and ran step by step. When I reached "terms(Y~X1*X2*...*X16)", I would then have "segmentation" fault. However, if I just ran this under regular "R interactive" mode, it would be fine though taking long time. My questions are: 1. Is there a known limit of terms for a formula? 2. Why does the

Dear all, I had a R segmentation fault, and then invoked debug mode and ran step by step. When I reached "terms(Y~X1*X2*...*X16)", I would then have "segmentation" fault. However, if I just ran this under regular "R interactive" mode, it would be fine though taking long time. My questions are: 1. Is there a known limit of terms for a formula? 2. Why does the

Hello R community members, I'm trying to install the 'rqpd' package which is developed by Roger Koenker and Stefan Bache. When I try to install the package using the command 'install.packages("rqpd",repos="http://R-Forge.R-project.org")' I'm getting the following two messages: i) package ?rqpd? is available as a source package but not as a binary

Dear All, I have two simple data samples (no groups or factors, etc.) and would just like to compute the two-sample Wilcoxon Rank Sum test using the wilcox_test function contained in the coin package, which is reportedly better than the regular wilcox.test function because it performs some adjustment for ties. Would anyone know how to craft a script to perform this task? Much appreciated. Janh

Good day, I'm trying the example cl <- makeCluster(2, type = "SOCK") from the makeCluster documentation and the R command prompt never returns. I am using a 32-bit Windows 10 computer. The problem doesn't happen on another 64-bit Windows 10 computer but does happen on another 32-bit Windows 7 computer. Can anyone reproduce it? Are there any other options that can be passed to

On 07/01/2015 5:20 PM, Jeroen Ooms wrote: > On Wed, Jan 7, 2015 at 8:00 AM, Duncan Murdoch <murdoch.duncan at gmail.com> wrote: >> >> This version includes only minor updates to the tools. I indicated last summer that I was hoping to update GCC from the current version 4.6.3 before the R 3.2.0 release, but this now looks unlikely, unless someone else with experience building

>>>>> Rui Barradas <ruipbarradas at sapo.pt> >>>>> on Wed, 12 Apr 2017 17:07:45 +0100 writes: > Hello, Inline. > Em 12-04-2017 16:40, Henric Winell escreveu: >> (Let's keep the discussion on-list -- I've added back >> R-devel.) >> >> On 2017-04-12 16:39, Ulrich Windl wrote: >>

Hi, I've noted that not all `predict' methods are hidden in the namespace: > methods("predict") [1] predict.ar* predict.Arima* [3] predict.arima0* predict.glm [5] predict.HoltWinters* predict.lm [7] predict.loess* predict.mlm [9] predict.nls* predict.poly [11] predict.ppr* predict.prcomp* [13]

Dear all, I'm analyzing a binary outcome using glm() with a binomial distribution and a logit link, and have now reached the point where I'd like to do some model checking. Since my data are in binary form I'd like to collapse over the cross-classification of the factors before the model checking. Are there any nice and simple ways doing this? If so, how? If not, I'd be

Hi everyone, I've tried the below on R 1.9.1 and the 2004-08-30 builds of R 1.9.1 Patched and R 2.0.0 on Windows 2000, and the results are consistent. > seq(0.5, 0, by = -0.1) [1] 0.5 0.4 0.3 0.2 0.1 0.0 > seq(0.7, 0, by = -0.1) [1] 7.000000e-01 6.000000e-01 5.000000e-01 4.000000e-01 3.000000e-01 2.000000e-01 1.000000e-01 -1.110223e-16 Is this really the intended behaviour?