search for: 0.0049

Hi All, I have am using Sweave and the \Sexpr{} to place some numeric variables in my tex document. I want to format the number prior to entry so they read slightly more elegantly. Say i have the following numbers x <- 0.00487324 y <- 0.000000432 z <- 0.567 I would like to have the numbers displayed as follows x1 <- 0.0049 y1 <- 0.00000043 z1 <- 0.57 I've seen i can use

Dear all, I'm using R 2.8.1 under Windows Vista on a dual core 2,4 GhZ with 4 GB of RAM. I'm trying to reproduce a result out of "Analysis of Financial Time Series" by Ruey Tsay. In R I'm using the fGarch library. After fitting a ar(3)-garch(1,1)-model > model<-garchFit(~arma(3,0)+garch(1,1), analyse) I'm saving the results via > result<-model

Dear R-helpers, I am trying to plot a Bland-Altman-Plot using the BA.plot function from the package MethComp. While there is a function to transform the values for analysis as shown in the snippet below, I would like to have logarithmic axes for display as well. The usual log = 'xy' does not work because of the properties of the y-asxis (positive and negative values). I am sure that

Hi Bob, are these performance regressions real? They look pretty serious. Ciao, Duncan. On 10/12/11 09:40, llvm-testresults at cs.uiuc.edu wrote: > > bwilson__llvm-gcc_PROD__i386 nightly tester results > > URL http://llvm.org/perf/db_default/simple/nts/332/ > Nickname bwilson__llvm-gcc_PROD__i386:4 > Name curlew.apple.com > > Run ID Order Start Time End Time >

Hi, If I used drop1 in a weighted lm fit, it seems to ignore the weights in the AIC calculation of the dropped terms, see the example below. Can this be right? Yan -------------------- library(car) > unweighted.model <- lm(trSex ~ (river+length +depth)^2- length:depth, dno2) > Anova(unweighted.model) Anova Table (Type II tests) Response: trSex Sum Sq Df F value

Yes, they are real. I re-ran the two tests with the biggest execution time regressions, and the results were completely reproducible. On Oct 12, 2011, at 1:24 AM, Duncan Sands wrote: > Hi Bob, are these performance regressions real? They look pretty serious. > > Ciao, Duncan. > > On 10/12/11 09:40, llvm-testresults at cs.uiuc.edu wrote: >> >>

Hi all, I am not a very proficient R-user yet, so I hope I am not wasting people?s time. I want to run a linear mixed model with 3 random factors (A, B, C) where A and B are partially crossed and C is nested within B. I understand that this is not easily possible using lme but it might be using lmer. I encountered two problems when trying: Firstly, I can enter two random factors in lmer but

Hi, I am having difficulty understanding some lme output -- I haven't found too many examples to help explain to me how to interpret the coefficients and would appreciate any help. I am fitting a model: fit <- lme(y ~ pre + group + time + group:time, random=~1|subject, na.action=na.omit, data=mydata) ...for a dataset where there are two groups being followed over time. pre is

Dear, We are trying to determine the (one-sided) CI for the coefficient of variation in a small sample (say n = 10), with mean 100 and standard deviation 21. It appears though that the R-function ci.cv() and our simulation do not agree. The R-code: library(MBESS) n = 10 ci.cv(mean = 100, sd = 21, n = 10, conf.level = 0.9) U10.95 <- 0.3551754 ci.cv(mean = 100, sd = 21, n = 10, conf.level =

Hi, Does anyone know if Asterisk is a good tool to be used for a large quantity of g711 and g729 transcoding? What is the best alternative for that? -- Woody Dickson woodydickson at gmail.com <woody.dickson at gmail.com> US and Worldwide Termination ============ Contact me for the following offering ============ USA Onnet - 0.0049/min USA Offnet - 0.011/min USA Mobile starting

Dear friends, I used R and SAS to analyze my data through generalized linear model, and there is some difference between them. Results from R: glm(formula = snail ~ grass + gheight + humidity + altitude + soiltemr + airtemr, family = Gamma) Deviance Residuals: Min 1Q Median 3Q Max -1.23873 -0.41123 -0.08703 0.24339 1.21435 Coefficients:

Dear all, I have been trying to investigate the behaviour of different weights in weighted regression for a dataset with lots of missing data. As a start I simulated some data using the following: library(MASS) N <- 200 sigma <- matrix(c(1, .5, .5, 1), nrow = 2) sim.set <- as.data.frame(mvrnorm(N, c(0, 0), sigma)) colnames(sim.set) <- c('x1', 'x2') # x1 & x2 are

Hi all, today we had a performance issue transfering a big amount of data where one file was over 50GB. Rsync was tunneled over SSH and we expected the data to be synced within hours. However after over 10 hours the data is still not synced ... The sending box has rsync running with 60-80 % CPU load (2GHz Pentium 4) while the receiver is nearly idle. So far I had no acces to the poblematic

I'm fitting generalized linear mixed models to using several fixed effects (main effects and a couple of interactions) and a grouping factor (site) to explain the variation in a dichotomous response variable (family=binomial). I wanted to compare the output I obtained using PROC GLIMMIX in SAS with that obtained using lmer in R (version 2.6.1 in Windows). When using lmer I'm specifying

Dear all R-users, I wanted to fit a Garch(1,1) model to a dataset by: >garch1 = garch(na.omit(dat)) But I got a warning message while executing, which is: >Warning message: >NaNs produced in: sqrt(pred$e) The garch parameters that I got are: > garch1 Call: garch(x = na.omit(dat)) Coefficient(s): a0 a1 b1 1.212e-04 1.001e+00 1.111e-14 Can any one

Hi all, I've started looking at the GlobalMerge pass, enabled by default on ARM and AArch64. I think we should reconsider that, at least for AArch64. As is, the pass just merges all globals together, in groups of 4KB (AArch64, 128B on ARM). At the time it was enabled, the general thinking was "it's almost free, it doesn't affect performance much, we might as well use it".

On 23.11.2012, at 15:12, john skaller <skaller at users.sourceforge.net> wrote: > > On 23/11/2012, at 5:46 PM, Sean Silva wrote: > >> Adding LLVMdev, since this is intimately related to the optimization passes. >> >>> I think this is roughly because some function level optimisations are >>> worse than O(N) in the number of instructions. >>

Target: FreeBSD 7.0-RC1 on amd64. autoconf says: configure:2122: checking build system type configure:2140: result: x86_64-unknown-freebsd7.0 [...] configure:2721: gcc -v >&5 Using built-in specs. Target: amd64-undermydesk-freebsd Configured with: FreeBSD/amd64 system compiler Thread model: posix gcc version 4.2.1 20070719 [FreeBSD] [...] objdir != srcdir, for both llvm and gcc. Release

Most of libc++ doesn't have explicit template instantiations, which leads to a pretty significant build time and code size cost when using libc++, since a large number of common templates will be emitted by the compiler and coalesced by the linker. Notably, in include/__config, we have: #ifndef _LIBCPP_EXTERN_TEMPLATE #define _LIBCPP_EXTERN_TEMPLATE(...) #endif whereas before

Hi, Below you can see the updated benchmark results for the new SLP-vectorizer. As you can see, there is a small number of compile time regressions, a single major runtime *regression, and many performance gains. There is a tiny increase in code size: 30k for the whole test-suite. Based on the numbers below I would like to enable the SLP-vectorizer by default for -O3. Please let me know if you