search for: 0.2625

Dear R help, I have some readings in three dimensions (x, y, z) and an amplitude for each. I'd like to visualize the data using ggplot, using tile plots, as I have some additional point data I would like to eventually overlay on the tile plots. I would like to subset the data by sections, slices if you will, in the z dimension, and plot the data for that slice. I can do all of this, but am

Hi I'm trying to extract complete rows from a dataframe by group based on the maximum in a column within that group. Thus I have a dataframe: cvd_basestudy ... es_time ... _____________ study1 ... 0.3091667 study2 ... 0.3091667 study2 ... 0.2625000 study3 ... 0.3033333 study3 ... 0.2625000 __________ etc I can extract the basestudy and the max(es_time)

Why are my coefficients getting appended with a 1? It borks a match I do later against the original list that doesn't have the random 1 added to the end. > linearModel[[1]] Call: lm(formula = modelSource ~ +UNITBUILD + UNITDB + ITBUILD + ITDB + UATBUILD + UATDB + HOGANCODE + RCF + ReleaseST1 + ReleaseST2 + ReleaseBLA + Small.Bank.Acquisitions + HLY.NewYear + HLY.MLK + HLY.PRES +

Dear List-Members, I have a problem with the function lqs() from package MASS. In some cases it produces different results for the same settings and needs a random seed to be set, in other cases not. I really cannot understand, why this happens. As well I do not understand what exactly you need the random seed for. Is it a starting point for iterations? Or do different results occur because of

First of all, sorry for the long mail. Inspired by the excellent analysis Rui did for lld, I decided to do the same for llvm. I'm personally very interested in build-time for LTO configuration, with particular attention to the time spent in the optimizer. Rafael did something similar back in March, so this can be considered as an update. This tries to include a more accurate high-level

On Sat, Dec 17, 2016 at 6:39 PM, Mehdi Amini <mehdi.amini at apple.com> wrote: > >> On Dec 17, 2016, at 1:35 PM, Davide Italiano via llvm-dev <llvm-dev at lists.llvm.org> wrote: >> >> First of all, sorry for the long mail. >> Inspired by the excellent analysis Rui did for lld, I decided to do >> the same for llvm. >> I'm personally very

I've been comparing variables among objects (taxa) related by known trees, using phylogentically independent contrasts in the ape package, and want to move on to more complex models e.g. by using gls with appropriate correlation terms. My trees contain lots of (hard) polytomies and information about ancestors, which I've been including- creating fully dichotomous trees by using zero branch

> On Dec 17, 2016, at 1:35 PM, Davide Italiano via llvm-dev <llvm-dev at lists.llvm.org> wrote: > > First of all, sorry for the long mail. > Inspired by the excellent analysis Rui did for lld, I decided to do > the same for llvm. > I'm personally very interested in build-time for LTO configuration, > with particular attention to the time spent in the optimizer. >

Hello R community, I am computing weighted average statistic by using ddply function: My data set is: N1 T1 S1 I1 C1 FY-4 ROE11 EPS11 MKT11 N1 T1 S1 I1 C1 FY-3 ROE12 EPS12 MKT12 N1 T1 S1 I1 C1 FY-2 ROE13 EPS13 MKT13 N1 T1 S1 I1 C1 FY-1 ROE14 EPS14 MKT14 N1 T1 S1 I1 C1 FY0 ROE15 EPS15 MKT15 N1 T1 S1 I1 C1 FY1 ROE16 EPS16 MKT16 N1 T1 S1 I1 C1 FY2 ROE17

Dear all, I have a surprising problem with the representation of frequencies in a histogram. Consider, for example, the R code: b<-rnorm(2000,3.5,0.3) hist(b,freq=F) When I plotted the histogram, I expected that values in the y-axis (the probability) varied between 0 and 1. Instead, they varied within the range 0-1.3. Have you got any suggestion for obtaining a correct graph with

> On Dec 17, 2016, at 1:35 PM, Davide Italiano via llvm-dev <llvm-dev at lists.llvm.org> wrote: > > First of all, sorry for the long mail. > Inspired by the excellent analysis Rui did for lld, I decided to do > the same for llvm. > I'm personally very interested in build-time for LTO configuration, > with particular attention to the time spent in the optimizer. >

On 12/17/2016 01:35 PM, Davide Italiano via llvm-dev wrote: > First of all, sorry for the long mail. > Inspired by the excellent analysis Rui did for lld, I decided to do > the same for llvm. > I'm personally very interested in build-time for LTO configuration, > with particular attention to the time spent in the optimizer. > Rafael did something similar back in March, so

On Sat, Dec 17, 2016 at 1:35 PM, Davide Italiano via llvm-dev < llvm-dev at lists.llvm.org> wrote: > First of all, sorry for the long mail. > Inspired by the excellent analysis Rui did for lld, I decided to do > the same for llvm. > I'm personally very interested in build-time for LTO configuration, > with particular attention to the time spent in the optimizer. >

Hi Davide, Thanks for the analysis, it's really interesting! And I'm really glad that we now put more and more attention at the compile time! Just recently I've been looking into historical compile time data as well, and have had similar conclusions. The regressions you've found are probably caused by: 1) r289813 and r289855 - new matchers in InstCombine 2) r286814 and r288024 -