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Hi all,? The survdiff() from survival package has an argument "rho" that implements Fleming-Harrington weighted long rank test.? But according to several sources including "survminer" package (https://cran.r-project.org/web/packages/survminer/vignettes/Specifiying_weights_in_log-rank_comparisons.html), Fleming-Harrington weighted log-rank test should have 2 parameters

> -----Original Message----- > From: Hal Finkel [mailto:hfinkel at anl.gov] > Sent: den 13 juli 2017 16:01 > To: Björn Pettersson A <bjorn.a.pettersson at ericsson.com>; David Chisnall > <David.Chisnall at cl.cam.ac.uk>; Dylan McKay <me at dylanmckay.io> > Cc: llvm-dev at lists.llvm.org; Carl Peto <carl.peto at me.com> > Subject: Re: [llvm-dev] RFC:

> On Feb 13, 2018, at 4:02 PM, array chip via R-help <r-help at r-project.org> wrote: > > Hi all, > > The survdiff() from survival package has an argument "rho" that implements Fleming-Harrington weighted long rank test. > > But according to several sources including "survminer" package

My experience of having the address space for functions (or function pointers) in the DataLayout i that when the .ll file is parsed we need to parse the DataLayout before any function declarations. That is needed because we want to attribute the functions with correct address space (according to DataLayout) when inserting them in the symbol table. An alternative would be to update address space

> On Feb 14, 2018, at 5:26 PM, David Winsemius <dwinsemius at comcast.net> wrote: > >> >> On Feb 13, 2018, at 4:02 PM, array chip via R-help <r-help at r-project.org> wrote: >> >> Hi all, >> >> The survdiff() from survival package has an argument "rho" that implements Fleming-Harrington weighted long rank test. >>

Cool. That explains a lot! Sorry if this is a total n00b question, but… how does the datalayout string get overridden? in llvm/lib/Target/AVR/AVRTargetMachine.cpp I can see the code that determines the default datalayout for AVR… static const char *AVRDataLayout = "e-P1-p:16:8-i8:8-i16:8-i32:8-i64:8-f32:8-f64:8-n8-a:8”; However in the LLVM iR below, the target datalayout was present and

I’m not sure if it’s truly deterministic. It always gives the same results (so far) on my machine but I’m not sure that’s enough. My guess is it’s probably going to be deterministic on one machine but might well not be deterministic across environments. Like it might give varying results if cross compiled on different hosts, macOS vs intel Linux vs arm vs s390. (Obviously AVR is always a cross

A new package is now available via CRAN, called metafor. The metafor package consists of a collection of functions for conducting meta-analyses in R. Fixed- and random-effects models (with and without moderators) can be fitted via the general linear (mixed-effects) model. For 2x2 table data, the Mantel-Haenszel and Peto's method are also implemented. The package also provides various plot

That’s useful info, thanks. I think it will be useful for me to understand the connection, why this type of pointer is being emitted now. Do you have any suggestions where i can look to find the platform specific code that is making function pointers go into addrspace? Carl p.s. I am also working on passing the avr target flag to swift, but swift itself had (has?) limitations that make it

We have a test that looks like this… define void @array16_store() { ; CHECK-LABEL: array16_store: ; CHECK: ldi [[REG1:r[0-9]+]], 204 ; CHECK: ldi [[REG2:r[0-9]+]], 170 ; CHECK: sts int.array+3, [[REG2]] ; CHECK: sts int.array+2, [[REG1]] ; CHECK: ldi [[REG1:r[0-9]+]], 187 ; CHECK: ldi [[REG2:r[0-9]+]], 170 ; CHECK: sts int.array+1, [[REG2]] ; CHECK: sts int.array, [[REG1]] ; CHECK: ldi

Hello, I'm using dovecot as a mail relay so that I can back up my providers IMAP mail locally. I'm impressed how easy this was to set up, but I'm having a quirk that I would like help with, if possible. There are many weak links in my set-up chain, if you will, but I think I've narrowed the problem. I'll describe my set-up anyway. I'm running dovecot 1.1.4 on ubuntu

Hi, I am running a server that does regular builds using llc. To begin with I was doing a fork/exec to launch llc, passing the files to compile and other switches as command line, etc. then waiting for the subprocess to complete. This was fine but seemed an unnecessary overhead. After all llc is mostly a fairly thin front end over the llc libraries, which do the actual work right? So I took

Thanks, I tried calling ResetAllOptionOccurrences after the run like this… // Compile the module TimeCompilations times to give better compile time // metrics. for (unsigned I = TimeCompilations; I; --I) if (int RetVal = compileModule(argv, Context)) return RetVal; if (YamlFile) YamlFile->keep(); cl::ResetAllOptionOccurrences(); return 0; } Unfortunately

Hello Hal, > Add this information to DataLayout and to use that information in relevant places. This sounds like a much better/cleaner idea, thanks! On Wed, Jul 12, 2017 at 1:13 AM, Hal Finkel <hfinkel at anl.gov> wrote: > > On 07/11/2017 12:54 AM, Dylan McKay via llvm-dev wrote: > > Hello all, I’m looking into solving an AVR-specific issue and would love > to hear

Is it possible to calculate Kaplan-Meier for left-truncated, right-censored data using survival5? -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at

Dear all, We are trying to do a meta regression in R using the lme function. The reason for doing this with lme function is that we have covariates and studies within references. In S-Plus this is possible by using the following command: lme(outcome ~ covars, random = ~1 | reference/study, weights = varFixed(~var.outcome), data = mydata, control = lmeControl(sigma = 1)) This means that the

Hi, If I have: foo <- survfit(y ~ x) where y is a survival object and x is a n-level factor. The documentation says when I plot(foo), the confidence intervals will not be plotted (which I guess is understandable as otherwise the plot will get really messy). I tried to plot with confidence intervals by using: plot(foo, conf.int = TRUE) and indeed the resulting plot is messy. However

Hello everybody. First of all, I would like to present myself. I'm a french student in public health and I like statistics though I'm not that good in mathematics (but I try to catch up). I've discovered R recently while trying to find a statistical program in order to avoid rebooting my computer under windows when I need to do some statistical work. And here is my first question.

Hi, I’ve come back and updated my llvm toolset with modern code (my branch was about 1-2 years old) and now the llvm IR files produced by my front end no longer compile with llc. Here is a sample of llvm ir produced by my front end (it’s a standard version 3.1 build of swift from the swift.org open source website). ; ModuleID = 'main.ll' source_filename = "main.ll" target