similar to: match.arg()

Here is a suggestion for improving match.arg() Comments welcome. Best Jens Oehlschl?gel # up to now match.arg() works as t1 <- function( param = c("default", "alternative1", "alternative2") ){ param <- match.arg(param) param } # and args(t1) # > function (param = c("default", "alternative1", "alternative2")) #

Full_Name: Kjetil Kjernsmo Version: Version 1.0.0 OS: osf1 Submission from: (NULL) (129.240.28.227) Hello again! This is a follow-up to my message on R-help about a problem with match.arg() I have a little more on the topic, but not much really. First, it was not entirely true what I wrote in r-help that I call match.arg() in my ramp(), because ramp() just calls qamp() with runif() as

> Thanks. This is also present in the current pre-1.3.0. Your patch > looks correct, but I wonder if the default for "enclos" > should not be > environment(formula(model)) rather than parent.frame() as it is now? > > (And wouldn't it be better named "envir"?) Peter, I was merely making an incremental improvement, your suggestions provide

Hi, I've installed recently OpenOffice.org 3.1 on Centos 5.3. I use OOo in headless mode. I have no problem when running as root, but I'd prefer run it as non-root user (oooserver user). When I run the script in debug mode this is the output: sh -x /tmp/ooo-headless-nonroot start + . /etc/rc.d/init.d/functions ++ TEXTDOMAIN=initscripts ++ umask 022 ++

When attempting to merge 3 data frames, one of which has fewer columns than the others, rbind.data.frame correctly refuses to perform the bind. However, the error message given is a bit obscure due to a logical bug in the match.names() internal function to rbind.data.frame. Illustration: ## Three data frames with same column variable names: > foo <- data.frame(v1 = c('a',

Dear useRs, I was searching CRAN for implementation of kurtosis and skewness tests, and found that there is some kind of lack on it. So, I have written three functions: 1. Anscombe-Glynn test for kurtosis 2. Bonett-Seier test based on Geary's kurtosis (which is not widely known, but I was inspired by original paper describing it, found coincidentally in Elsevier database) 3.

> -- Now to the ``real'' one : -- > > Martyn> 3) I have no idea what's going on here. > > >> c(r=1,s=2) > Martyn> s > Martyn> 2 > >> c(u=100,b=4) > Martyn> b > Martyn> 4 > > Martyn> This seems to happen only for "r" and "u". Other single-letter > Martyn> names

Hello there, I am not quite sure how to interpret the output of Rprof (in the following the output I was staring at). I was poking around the web a little bit for documentation but without much success. I guess if I want to figure out what takes so long in my code the 2nd table $by.total and the total.pct column (pct = percent) is the most helpful. What does it mean that [ or [.data.frame is

If a function needs to be passed as an argument to another function, default arguments to the function being passed are lost. Consider this example: fun1 <- function(x, A=c("power","constant")) { arg <- match.arg(A) cat(paste("A is:",paste(A,collapse=", "),"\narg is:",arg,"\n")) cat("formals:\n") print(formals())

I am using R0.64.1 under Windows 95. The help for `pmatch' states that: If `duplicates.ok' is false multiple matches will result in the value of `nomatch' being returned, and if it is true, the index of the first matching value will be returned. The help for pmatch gives the following examples: pmatch("m", c("mean",

I found this tracing a bug when experimenting with a new sort() function using match.arg(). It was triggered because mosaicplot.default(.) has an argument called `sort' and calls itself the sort() function in which I was using match.arg()... Here is (input for) a small clean example : #### bad match.arg() // sys.function() bug : #### MM, 22.3.2002 callme <- function(a = 1, mm =

Hi Hilmar and Ivan, I have used your code examples to write a blog post about this topic, which has figures that show the asymptotic time complexity of the various approaches, https://tdhock.github.io/blog/2023/df-partial-match/ The asymptotic complexity of partial matching appears to be quadratic O(N^2) whereas the other approaches are asymptotically faster: linear O(N) or log-linear O(N log N).

On Wed, 13 Dec 2023 09:04:18 +0100 Hilmar Berger via R-devel <r-devel at r-project.org> wrote: > Still, I feel that default partial matching cripples the functionality > of data.frame for larger tables. Changing the default now would require a long deprecation cycle to give everyone who uses `[.data.frame` and relies on partial matching (whether they know it or not) enough time to

On 05/04/2019 10:46 a.m., Therneau, Terry M., Ph.D. wrote: > > > On 4/5/19 9:39 AM, Duncan Murdoch wrote: >> On 05/04/2019 10:19 a.m., Therneau, Terry M., Ph.D. wrote: >>> Duncan, >>> ?? I should have included it in my original note, but >>> >>> ??? ? all.equal(unclass(t0x), unclass(t1x)) >>> >>> returns TRUE as well.? I had

The pmatch help (see also section 4.3.2 in the R Language Definition) claims that pmatch with duplicates.ok=FALSE provides the same functionality as R's argument matching algorithm, modulo how empty strings are matched. Here's an undocumented inconsistency between pmatch and R's argument matching algorithm: > sessionInfo() R version 3.0.1 (2013-05-16) Platform:

Full_Name: Dirk Koschuetzki Version: 2.1.1 OS: source code Submission from: (NULL) (194.94.136.34) Hello, >From the source code (R-2.1.1, file: .../R-2.1.1/src/library/stats/R/) ****************************** cor.test.default <- function(x, y, alternative = c("two.sided", "less", "greater"), method = c("pearson", "kendall",

Hi folks, Can anyone suggest an efficient way to do "matching without replacement", or "one-to-one matching"? pmatch() doesn't quite provide what I need... For example, lookupTable <- c("a","b","c","d","e","f") matchSample <- c("a","a","b","d") ##Normal match()

Hello, I wuold like to propose my modifications of the original cor.test to you : I tried to calcolate the correct p-value for Spearman and Kendall's test with ties. Let me know what you think. Thanks you for your time. Antonietta di Salvatore test <- function(x, ...) UseMethod("test") test.default <- function(x, y, alternative = c("two.sided",

Hi All, I'm having a few problems using match and a lookup table, previous Googling show numerous solutions to matching a lookup table to a dataset, My situation is slightly different as i have multiple lookup tables, (that i cannot merge - for integrity reasons) that i wish to match against my data, and each of these files is large, so lots of for / if conditions are not ideal. (withstanding

I was able to track down the problem that causes R-Swig to fail in 2.6. In 2.5, I am able to use the set method against '$' and '$<-' to cause expressions like r$d to get dispatched to get and set functions. This no longer seems to work in 2.6, and I was wondering if anyone can give information as why. The function that are failing are at the end of this e-mail. In 2.5 a