similar to: Converting text to numbers

Let's say I have a within-subject experiment with 2 observables, obs1 and ob2 and 2 independent factors, fac1 and fac2. I can do summary( aov( obs1~fac1*fac2 + Error(Subject/(fac1*fac2)) ) ) summary( aov( obs2~fac1*fac2 + Error(Subject/(fac1*fac2)) ) ) to test the 2 observables separately. > summary( fit<-manova( cbind(obs1,obs2)~fac1*fac2 + Error(Subject/(fac1*fac2)) ) ) gives

Hi Philip, Could you define what is an accurate stack trace for your project? In other words, what do you mean by full and accurate stack frame? Without this definition, this is difficult to give you any feedback. In particular, I do not see what it means when we use inlining. E.g., what do you expect from code like this: static void fct1(…) { ... } static void fct2(…) { … fct1(…) ... }

On 10/30/13 9:56 AM, Quentin Colombet wrote: > Hi Philip, > > Could you define what is an accurate stack trace for your project? > In other words, what do you mean by full and accurate stack frame? > > Without this definition, this is difficult to give you any feedback. > In particular, I do not see what it means when we use inlining. Sure. Just to note, I *think* your

Philip, Thanks for the clarification. As far as I can tell, there is currently no way to preserve a full and accurate stack trace while utilizing most of LLVM’s optimization abilities. The work on debug information may help you get the information you need, but I do not think we will provide information on stack frames that have been removed via inlining or tail call. Moreover, if at some point

Actually CCing Eric. On Wed, Oct 30, 2013 at 11:00 AM, Quentin Colombet <qcolombet at apple.com>wrote: > Philip, > > Thanks for the clarification. > > As far as I can tell, there is currently no way to preserve a full and > accurate stack trace while utilizing most of LLVM’s optimization abilities. > > The work on debug information may help you get the information

Hello, See my problem below. a<-data.frame(c("s","c","c","n","n","n"),c(rep(1,3),rep(2,3)),c(rep(2,3),rep(1,3)),c("01/01/1999","10/02/2000","13/02/2000","11/02/2000","15/02/2000","23/02/2000"))

Hi, newbie question. I have a data-frame with 3 named columns: Name, Obs1, Obs2. The Name column members are made of alphanumeric characters: T1, T2, T3 etc. I would like to acess only that subset of the data-frame with Name == T44. X <- dataframe[dataframe$Name=='T44'] does not work. Any ideas on how to do this? I'm sure I'm missing a simple concept here. Thanks, Anjan --

Hello, I want to do fisher test for the rows in data file which has value less than 5 otherwise chi square test .The p values from both test should be stored in one resulted file. but there is some problem with bold if statement. I don't know how implement this line properly. x = cbind(obs1,obs2,exp1,exp2) a = matrix(c(0,0,0,0), ncol=2, byrow =TRUE) #matrix with initialized values

Hi, Here is a small C++ program: vec.cc: #include <cmath> using v4f32 = float __attribute__((__vector_size__(16))); v4f32 fct1(v4f32 x) { return 1.0 / x; } v4f32 fct2(v4f32 x) { return __builtin_ia32_rcpps(x); } Which is compiled to: vec.o: file format elf64-x86-64 Disassembly of section .text: 0000000000000000 <_Z4fct1Dv4_f>: 0: c4 e2 79 18 0d 00 00 vbroadcastss

Is there a known way to preserve a full and accurate stack trace while utilizing most of LLVM's optimization abilities? We are investigating using LLVM as a JIT for a language which requires the ability to generate an accurate stack trace from any arbitrary point(1) during the execution. I know that we can make this work by doing inlining externally, manually recording virtual frames,

Hi Quentin, You are correct, I could manage to get clang to use vrcpps, but not in a satisfying way: clang++ -O3 -march=native -mtune=native \ -Rpass=loop-vectorize -Rpass-missed=loop-vectorize -Rpass-analysis=loop-vectorize \ -ffast-math -ffp-model=fast -ffp-exception-behavior=ignore -ffp-contract=fast \ -c -o vec.o vec.cc 0000000000000140 <_Z4fct4Dv4_f>: 140: c5 f8 53 c8

Dear experts, Please forgive the puzzled title and the length of this message - I thought it would be best to be as complete as possible and to show the avenues I have explored. I'm trying to fit a linear model to data with a binary dependent variable (i.e. Target.ACC: accuracy of response) using lrm, and thought I would start from the most complex model (of which "sample1.lrm1" is

Hello all, I have what feels like a simple problem, but I can't find an simple answer. Consider this data frame: > x <- data.frame(sample1=c(35,176,182,193,124), sample2=c(198,176,190,23,15), sample3=c(12,154,21,191,156), class=c('a','a','c','b','c')) > x sample1 sample2 sample3 class 1 35 198 12 a 2 176 176

Dear R Users, I have some data that is very similar in form to a 3D image - ie univariate data on a regular 3D grid. I keep this as a 3D numeric array in R with attributes describing the sampling points along the 3 dimensions. I would like to interpolate this onto a new regular 3D grid that I specify (eg by supplying 3 vectors corresponding to the new grid locations on each of the 3

hi, all I inherited a data set with format like the following: para.A para.B para.C observation.1 observation.2 observation.3 .... Essentially, the same experiment (with same parameters) are conducted 3 times, with corresponding observations. What are the efficient ways of re-arranging this data set in a one row per observation such that: para.A para.B para.C observation.1 para.A

David, Quentin - Thanks for the feedback. Responses inline. On 10/30/13 11:21 AM, David Blaikie wrote: > Actually CCing Eric. > > > On Wed, Oct 30, 2013 at 11:00 AM, Quentin Colombet > <qcolombet at apple.com <mailto:qcolombet at apple.com>> wrote: > > Philip, > > Thanks for the clarification. > > As far as I can tell, there is currently

Hi group, This is how my test file looks like: Chr start end sample1 sample2 chr2 9896633 9896683 0 0 chr2 9896639 9896690 0 0 chr2 14314039 14314098 0 -0.35 chr2 14404467 14404502 0 -0.35 chr2 14421718 14421777 -0.43 -0.35 chr2 16031710 16031769 -0.43 -0.35 chr2 16036178 16036237 -0.43 -0.35 chr2 16048665 16048724 -0.43 -0.35 chr2 37491676 37491735 0 0 chr2 37702947 37703009 0 0

Hi Group, I have a data frame below. Within this data frame there are samples (columns) that are measured more than once. Samples are indicated by "idx". So "id1" is present in columns 1, 3, and 5. Not every id is repeated. I would like to create a new data frame so that the repeated ids are averaged. For example, in the new data frame, columns 1, 3, and 5 of the original

Hi! I need to perform this simple sampling function several hundred times: x1=as.character(rnorm(1000, 100, 15)) x2=as.character(rnorm(1000, 150, 10)) y1=as.data.frame(x1,x2) sample1=as.data.frame(sample(y1$x1, 12, replace = FALSE, prob = NULL)) sample1 write.table(sample1, "sample1.txt", sep=" ",row.names=F,quote=F) My knowledge of loops is quite low. How can I produce 100

Dear Researchers, I have several files as this example: Myfile_MyArea1_sample1.txt i wish to split in "Myfile", "MyArea1", "sample1", and "txt", becasue i need to use "sample1" label. I try to use "strsplit" but I am able just to split as "Myfile_MyArea1_sample1" and "txt" OR "Myfile", "MyArea1",