similar to: Quicker quantiles?

Hi, I've got two suggestions how to speed up median() about 50%. For all iterative methods calling median() in the loops this has a major impact. The second suggestion will apply to other methods too. This is what the functions look like today: > median function (x, na.rm = FALSE) { if (is.factor(x) || mode(x) != "numeric") stop("need numeric data")

Hi, I was wondering whether there is any Fortran function or associated library for evaluating the quantiles of a set of values (something which the R-function quantile() does). Any help will be much appreciated. Thanks and regards, Dhiman Bhadra [[alternative HTML version deleted]]

I sent the message below to r-sig-mac yesterday, but having no reply I decided to explore a bit myself and found that editing: /Library/Frameworks/R.framework/Versions/2.3/Resources/share/make/ shlib.mk yzzy: diff shlib.mk shlib.mk~ 3c3 < include $(R_HOME)/etc/Makeconf --- > include $(R_HOME)/etc${R_ARCH}/Makeconf restored the functionality of R CMD INSTALL. Is this a known issue?

Hi, This is essentially a reposting of http://tolstoy.newcastle.edu.au/R/devel/05/11/3305.html which had no responses, and the behaviour reported there persists in r-devel as of yesterday. (1) sort() with non-null partial > x = rnorm(100000) > keep = as.integer(ppoints(10000) * 100000) > system.time(sort(x)) [1] 0.05 0.00 0.04 0.00 0.00 > system.time(sort(x, partial = keep)) [1]

Does any have installation instructions for this? When I run install.packages('quantreg') I get: gcc -std=gnu99 -shared -o quantreg.so akj.o boot.o brute.o chlfct.o cholesky.o combos.o crq.o crqfnb.o dsel05.o etime.o extract.o idmin.o iswap.o kuantile.o mcmb.o penalty.o powell.o rls.o rq0.o rq1.o rqbr.o rqfn.o rqfnb.o rqfnc.o sparskit2.o srqfn.o srqfnc.o srtpai.o -llapack -lblas

Hey guys, I tried to program quicksort like this but somethings wrong. please help >partition <- function(x, links, rechts){ > > i <- links > j <- rechts > t <- 0 > pivot <- sample(x[i:j],1) > > while(i <= j){ > > while(x[i] <= pivot){ > i = i+1} > > while(x[j] >= pivot){ > j = j-1} > > if( i

Dear R-devel: The Cluster administrators at KU got enthusiastic about testing R-3.2.2 with Intel MKL when I asked for some BLAS integration. Below I forward a performance report, which is encouraging, and thought you would like to know the numbers. Appears to my untrained eye there are some extraordinary speedups on Cholesky decomposition, determinants, and matrix inversion. They had

On Wed, Oct 3, 2012 at 10:15 AM, Matthieu Moy <Matthieu.Moy at grenoble-inp.fr> wrote: > Preston Briggs <preston.briggs at gmail.com> writes: >> Think about costs asymptotically; that's what matters. Calls and >> returns require constant time, just like addition and multiplication. > > Constant time, but not necessarily constant memory. > > Deep recursion

> Only if the recursion is very deep. In practice, a recursive descent > parser isn't going to run out of stack space, nor will a quicksort or > binary-tree walker, The recursive-descent parser case has happened in practice: http://my.opera.com/hallvors/blog/2012/07/17/twitter-crashes-itself-with-commas?1 Also, I've seen some recursion-related PR's in Clang, although I think

Dear R users, I am trying to minimize the distance between my data points and theoretical gamma distribution over shape and scale parameters. the function "mde" from actuar package does it for empirical distribution function and theoretical gamma distribution. However, I would like to minimize the distance by using only the data between 0.1 and 0.9 quantiles. I cannot use ecdf in this

Hi all, I would like to set the following configuration : - A Samba / LDAP PDC (name : rivest). - A NFS / Samba filer (name : brahms). Brahms is a member of my domain. I would like to store my windows profiles in brahms (into [profile] share). Here is brahms setup : [global] workgroup = MAPMO security = domain netbios name = brahms server string

I can't figure out how to verify flac md5 signatures. The docs say flac does an md5 over the unencoded audio data, but I can't reproduce it. I'm using flac 1.0.2 and Ron Rivest's reference md5 implementation. $ md5 temp.wav MD5 (temp.wav) = a7c92c4a656b2be2582f77c51b68a623 $ flac temp.wav $ metaflac temp.flac file: temp.flac METADATA block #0 ... MD5 signature:

i know this is not about R. After applying quantile regression with t=0.5,0.6 on the data set WBC( Wisconsin Breast Cancer)with 678 observations and 9 independent variables(inp1,inp2,...inp9) and 1 dependent variable(op) i have got the following results for beta values. when t=0.5(median regression) beta values b1=0.002641,b2=0.045746,b3=0.

If one makes the reasonable assumption that Pct is much larger than Cutoff, sorting Cutoff is the expensive part e.g O(nlog2(n) for Quicksort (n = length Cutoff). I believe looping is O(n^2). Jeff's approach using findInterval may be faster. Of course implementation details matter. -- Bert On Mon, Oct 16, 2023 at 4:41?AM Leonard Mada <leo.mada at syonic.eu> wrote: > > Dear

sort() and order() are not quite the same, as "one knows": o order allows breaking ties by more than one argument; o sort allows a 'partial' and 'na.last' argument Still, the following timing (on a `simple' UltraSparc I) suggest that actually two different algorithms are used > N <- 10000 > typeof(x0 <- 1:N) # --- x0 : already sorted --- [1]

Hi Paul, We've been through this process ourselves for the Revolution R Open project. There are a number of pitfalls to avoid, but you can take a look at how we achieved it in the build scripts at: https://github.com/RevolutionAnalytics/RRO There are also some very useful notes in the R Installation guide: https://cran.r-project.org/doc/manuals/r-release/R-admin.html#BLAS Most packages do

Everyone, There is non-determinism in LLVM codegen in the following scenarios: 1. Between back-to-back runs of the same LLVM toolchain 2. Between Release vs Release+Asserts toolchains 3. Between Linux vs Windows toolchains The main reasons for the non-determinism in codegen are: 1. Iteration of unordered containers (like SmallPtrSet, DenseMap, etc) where the iteration order is undefined 2.

As part of checking Martin Maechler's observations of sorting efficiency I have been putting the code under the microscope. Part of this involved comparing the performance with Splus. I turns out that the R code is much slower. A good deal of this is due to the interpreted front end to the R verion of sort. It is worth taking a look at the sort function and seeing if you can see what the

Hi there, I got a data frame with more than 200k columns. How could I get median of each column fast? mapply is the fastest function I know for that, it's not yet satisfied though. It seems function "median" in R calculates median by "sort" and "mean". I am wondering if there is another function with better algorithm. Any hint? Thanks, Xin Zheng

Sadly you failed to set your email program to send plain text and the data is corrupted at my end. I also think you need to reduce the size of the data set... the intent here is to increase your understanding, not debug your particular analysis. I will say that I am having a very challenging time understanding what you are trying to accomplish though. What are the equations that you think need