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Hello, I am a new user or R, and am so far very impressed with its capabilities. However, I have no programming experience, and am having some issues in trying to tell the software what I want done. There are basically two issues which I am currently grappling with. The first, I have a data matrix, with two factors and dozens of response variables. I am interested on conducting ANOVAs on

Dear R help list, I have done a lot of searching but have not been able to find an answer to my problem. I apologize in advance if this has been asked before. I am applying a mixed model to my data using lmer. I will use sample data to illustrate my question: >library(lme4) >library(arm) >data("HR", package = "SASmixed") > str(HR) 'data.frame': 120 obs.

Hello R users, I have a substantial question about statistics, not about R itself, but I would love to have an answer from an R user, in form of an example in R syntax. I have spent whole Sunday searching in Google and browsing the books. I've been really close to the answer but there are at least three standard errors you can talk about in the linear regression and I'm really confused.

Comparing the Polyhedron 2005 benchmark results for gfortran from llvm-gcc-4.2 of April 7th, 2010 and September 18th, 2010 (from the rc2 2.8 release branch), we seem to be regressing in performance for this release.... ================================================================================ Date & Time : 7 Apr 2010 22:24:16 Test Name : llvm_gfortran_lin_p4 Compile Command :

(1) So finally, thank to your help I have this: summary(lm(x ~ 0+I(t^2))) And then I get this result: ================================================= Call: lm(formula = x ~ 0 + I(t^2)) Residuals: Min 1Q Median 3Q Max -3.332e-02 -9.362e-03 1.169e-05 1.411e-02 3.459e-02 Coefficients: Estimate Std. Error t value Pr(>|t|) I(t^2) 0.0393821

I tried multiple imputation with aregImpute() and fit.mult.impute() in Hmisc 3.8-3 (June 2010) and R-2.12.1. The warning message below suggests that summary(f) of fit.mult.impute() would only use the last imputed data set. Thus, the whole imputation process is ignored. "Not using a Design fitting function; summary(fit) will use standard errors, t, P from last imputation only. Use

System: R 2.3.1 on Windows XP machine. I am building a logistic regression model for a sample of 100 cases in dataframe "d", in which there are 3 binary covariates: x1, x2 and x3. ---------------- > summary(d) y x1 x2 x3 0:54 0:50 0:64 0:78 1:46 1:50 1:36 1:22 > fit <- glm(y ~ x1 + x2 + x3, data=d, family=binomial(link=logit)) >

On Thu, 30 Jun 2005, Tanu Sharma wrote: > I am compiling SPEC 2000 benchmarks with llvm .Got stuck with > calculating "execution time" of all the .bc and native files. > > The log for nightly test itself gives execution times but I am passing > the bytecode files to my pass which gives another bytecode file.I have > to calculate execution time of such bytecode and

Hello , I am compiling SPEC 2000 benchmarks with llvm .Got stuck with calculating "execution time" of all the .bc and native files. The log for nightly test itself gives execution times but I am passing the bytecode files to my pass which gives another bytecode file.I have to calculate execution time of such bytecode and native files as well.If i simply do this: time lli

Hello All, Thanks for the reply.I can generate the reports by compiling Spec through llvm, but that couldn't resolve my problem. I m trying to determine execution time for the bytecode and native files , which are obtained as a result of running my pass over the original bytecode .I am running these experiments on spec benchmark. In SPEC we have command line tools such as runspec where

Hello Guys, I'm using the package RankProduct to determine the differential expression of genes in 2 classes. The importance of the thing is to know: - How can I determine which class is the class of up-regulated genes and which to the down regulated gene - How can i interpret the results : $pfp, $RPs, $RPrank, Orirank,

Dear all, Even after a couple of hours looking at old messages I still haven't found a solution for my problem. I'm trying to fit an additive linear regression model with 2 effects, both fixed, to some dataset. The function contrasts(effectA) <- contr.sum can gaurantee that the coefficients per parameter sum to one, and the function dummy.coef provices the estimates of all

I get a list object from an iterative function. I'm trying to figure out the most efficient way to calculate the mean of one element, across all components of the overall list. I've tried output <- mean (listobject[[1:5]]$element) to get the mean of "element" in the first five components? It doesn't like the $. I'm suspect one of the "apply" functions

On Feb 19, 2011, at 11:25 AM, Jack Howarth wrote: > Is anyone able to bootstrap llvm-gcc42 svn on x86_64-apple-darwin10? Currently it is > failing here with... It was broken. I think I've fixed it in reverting 125960. -eric

A big compile time regression. Any ideas? Ciao, Duncan. On 22/07/11 19:13, llvm-testresults at cs.uiuc.edu wrote: > > bwilson__llvm-gcc_PROD__i386 nightly tester results > > URL http://llvm.org/perf/db_default/simple/nts/253/ > Nickname bwilson__llvm-gcc_PROD__i386:4 > Name curlew.apple.com > > Run ID Order Start Time End Time > Current 253 0 2011-07-22 16:22:04

On Mon, Feb 21, 2011 at 03:58:19PM -0800, Eric Christopher wrote: > > On Feb 19, 2011, at 11:25 AM, Jack Howarth wrote: > > > Is anyone able to bootstrap llvm-gcc42 svn on x86_64-apple-darwin10? Currently it is > > failing here with... > > It was broken. I think I've fixed it in reverting 125960. > > -eric Eric, The llvm-gcc42 bootstrap is fixed in

Is anyone able to bootstrap llvm-gcc42 svn on x86_64-apple-darwin10? Currently it is failing here with... /sw/src/fink.build/llvm-gcc42-2.9-0/llvm_gcc42_objdir/./prev-gcc/xgcc -B/sw/src/fink.build/llvm-gcc42-2.9-0/llvm_gcc42_objdir/./prev-gcc/ -B/sw/lib/llvm-gcc-4.2/x86_64-apple-darwin10/bin/ -c -g -O2 -mdynamic-no-pic -DIN_GCC -W -Wall -Wwrite-strings -Wstrict-prototypes -Wmissing-prototypes

On Jul 24, 2011, at 3:02 AM, Duncan Sands wrote: > A big compile time regression. Any ideas? > > Ciao, Duncan. False alarm. For some reason that I have not yet been able to figure out, these tests run significantly more slowly when I run them during the daytime, which I did for that run. I checked a few of the worst regressions reported here and they all recovered in subsequent

Some wheird problem, Target/X86 builds okay now. But there seems to be another problem with the Cygwin build :- make[4]: Entering directory `/usr/build/llvm/lib/Target/SparcV9/ModuloScheduling' llvm[4]: Compiling ModuloSchedulingSuperBlock.cpp for Debug build /usr/src/llvm/lib/Target/SparcV9/ModuloScheduling/ModuloSchedulingSuperBlock.cpp : In member function `virtual bool

Hi all, I've started looking at the GlobalMerge pass, enabled by default on ARM and AArch64. I think we should reconsider that, at least for AArch64. As is, the pass just merges all globals together, in groups of 4KB (AArch64, 128B on ARM). At the time it was enabled, the general thinking was "it's almost free, it doesn't affect performance much, we might as well use it".