similar to: How to automate this model selection algorithm?

Below is a version of [.data.frame that is faster for subscripting rows of large data frames; it avoids calling duplicated(rows) if there is no need to check for duplicate row names, when: i is logical attr(x, "dup.row.names") is not NULL (S+ compatibility) i is numeric and negative i is strictly increasing "[.data.frame" <- function (x, i, j,

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

Dear Ivan, thanks a lot, that is helpful. Still, I feel that default partial matching cripples the functionality of data.frame for larger tables. Thanks again and best regards Hilmar On 12.12.23 13:55, Ivan Krylov wrote: > ? Mon, 11 Dec 2023 21:11:48 +0100 > Hilmar Berger via R-devel <r-devel at r-project.org> ?????: > >> What was unexpected is that in this case was that

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).

#I am trying to understand how R fits models for contrasts in a #simple one-way anova. This is an example, I am not stupid enough to want #to simultaneously apply all of these contrasts to real data. With a few #exceptions, the tests that I would compute by hand (or by other software) #will give the same t or F statistics. It is the contrast estimates that R produces #that I can't seem to

Hi all, I started using R about 3 weeks ago, and now I've pretty much figured out how to do the types of statistical modeling, graphs, tables etc. that I frequently  use (with zero background in computer languages or other statistical packages that are similar to R like S or SAS!). So it's been a  quite  rewarding process so far, and I thank you all R gurus for all your generous help! That

Thank you David. Using xtabs operation simplifies the code very much, many thanks ;) On Tue, Jun 6, 2017 at 7:44 AM, David Winsemius <dwinsemius at comcast.net> wrote: > > > On Jun 6, 2017, at 4:01 AM, Jim Lemon <drjimlemon at gmail.com> wrote: > > > > Hi Bogdan, > > Kinda messy, but: > > > > N <-

Thank you for your answers and suggestions! Well, I want to do some experiments about the compiler optimization in llvm under SPM and I need performance evaluation. As I know, alpha is a general-purpose CPU, so I consider that arm may be an alternative. Simplesim-arm is able to do performance simulation while m5-arm is still under development. Then, what should I do? Do the experiment under

Simple matrix indexing suffices without any fancier functionality. ## First convert M and N to character vectors -- which they should have been in the first place! M <- sort(as.character(M[,1])) N <- sort(as.character(N[,1])) ## This could be a one-liner, but I'll split it up for clarity. res <-matrix(NA, length(M),length(N),dimnames = list(M,N)) res[as.matrix(C[,2:1])] <-

> On Jun 6, 2017, at 4:01 AM, Jim Lemon <drjimlemon at gmail.com> wrote: > > Hi Bogdan, > Kinda messy, but: > > N <- data.frame(N=c("n1","n2","n3","n4")) > M <- data.frame(M=c("m1","m2","m3","m4","m5")) > C <-

Given four known and fixed vectors, x1,x2,x3,x4, I am trying to generate a fifth vector,z, with specified known and fixed partial correlations. How can I do this? In the past I have used the following (thanks to Greg Snow) to generate a fifth vector based on zero order correlations---however I'd like to modify it so that it can generate a fifth vector with specific partial

Here's another approach: N <- data.frame(N=c("n1","n2","n3","n4")) M <- data.frame(M=c("m1","m2","m3","m4","m5")) C <- data.frame(n=c("n1","n2","n3"), m=c("m1","m1","m3"), I=c(100,300,400)) # Rebuild the factors using M and N C$m <-

Hi all, Has anyone tried using Asterisk as an SCCP client? My company has just signed up a 2 year agreement with M5 (fools!!) but are having intellectual issues with things like intra office phone calls and voice mail etc. They suddenly realized after M5 was installed that ALL their calls go out to the Internet and back and they don't like it. M5 uses SCCP. Could an Asterisk box be

Hi to all the people again, I was writting a simply function in R, and wish to collect the results in a excel file. The work goes as follows, Ciervos<-function(K1, K0, A, R,M,Pi,Hembras) {B<-(K1-K0)/A T1<-(R*Pi*Hembras-M*Pi+B)/(Pi-M*Pi+R*Pi*Hembras) P1<-Pi-B R1<-P1*Hembras*R M1<-P1*M T2<-(R1-M1+B)/(P1-M1+R1) P2<-P1-B R2<-P2*Hembras*R M2<-P2*M

Two questions related to creating animated movies with rgl: 1. I've created an rgl scene with 5 different views I want to display in a movie, but I'd like to insert pauses (say, 5 seconds) at each view. How can I do this? I first created 5 userMatrix's, then play3d( par3dinterp( userMatrix=list(M1, M2, M3, M4, M5)), ,duration=2*60/5) ) then tried simply repeating each twice,

NB: This email and its contents are subject to the Eskom Holdings Limited EMAIL LEGAL NOTICE which can be viewed at http://www.eskom.co.za/email_legalnotice ------------------------------------------------------------------------ Good Morning Im busy doing an exercise on Foundation of Statistical Description and Analysis,Please check the attached exercise on page 3.In this exercise the first

Hi all, I am new to LLVM (even the field of compiler) and currently I am engaged in the work of adapting LLVM IR to M5 simulator to observe the enhancement of the novel architecture we design. Simply speaking if you know little about M5, my aim is to know how LLVM IR is interpreted and encoded, then try to implement it in the framework of M5. I have read the LLVM documents, yet I still have some

Dear R-Gurus I am a PhD student from South Africa working on chimpanzee behaviour. I am looking at patterns of shade utilization and am using generalized linear mixed models to examine the effects of various factors on whether chimpanzees choose to spend time in the sun or shade. I realise that the lme4 package and the outputs of the lmer functions have been discussed ad nauseum but I have been

Hi Bogdan, Kinda messy, but: N <- data.frame(N=c("n1","n2","n3","n4")) M <- data.frame(M=c("m1","m2","m3","m4","m5")) C <- data.frame(n=c("n1","n2","n3"), m=c("m1","m1","m3"), I=c(100,300,400))

Hi, Selon "Zhang, Xiantao" <xiantao.zhang at intel.com>: > >From 5f82ea88c095cf89cbae920944c05e578f35365f Mon Sep 17 00:00:00 2001 > From: Xiantao Zhang <xiantao.zhang at intel.com> > Date: Wed, 12 Mar 2008 14:48:09 +0800 > Subject: [PATCH] kvm/ia64: Add mmio decoder for kvm/ia64. [...] > + post_update = (inst.M5.i << 7) + inst.M5.imm7; > + if