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Hi, I have been trying unsuccessfully to plot data using different colors based on a variable within a subset of an imported file. The file I am reading is about 20000 lines long and has a column (in the example called FILE) that contains approximately 100 unique entries. I would like to plot a subset of the data from the file and key the color from the FILE column, This is what my file looks like

Dear R-helpers, To get curves for a pseudo cohort other than the one centered at the mean of the covariates, I have been trying to use the newdata argument to survfit with no success. Here is my model statement, the newdata and the ensuing error. What am I doing wrong? > summary(fit) Call: coxph(formula = Surv(Start, Stop, Event, type = "counting") ~ Week + LagAOO + Prior.f +

Hello, I have some conflicting output from an aov summary and tukey contrasts with a mixed effects model I was hoping someone could clarify. I am comparing the abundance of a species across three willow stand types. Since I have 2 or 3 sites within a habitat I have included site as a random effect in the lme model. My confusion is that the F test given by aov(model) indicates there is no

I'm puzzled by the behaviour of factors in rma models, see example and comments below. I'm sure there's a simple explanation but can't see it... Thanks for any input John Hodgson ------------------------------------- code/selected output ----------------- library(metafor) ## Set up data (from Lenters et al A Meta-analysis of Asbestos and Lung Cancer... ##

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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

> 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 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 -

Hi, I am trying to make a multi-class classification tree by using rpart. I used MASS package'd data: fgl to test and it works well. However, when I used my small-sampled data as below, the program seems to take forever. I am not sure if it is due to slowness or there is something wrong with my codes or data manipulation. Please be advised ! The data is described as the output from str()