similar to: Need some help reproducing a graph

I can take the results of a simulation with one random variable and generate an empirical interval that contains 95% of the observations, e.g., x <- rnorm(10000) quantile(x,probs=c(0.025,0.975)) Is there an R function that can take the results from two random variables and generate an empirical ellipse that contains 95% of the observations, e.g., x <- rnorm(10000) y <- rnorm(10000) ?

Hello, can anybody tell me how to produce a plot like the one in http://cran.r-project.org/web/packages/lme4/vignettes/Implementation.pdf on page 13, Figure 6? The data is stored in: library(nlme) data(Oats) Cheers -- View this message in context: http://r.789695.n4.nabble.com/Help-needed-in-reproducing-a-plot-tp4119603p4119603.html Sent from the R help mailing list archive at

This is probably a trivial question but the way I used to do this turns out to be flawed. I used run clang with -O0 -emit-llvm and then use the resulting IR with opt -O3. The problem is that clang at -O0 does not generate TBAA metadata so the passes run by opt behave differently. What’s the recommended way of doing this? Thanks, Adam

Dear List: I am having some trouble reproducing some GLS estimates using matrix operations that I am not having with other R procedures. Here are some sample data to see what I am doing along with all code: mu<-c(100,150,200,250) Sigma<-matrix(c(400,80,16,3.2,80,400,80,16,16,80,400,80,3.2,16,80,400),n c=4) sample.size<-100 temp <-

Hi I have an action which saves the originating resource from which it was called, and returns to it later. This is done by setting: session[:return_to] = request.referer ... redirect_to(session[:return_to]) Which works great for GET resources. The problem is that POST variables aren''t kept in request.referer, so this method fails when the originating resource is a a POST.

Hello, I'd like to reproduce the standard biplot with pc.biplot=TRUE using xyplot in lattice in order to assign different symbols to groupings of observations (similar to the example in fig. 11.2 on page 285 in DAAG). In order to reproduce the biplots I need to know how to scale the observations and variables. In ?biplot.princomp I found that the variables are scaled by 'lambda ^

Are the packaging scripts used to produce the clang+llvm binaries on llvm.org under version control? If so, can you please point me to them? Thanks, Greg

On Tuesday 09 July 2013 15:11:03 Greg Fitzgerald wrote: > Are the packaging scripts used to produce the clang+llvm binaries on > llvm.org under version control? If so, can you please point me to > them? The script used for building the binaries can be found under llvm : utils/release/test-release.sh The packages are mere tarballs of the installation dir. Cheers, > > Thanks,

On Fri, Apr 11, 2014 at 12:35 AM, Kostya Serebryany <kcc at google.com> wrote: > > I wonder if we can configure the lit test runner to print the (tail of) > test output on failure. > You'd have to teach FileCheck, actually, since it's the one that consumes stderr in this case. Rather than doing that, why not use *SAN_OPTIONS=log_file=blah.txt, and teach lit to dump that

Greetings, I am doing many iterations of simple optimizations using the L-BFGS-B method of optim() in R-1.6.2. Very rarely (about 1 in every 100,000 trials), a process will encounter some problem in optim that triggers the error message "non-finite value supplied by optim". From the C code, it appears that this message could be emitted

Dear R-Users, I'm currently studying the book Statistical model in S by Chambers J.M and Hastie T.J.. At page n 3 there's a plot showing the means of a variable at each of the levels of the factors of an experiment. I hope to be able to reproduce it here by ASCII art. B6 S | | Thin | | | | | | B3 __|_____|_____|__......etc | |

Hi everybody, I'm a newbye with lots of problems :). I'm trying to use nlsystemfit, but I recieve two error messages whose origin that I don't understand. 1) When I try to reproduce the example reported in the systemfit package manual, that is library( systemfit ) data( ppine ) hg.formula <- hg ~ exp( h0 + h1*log(tht) + h2*tht^2 + h3*elev + h4*cr) dg.formula <- dg ~ exp( d0

Dear R-community! I still have the problem reproducing the following example using lme. id<-factor(rep(rep(1:5,rep(3,5)),3)) factA <- factor(rep(c("a1","a2","a3"),rep(15,3))) factB <- factor(rep(c("B1","B2","B3"),15)) Y<-numeric(length=45) Y[ 1: 9]<-c(56,52,48,57,54,46,55,51,51) Y[10:18]<-c(58,51,50,54,53,46,54,50,49)

> The script used for building the binaries can be found under llvm : > utils/release/test-release.sh Thanks! A few follow-up questions: > # Phase 3: Build llvmCore with newly built clang from phase 2. > c_compiler="$gcc_compiler -fplugin=$dragonegg_phase2_objdir/dragonegg.so" > cxx_compiler="$gxx_compiler

There seems to be a minor problem with reproducing numbers from rnorm with Box-Muller. The pattern suggests it might have something to do with the value that gets dropped when an odd number of numbers is requested. (Details below.) Also, could "user-supplied" be added as an option for normal.kind in RNGkind. I'm sure the Box-Muller in R is better than my own attempt, but I would

On Wed, Apr 18, 2018 at 5:45 PM, Michael Zolotukhin via llvm-dev <llvm-dev at lists.llvm.org> wrote: > Hi, > > Recently I committed a change (r330175) that passed all my testing, but > failed on several bots. Namely, these are the failed ones: > http://lab.llvm.org:8011/builders/clang-with-thin-lto-ubuntu/builds/9803 >

On Dec 7, 2007, at 1:43 AM, Wojciech Matyjewicz wrote: > Recently, I was looking into the bug #1227. I wanted to check if > reordering optimization passes could solve it. To start with, I > tried to > reproduce the output of llvm-g++ -O3 using the combination of llvm-g++ > -O0 and opt with the appropriate passes. However, I was unable to. I > use > SVN versions of llvm and

Hi, (Reposting the request to llvm-dev for better visibility) I had to revert a change today, due to test errors on some buildbots. The errors did seem to only happen on the ppc64be and aarch64 buildbots. I don't have access to a ppc64be setup to test on, but I've tried to build and run in a setup very similar to the one on the aarch64 buildbot, but I can't reproduce the errors

I was playing around with the ghyp package and simulated series of t-distributed variables when suddenly i was not able to reproduce the log likelihood values reported by the package. When trying to reproduce the likelihood values, I summed the log(dt(x,v)) values and it worked with some simulated series but not all. Is there any obvious flaws with this script? library("ghyp")

So I think I am able to reproduce the dropped call scenario. Here is what I do to get a dropped call: 1. Call 1-800-tmobile 2. Go true their IVR and get connected to the customer service IVR 3. Enter my number and SSN 4. press 0 5. Then the audio please hold starts. After about 2-4 seconds the call gets dropped. (fast busy tone) The time on my phone will stop running (call time) and I will get