search for: 0.0068

Howdy! I'm trying to print a correlation matrix where every r is rounded to two digits after the colon. How can I get that? I tried options(digits=2) but that rather yields two digits minimum precision so that I get 0.0068 instead of 0.01. Ragnar -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

i want to ask 2 questions. 1) can R do Random-effects GLS regression which i can get from Stata? the following result is frome Stata.can I get the alike result from R? xtreg lwage educ black hisp exper expersq married union, re Random-effects GLS regression Number of obs = 4360 Group variable (i) : nr Number of groups = 545 R-sq:

Using the small reproducible example below, I'd like to know if one can somehow use the matrix "sig" (defined below) to add a black outline (with lwd=2) to all pixels with a corresponding value of 1 in the matrix 'sig'? So for example, in the ggplot2 plot below, the pixel located at [1,3] would be outlined by a black square since the value at sig[1,3] == 1. This is my first

Target: FreeBSD 7.0-RC1 on amd64. autoconf says: configure:2122: checking build system type configure:2140: result: x86_64-unknown-freebsd7.0 [...] configure:2721: gcc -v >&5 Using built-in specs. Target: amd64-undermydesk-freebsd Configured with: FreeBSD/amd64 system compiler Thread model: posix gcc version 4.2.1 20070719 [FreeBSD] [...] objdir != srcdir, for both llvm and gcc. Release

Dear Users, I ran factor analysis using R and SAS. However, I had different outputs from R and SAS. Why they provide different outputs? Especially, the factor loadings are different. I did real dataset(n=264), however, I had an extremely different from R and SAS. Why this things happened? Which software is correct on? Thanks in advance, - TY #R code with example data # A little

Dear R-tisans, I am trying to calculate the 12th root of a transition (square) matrix, but can't seem to obtain an accurate result. I realize that this post is laced with intimations of quantitative finance, but the question is both R-related and broadly mathematical. That said, I'm happy to post this to R-SIG-Finance if I've erred in posting this to the general list. I've

LLVMers, The 2.2 prerelease is now available for testing: http://llvm.org/prereleases/2.2/ If anyone can help test this release, I ask that you do the following: 1) Build llvm and llvm-gcc (or use a binary). You may build release (default) or debug. You may pick llvm-gcc-4.0, llvm-gcc-4.2, or both. 2) Run 'make check'. 3) In llvm-test, run 'make TEST=nightly report'. 4) When

Dear Members, Greetings! I have come across a discrepancy shown by R and SAS results on same data for logistics regression.. When I processed the above csv file(1000.csv) for predicting the Action (i/c) by Age Group(1-7,Na) and Gender(M,F,Na) with GLM of R I get: R result Call: glm(formula = Action ~ Gender + AgeGroup, family = binomial, data = mydata1, na.action = na.pass) Deviance

Dear friends, I used R and SAS to analyze my data through generalized linear model, and there is some difference between them. Results from R: glm(formula = snail ~ grass + gheight + humidity + altitude + soiltemr + airtemr, family = Gamma) Deviance Residuals: Min 1Q Median 3Q Max -1.23873 -0.41123 -0.08703 0.24339 1.21435 Coefficients:

Target: FreeBSD 6.2-STABLE on i386 autoconf says: configure:2122: checking build system type configure:2140: result: i386-unknown-freebsd6.2 [...] configure:2721: gcc -v >&5 Using built-in specs. Configured with: FreeBSD/i386 system compiler Thread model: posix gcc version 3.4.6 [FreeBSD] 20060305 [...] objdir != srcdir, for both llvm and gcc. Release build. llvm-gcc 4.2 from source.

Hi Eric, you can use an annotate-layer, eg ind<-which(sig>0,arr.ind = T) ggplot(m1.melted, aes(x = Month, y = Site, fill = Concentration), autoscale = FALSE, zmin = -1 * zmax1, zmax = zmax1) + geom_tile() + coord_equal() + scale_fill_gradient2(low = "darkred", mid = "white", high = "darkblue",

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

Hi R community! I've fitted three mixed-effects regression models to a thousand bootstrap samples (case-resampling regression) using the lme4 package in a custom-built for-loop. The only output I saved were the inferential statistics for my fixed and random effects. I did not save any output related to the performance to the machine learning algorithm used to fit the models (REML=FALSE).

Windows 2000 . 5.00.2195 with Service Pack 1. R 1.5.1 Output from my split-split plot aov "alerted" me that I have done something wrong. I designed an experiment with all combinations of all levels of each treatment, but lost a little data (3 out of 192 plots). With the following data, I run the following model: > collim[c(1:6,187:192),c(1,3:6,9)] plot Litter Fert

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

Dear Spencer and Prof. Fox, Thank you for your replies. I'll very appreciate, if you have any ideas concerning the problem described below. First, I'd like to describe the model in brief. In general I consider a model with three equations. First one is for annual GRP growth - in general it looks like: 1) GRP growth per capita = G(investment, migration, initial GRP per

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".

On 23.11.2012, at 15:12, john skaller <skaller at users.sourceforge.net> wrote: > > On 23/11/2012, at 5:46 PM, Sean Silva wrote: > >> Adding LLVMdev, since this is intimately related to the optimization passes. >> >>> I think this is roughly because some function level optimisations are >>> worse than O(N) in the number of instructions. >>

On Fri, Sep 14, 2007 at 11:42:18PM -0700, Tanya Lattner wrote: > The 2.1 pre-release (version 1) is available for testing: > http://llvm.org/prereleases/2.1/version1/ > > [...] > > 2) Download llvm-2.1, llvm-test-2.1, and the llvm-gcc4.0 source. > Compile everything. Run "make check" and the full llvm-test suite > (make TEST=nightly report). > > Send

LLVMers, The 2.1 pre-release (version 1) is available for testing: http://llvm.org/prereleases/2.1/version1/ I'm looking for members of the LLVM community to test the 2.1 release. There are 2 ways you can help: 1) Download llvm-2.1, llvm-test-2.1, and the appropriate llvm-gcc4.0 binary. Run "make check" and the full llvm-test suite (make TEST=nightly report). 2) Download