similar to: apply function

Peter, I thought this one was noted and fixed, but I could be wrong. R : Copyright 1999, The R Development Core Team Version 0.63.3 (March 6, 1999) .... [Previously saved workspace restored] > tabulate(1:10, 5) Process R:1 segmentation fault at Sat Apr 3 17:48:34 1999 -- (The following contact details become official on 1 May 1999, but the email

Is there anyone who is currently using Asterisk as a production H323 gateway? And using which combination of asterisk and H323 (chan_h323, chan_oh323?) The main issue is interoperability with other H323 parties (Cisco AS53xx, Nextone, etc). Searching the mailing list it seems that both h323 and oh323 are not so stable, is it only an impression or using h323 is really not so advisable?

Hi, I'd like to ask you a question again. It is basically about data frames, NAs and tabulate function. I have this data frame. I already used this in one of the previous questions of mine. It intentionally looks this simple, my real 'df' dataframe is much bigger actually and again, I am not willing to annoy anyone with huge databases... So, my database: id

I'd like to add a custom #pragma directive in clang for research purposes. I'm familiar with the LLVM pass-writing interface but new to the rest of LLVM and clang. I've looked into Simone Pellegrini's work on a #pragma interface (https://github.com/motonacciu/clomp and http://llvm.org/devmtg/2013-04/pellegrini-slides.pdf), but I'm in no position to determine if this approach

tabulate has .C("R_tabulate", as.integer(bin), as.integer(length(bin)), as.integer(nbins), ans = integer(nbins), PACKAGE="base")$ans The implementation of R_tabulate has if(x[i] != R_NaInt && x[i] > 0 && x[i] <= *nbin) and so copes with (silently drops) NA. Perhaps the .C could have NAOK=TRUE? This is useful in apply'ing tabulate to

|> x <- sample(0:2, 10, replace = TRUE) |> x [1] 1 0 2 1 0 2 2 0 2 1 |> tabulate(x) [1] 3 4 |> table(x) x 0 1 2 3 3 4 B. > On Nov 10, 2017, at 4:32 AM, Allaisone 1 <allaisone1 at hotmail.com> wrote: > > > > Thank you for your effort Bert.., > > > I knew what is the problem now, the values (1,2,3) were only an example. The values I have are

Hi, To clarify the default behavior that Boris is referencing below, note the definition of the 'bin' argument to the tabulate() function: bin: a numeric vector ***(of positive integers)***, or a factor. Long vectors are supported. I added the asterisks for emphasis. This is also noted in the examples used for the function in ?tabulate at the bottom of the help page. The second

Hallo, we have started playing with asterisk about one month ago, and we do like very much what we are experiencing. Now we would like to take some step further towards "standardizing" installed modules, functionalities, tools etc. The "wall" we are facing now is: choosing the right tool for * management. We tried AMP, very powerful but incomplete (CAPI is very important to

dear helplist, my student has fifty trees, numbered one to fifty, and a vector recording which tree a certain possum slept in on 12 nights. R> c [1] 3 14 17 22 26 26 17 40 43 25 46 46 R> Thus it slept in tree #3 on Monday, then tree #14 on Tues, and so on. I wish to test the null hypothesis that the animal chooses trees randomly; try R> table(c) c 3 14 17 22 25 26 40 43 46 1 1

On 07/04/2012 10:35 AM, Raghavendra, Prakash wrote: > Hi > > I wanted to know the status of OpenMP (or in general auto > parallelization) support in > > LLVM. > > I read some threads discussing about possible effort in that direction > in 3.1 onwards, > > but did not see any further discussion on that. > > If someone in the list knows the updated info or can

Hello, This sounds like a problem to which many solutions should exist, but I did not manage to find one. Basically, given a list of datapoints, I'd like to keep those within the X% percentile highest density. That would be equivalent to retain only points within a given line of a contour plot. Thanks to anybody who could let me know which function I could use! Best, Emmanuel

Hi, I posted a message earlier entitled "How to fit a line through the "Mountain crest" ..." I figured loess is probably the best way, but it seems that the problem is the robustness of the fit. Below I paste an example to illustrate the problem: tmp=rnorm(2000) X.background = 5+tmp; Y.background = 5+ (10*tmp+rnorm(2000)) X.specific = 3.5+3*runif(1000);

https://bugzilla.mindrot.org/show_bug.cgi?id=1008 DarioP <pellegrini.dario at gmail.com> changed: What |Removed |Added ---------------------------------------------------------------------------- CC| |pellegrini.dario at gmail.com --- Comment #11 from DarioP <pellegrini.dario at gmail.com> --- (In reply to

Hi, I'm wondering how can I change the position of xlab in truehist. For example, the following code creats a histogram with 4 bins for my discrete data. I want each bin to be labelled as 0, 1, 2, or 3 in the middle, so that it's clear each bin corresponds to each of the discrete case. I was thinking of first delete xlab and then add marks myself, but it doesn't look like it's

Hi all, I've searched high and low on this and found nothing of help. I'm using v2.6.2 and trying to write a function that will count how many people from a dataset fall under a poverty line of 50% of the mean income. a9 is my 100-element vector of incomes. I want pa9 to be my vector that counts how many of these are classed as in poverty. My problem is that my command if(a9[i] <

Hi, table1D() below can be up to 60x faster than base::table() for the 1D case. Here are the detailed speedups compared to base::table(). o With a logical vector of length 5M: 11x faster (or more if 'useNA="always"') o With factor/integer/numeric/character of length 1M and 9 levels (or 9 distinct values for non-factors):

Hi, May be this helps: ?tb[1,]%*%(((val-rep(meansb79[1,],5))^2)/6) #??????? [,1] #[1,] 1.47619 tryvarb<-c(1,2,3,4,4,4,4) ?var(tryvarb) #[1] 1.47619 tb[2,]%*%(((val-rep(meansb79[2,],5))^2)/6) #???????? [,1] #[1,] 1.904762 sapply(seq_len(nrow(tb)),function(i) tb[i,]%*%(((val-rep(meansb79[i,],5))^2/6))) # [1] 1.4761905 1.9047619 1.9047619 1.9047619 1.9047619 2.2857143 1.9047619 # [8] 1.9047619

Is the phrase "index <- match(x, sort(unique(x)))" reliable, in the sense that it will never return NA? Context: Calculation of survival curves involves the concept of unique death times. I've had reported cases in the past where survfit failed, and it was due to the fact that two "differ by machine precision" values would sometimes match and sometimes not,

I've had a need for multinomial "random number generation" occasionally. And other people too. The following code is currently in the (very small ``not very high importance'') CRAN package normix --- which I will rename to "nor1mix" very seen because of a ``name registration'' problem I want to add "this" (well the functionality) to a

Hi, I am an R novice and I am trying to do something that it seems should be fairly simple, but I can't quite figure it out and I must not be using the right words when I search for answers. I have a dataset with a number of individuals and observations for each day (7 possible codes plus missing data) So it looks something like this Individual A, B, C, D Day1 1,1,1,1 Day 2 1,3,4,2 Day3