search for: 0.0138

    I created a couple of timeSeries objects - when I was merging them , I got an error. Looking at the data , I see that one of the time series has   06/30/2007  0.0028       0.0183  0.0122      0.0042  0.0095    -          07/31/2007 -0.0111       0.0255  0.0096     -0.0069 -0.0024  0.0043       08/31/2007 -0.0108      -0.0237 -0.0062     -0.0138 -0.0173 -0.0065       09/30/2007 

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

Thanks. In this case below, what is "x"? I tried rownames(out) which did not work. Sorry. Does this sound like homework to you? On 7/12/2024 5:09 PM, Uwe Ligges wrote: > > > On 12.07.2024 10:54, Steven Yen wrote: >> Below is part a regression printout. How can I use "grep" to identify >> rows headed by variables (first column) with a certain label. In

Below is part a regression printout. How can I use "grep" to identify rows headed by variables (first column) with a certain label. In this case, I like to find variables containing "somewhath", "veryh",?"somewhatm", "verym", "somewhatc", "veryc","somewhatl", "veryl". The result should be an index 6:13 or

On 12.07.2024 10:54, Steven Yen wrote: > Below is part a regression printout. How can I use "grep" to identify > rows headed by variables (first column) with a certain label. In this > case, I like to find variables containing "somewhath", > "veryh",?"somewhatm", "verym", "somewhatc", "veryc","somewhatl",

On 09/09/2013 05:18 AM, Star Tan wrote: > > At 2013-09-09 05:52:35,"Tobias Grosser" <tobias at grosser.es> wrote: > >> On 09/08/2013 08:03 PM, Star Tan wrote: >> Also, I wonder if your runs include the dependence analysis. If this is >> the case, the numbers are very good. Otherwise, 30% overhead seems still >> to be a little bit much. > I think

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 >

Now I've found another way to make it work. All I need is to pick up the names in the column (x.1.age...). > v<-pr(goprobit.p); v Maximum-Likelihood Estimates weighted = FALSE iterations = 5 logLik = -14160.75 finalHessian = TRUE Covariance matrix is Robust Number of parameters = 66 Sample size = 17922 est se t p g sig x.1.age 0.0341 0.0138 2.4766 0.0133 -3.8835e-04 ** x.1.sleep

Could not get "which" to work, but my grep worked. Thanks. > which(grep("very|somewhat",names(goprobit.p$est))) Error in which(grep("very|somewhat", names(goprobit.p$est))) : argument to 'which' is not logical > grep("very|somewhat",names(goprobit.p$est)) [1] 6 7 8 9 10 11 12 13 28 29 30 31 32 33 34 35 50 51 52 53 54 55 56 57 On 7/12/2024

I was testing Ilia's patches for ddx, and while they definitely helped for Xorg itself, qemu still eats a lot of CPU if launched like this qemu-system-x86_64 -cdrom ~/Downloads/ISO/slax-English-US-7.0.8-x86_64.iso -m 1G -display sdl,gl=on -enable-kvm and left for few hours. top - 07:38:01 up 18:05, 2 users, load average: 2,00, 1,89, 1,83 Tasks: 224 total, 3 running, 221 sleeping, 0

At 2013-09-09 13:07:07,"Tobias Grosser" <tobias at grosser.es> wrote: >On 09/09/2013 05:18 AM, Star Tan wrote: >> >> At 2013-09-09 05:52:35,"Tobias Grosser" <tobias at grosser.es> wrote: >> >>> On 09/08/2013 08:03 PM, Star Tan wrote: >>> Also, I wonder if your runs include the dependence analysis. If this is >>> the

Hi, The below very strange: # a) aov function av <- aov(Sepal.Length ~ Species, data=iris) # Error in parse(text = x) : # unexpected symbol in "Sepal(Sepal.Length+Species)Length" av <- aov(iris[, 1] ~ iris[, 5]) # summary(av) # Df Sum Sq Mean Sq F value Pr(>F) # iris[, 5] 2 63.2 31.6 119 <2e-16 *** # Residuals 147 39.0 0.3 # ---

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: >> >>

At 2013-09-09 05:52:35,"Tobias Grosser" <tobias at grosser.es> wrote: >On 09/08/2013 08:03 PM, Star Tan wrote: >> Hello all, >> >> >> I have done some basic experiments about Polly canonicalization passes and I found the SCEV canonicalization has significant impact on both compile-time and execution-time performance. > >Interesting. > >>

Greetings: I'm running R-1.9.1 on Fedora Core 2 Linux. I tested a proportional odds logistic regression with MASS's polr and Design's lrm. Parameter estimates between the 2 are consistent, but the standard errors are quite different, and the conclusions from the t and Wald tests are dramatically different. I cranked the "abstol" argument up quite a bit in the polr

Hello all, I have evaluated the compile-time and execution-time performance of Polly canonicalization passes. Details can be referred to http://188.40.87.11:8000/db_default/v4/nts/recent_activity. There are four runs: pollyBasic (run 45): clang -O3 -Xclang -load -Xclang LLVMPolly.so pollyNoGenSCEV (run 44): clang -O3 -Xclang -load -Xclang LLVMPolly.so -mllvm -polly -mllvm -polly-codegen-scev

Dear R users, I need to fit an ARMA model. As far as I've seen, EACF (extended ACF) is not available in R. 1. Let's say I fit a series of ARMA models in a loop. Given the code/output included below, how do I pull 'Model' and 'Fit' (AIC) from each summary() so that I can combine them into an array/data frame to be sorted by AIC? 2. Apart from EACF, are you aware perhaps

Dear list members, I fitted a glm model (See output below). My outcome is death, and weight (continuous), ClutchSize (3-level factor), EggVolume (continuous), Sex (obviously 2-level factor), and SiblingCompetence (2-level factor) are my covariates. I'd like to obtain the odds of death for a range of Weights, EggVolumes, and different combinations of ClutchSize. I've tried using the

The DDX eating CPU isn't intrinsically bad. Did you check where perf says the CPU time is going? Could be doing copies/etc. On Mon, Aug 17, 2020 at 12:52 AM Andrew Randrianasulu <randrianasulu at gmail.com> wrote: > > I was testing Ilia's patches for ddx, and while they definitely helped for Xorg itself, > qemu still eats a lot of CPU if launched like this > >

Behavioral science data is often collected from nested structures (students in schools, in districts, etc.). This can produce nonindependence among responses from individuals in the same groups. Consequently, researchers are advised to model the nested nature of the data to avoid biases in SE estimates. Failing to account for nonindependence can lead to SE estimates that are too large or too