search for: 0.0444

Hi, I am trying to construct a logistic regression model from my data (104 patients and 25 events). I build a full model consisting of five predictors with the use of penalization by rms package (lrm, pentrace etc) because of events per variable issue. Then, I tried to approximate the full model by step-down technique predicting L from all of the componet variables using ordinary least squares

Tengo un modelo de regresión de Cox y quiero obtener el plot ajustado por una covariable (sexo) con la función ?ggadjustedcurves?, pero me da el siguiente error: > cox2 <- coxph(os ~ imc_25 + sexo.1, data = datos) > cox2 Call: coxph(formula = os ~ imc_25 + sexo.1, data = datos) coef exp(coef) se(coef) z p imc_25 -0.621 0.537 0.299 -2.08 0.038 sexo.1M 0.714

The following fit does not make sense to me, please, correct me if I have a logical error. > moddowsn Call: coxph(formula = Surv(start, stop, resp) ~ sn + matfac2, data = coxsn1, method = "efron") coef exp(coef) se(coef) z p sn2 0.0497 1.051 0.02030 2.450 1.4e-02 sn3 -0.0532 0.948 0.02038 -2.610 9.0e-03 sn4 -0.0410 0.960

Hi Rneters, I'm running a GLM model with a full factorial design in blocks and binomial error distribution. I would like to have the F values for this model but I got a message that "using F test with a binomial family is inappropriate in: anova.glm(model, test = "F")". Should I not report F statistics on this kind of analysis? I would appreciate any comment on this.

Hello, I am a relative novice at both using regression analysis and at using R in general (and at object oriented programing). A colleague convinced me that binary logistic regression is the most appropriate analysis for the data that I have though, so I've been trying to muddle through. I'm currently stumped on how to interpret/compute odds ratios for two and three way interactions

Greetings R-ers, A colleague and I have been exploring the behaviour of glmmPQL in R and S-PLUS 6 and we appear to get different results using the same code and the same data set, which worries us. I have checked the behaviour in R 1.7.1 (MacOS 9.2) and R. 1.9.0 (Windows 2000) and the results are the same, but differ from S-PLUS 6 with the latest Mass and nlme libraries (Windows XP). Here

Hi, I am trying to make a multi-class classification tree by using rpart. I used MASS package'd data: fgl to test and it works well. However, when I used my small-sampled data as below, the program seems to take forever. I am not sure if it is due to slowness or there is something wrong with my codes or data manipulation. Please be advised ! The data is described as the output from str()

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

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".

Are these 225 compile time regressions real? It sure looks bad! Ciao, Duncan. On 01/12/11 09:39, llvm-testresults at cs.uiuc.edu wrote: > > bwilson__llvm-gcc_PROD__i386 nightly tester results > > URL http://llvm.org/perf/db_default/simple/nts/380/ > Nickname bwilson__llvm-gcc_PROD__i386:4 > Name curlew.apple.com > > Run ID Order Start Time End Time > Current 380

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