similar to: Bugs compiling R-1.7.1 with Intel compilers icc and ifc (PR#4295)

>>>>> CanisMaior writes: > Bugs compiling R-1.7.1 with Intel compilers icc and ifc, > on x86-computer (Pentium IV) and linux operating system > Hello, > as there aren't many reports about that issue, I'll give a little > report here. (Hope I don't bother anyone) > The best thing about using icc and ifc are the warnings, because > it is said that

>>>>> Saikat DebRoy writes: > On Thursday, Sep 25, 2003, at 05:26 US/Eastern, CanisMaior@web.de wrote: >> Bugs compiling R-1.7.1 with Intel compilers icc and ifc, >> on x86-computer (Pentium IV) and linux operating system >> > Many of those bugs can be fixed by using appropriate configure options. > Some of the warnings are serious, but they quite a

Full_Name: Danny van Dyk Version: R-2.2.0 OS: Gentoo/AMD64 Submission from: (NULL) (83.129.50.65) I have successfully compiled R-2.2.0 using ifc-9.0.026 on my x86_64 box. The only (minor) change that hade to be made was adding fpicflags="-fPIC" to configure.ac in case F77 matches either "ifc" (old name) or "ifort" (new name). Please apply following patch: dvandyk

Dear R-devel, I'm trying to compile R-1.9.0beta (2004-03-31) with the PGI compilers (15-day free evaluation: after the 15 days the binaries will stop working). I'm running into some problems, that I hope some can offer pointers. 1. After configure, the FLIBS include /usr/local/lib' --- note the extra '. I had to manually delete it in a couple of places. In a previous thread on

On Thu, Mar 26, 2020 at 3:08 PM Jason Wang <jasowang at redhat.com> wrote: > > From: Zhu Lingshan <lingshan.zhu at intel.com> > > This commit introduced two layers to drive IFC VF: > > (1) ifcvf_base layer, which handles IFC VF NIC hardware operations and > configurations. > > (2) ifcvf_main layer, which complies to VDPA bus framework, >

Hi, all There are 62 samples in my data and I tested 3 times for each one, then I want to use ICC(intraclass correlation) from irr package to test the consistency among the tests. *combatexpdata_p[1:62] is the first text results and combatexpdata_p[63:124] * is the second one and *combatexpdata_p[125:186]* is the third. Here is the result:

Hello, R-folks: Here is a statement I use to make a data frame: iccdata <- data.frame(i=rep(1:10,rep(2,10)),j=rep(1:2,10), x=c(0.35011,0.11989,0.13081,0.09919,0.16000,0.12000,0.00000,0.00000, 0.44023,0.32977,2.67081,2.63919,0.09050,0.03950,0.44019,0.30981,0.59000, 0.57000,4.03000,3.77000)) Then here are the data: > iccdata i j x 1 1 1 0.35011 2 1 2 0.11989 3 2 1 0.13081 4

Hello everyone. As some of you know, I was able to compile wine with Intel C++ Compiler (ICC), compilation log is located here: http://wine.x.pl/wine-1.1.38-ICC-compilation.log.tar.gz Some tests failed, and dxgi failed to compile (this is only related to DX10 I think), anyway all apps I tested worked fine (Red Faction 1 & 2, 3DMark 2000,2001SE,2003,Foobar2000,Operation Flashpoint), so I did

Dear R users I would like to ask a question regarding to icc (intraclass correlation) or many biologists refer it to as repeatability. It is very useful to get icc for many reasons and it is easy to do so from linear mixed-effects models and many packages like psy, psychometric, aod and irr have functions to calculate icc. icc = between-group variance/(between-group variance + residual

Dear developeRs, As of icc 10, the -c99 option is deprecated, and generates a lot of warnings when compiling R or R packages. If you use CC="icc -std=c99" instead of just CC="icc", R's configure will not add the -c99 option, and the code seems to compile and run just fine. (Please don't hesitate to let me know if this is a bad idea. :-) -- Regards, Bj?rn-Helge

I would appreciate some thoughts on using the bootstrap functions in the library "bootstrap" to estimate confidence intervals of ICC values calculated in lme. In lme, the ICC is calculated as tau/(tau+sigma-squared). So, for instance the ICC in the following example is 0.116: > tmod<-lme(CINISMO~1,random=~1|IDGRUP,data=TDAT) > VarCorr(tmod) IDGRUP = pdLogChol(1)

I am having trouble compiling R-2.6.2 on suse linux x86_64 using the intel compiler. I read section C.2.1 Intel compilers in the R Installation and Administration manual, and put CC=icc CFLAGS="-g -O3 -wd188 -ip" F77=ifort FFLAGS="-g -O3" ICC_LIBS=/opt/intel/cce/10.1.012/lib IFC_LIBS=/opt/intel/fce/10.1.012/lib/ LDFLAGS="-L$ICC_LIBS -L$IFC_LIBS -L/usr/lib64"

Win7, R2.11.0 I am working on a report together with several co-authors. The data concern several performance measures on a set of groups. These measures are scored by external judges. We report findings on several datasets, including several of the 6 ICC's discussed by Shrout & Fleiss (1979). We determine these using the icc function from the irr package. We have also used the ICC

Hi all, I have some C++ code that I call from my package. In my main C++ loop, I check for user interrupts and return to the R shell after ensuring I've deallocated memory appropriately. This works fine when the code is compiled with gcc+openmp and with icc without openmp, but when I compile with icc and use openmp, the entire R session is immediately terminated when I hit ctrl-C. This

Hello R users: I am fairly new to R and am trying to figure out how to compute an intraclass correlation (ICC) and/or design effect for binary data? More specifically, I am trying to determine the amount of clustering in a data set - that is, whether certain treatment programs tend to work with more or less severe clients. The outcome variable is dichotomous (low severity / high severity)

On Sat, 2015-09-05 at 11:53 +0200, arnaud gaboury wrote: > On Fri, Sep 4, 2015 at 5:58 PM, Martyn Plummer <plummerm at iarc.fr> wrote: > > On Wed, 2015-09-02 at 20:49 +0200, arnaud gaboury wrote: > >> On Wed, Sep 2, 2015 at 7:35 PM, arnaud gaboury <arnaud.gaboury at gmail.com> wrote: > >> > After a few days of reading and headache, I finally gave a try at

On Wed, Sep 9, 2015 at 11:26 PM, Nathan Kurz <nate at verse.com> wrote: > > As a short and simple approach, I just compiled the current R release > on Ubuntu with ICC and MKL using just this: > > $ tar -xzf R-3.2.2.tar.gz > $ cd R-3.2.2 > $ CC=icc CXX=icpc AR=xiar LD=xild CFLAGS="-g -O3 -xHost" CXXFLAGS="-g > -O3 -xHost" ./configure

I'm using the lme function in nmle to estimate the variance components of a fully nested two-level model: Y_ijk = mu + a_i + b_j(i) + e_k(j(i)) lme computes estimates of the variances for a, b, and e, call them v_a, v_b, and v_e, and I can use the intervals function to get confidence intervals. My understanding is that these intervals are probably not that robust plus I need intervals on the

Hi, I'm trying to run a ICC calculation on a data frame. I get the following error message: Error in data.frame(x.s, subs = rep(paste("S", 1:n.obs, sep = ""), nj)) : arguments imply differing number of rows: 1700, 1750 I've looked at the data in the file and it seems to be okay. Any thoughts would be much appreciated. I'm getting used to R so if possible, I

After a few days of reading and headache, I finally gave a try at building R from source with Intel MKL and ICC. Documentation and posts on this topic are rather incomplete, sometime fantasist et do not give much explanations about configure options. As I am not sure if mine is correct, I would appreciate some advices and hints. OS: Fedora 22 parallel_studio_xe_2016 Hardware : 8 Thread(s) per