similar to: [Bug 111178] New: low quality icc profile for monitor Benq ew2775zh

https://bugs.freedesktop.org/show_bug.cgi?id=75800 Priority: medium Bug ID: 75800 Assignee: nouveau at lists.freedesktop.org Summary: [NVE4] [GTX660Ti] Pink line on left side of monitor and slightly blurry areas on screen Severity: minor Classification: Unclassified OS: Linux (All) Reporter:

I guess the DVI adapter is passive. $ xrandr --props Screen 0: minimum 320 x 200, current 4480 x 1440, maximum 8192 x 8192 XWAYLAND1 connected 1920x1080+0+0 (normal left inverted right x axis y axis) 510mm x 290mm ??? non-desktop: 0 ??? ??? supported: 0, 1 ?? 1920x1080???? 59.96*+ XWAYLAND4 connected 2560x1440+1920+0 (normal left inverted right x axis y axis) 600mm x 340mm ??? non-desktop:

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

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

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

I am helping someone calculate ICC using R. I know R has several packages like irr, psy etc which provide options to calculate ICC (intraclass correlation coefficient). When getting ICC, we need to use the model: two-way mixed model with absolute agreement. I only found that in irr package, it provides the option of choosing one or two way and consistence or absolute agreement model. However,

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

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:

>>>>> 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

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 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)

So far, i couldn't find an answer on the net, maybe someone here can help me: Is it possible to install ICC-profiles under WINE? If yes, how can this be done? (I need support of ICC-profiles, because i run PhotoLine under WINE) Any help would be very much appreciated! Michael

Hi, I'm essentially wanting to calculate intra- and inter-observer variabilities for the first principal component of an optic disc shape measure of a sample of individuals, so from what I can work out I need to work out an intraclass correlation coefficient(s). For the intra-data, I have 2 measurements taken on each individual by the same observer. For the inter-data, 2 observers have taken

Seth, I encountered problems installing RSQLite, R-2.7.0, on RHEL4 using Intel 10.1 icc, My sysadmin helped me track down the problem and kindly forwarded me the fix, which corrected the problem. What follows is from the sysadmin. Mark I looked at the error, looks like there is a bug in the source code. I've attached a new tarball, hopefully fixed. I added #include <sys/types.h>

arnaud gaboury <arnaud.gaboury at gmail.com> writes: > 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

On Fri, Sep 4, 2015, 9:24 AM Bj?rn-Helge Mevik <b.h.mevik at usit.uio.no> wrote: arnaud gaboury <arnaud.gaboury at gmail.com> writes: > 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

Hi Arnaud -- I'm glad it's working for you. I'm not sure I understand your final answer. Are you saying that the version I posted worked for you as given, or that you had to remove some of the other options? Perhaps you could post the full final recipe in a way that others can copy and paste if they find this thread in the future? Also, did you determine if the the same approach

Hello dear R help group. I encountered this old thread (http://tinyurl.com/dklgsk) containing the a similar question to the one I have, but left without an answer. I am and hoping one of you might help. A simplified situation: I have a factorial design (with 2^3 experiment combinations), for 167 subjects, each one has answered the same question twice (out of a bunch of "types" of

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)

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