similar to: Strange p-level for the fixed effect with lme function

Hello wizards, First of all, I'm a physicist and therefore I'm not much used to use statistics. So, please forgive me if this is a FAQ or stupid, but I failed to find the answer by myself. I have used "runs.test" (Package tseries) for computes the runs test for randomness , but I get this result: Runs test -1.8732 P-value = 0.0610 Alternative Hypothesis : Two sided How can

Hi All, I am trying to reproduce this using R instead. [image: Full-size image (38 K)] I tried using the following code *SChla <- read.csv("SM_Chla_data.csv")* *Atlantis <- SChla[16:66,]* *head(Atlantis)* * * Seamount Station Depth Pico Nano Micro Total_Ch dbar Latitude Longitud 16 Atlantis 1217 Surface 0.0639 0.1560 0.0398 0.2597 2.082 -32.71450 57.29733

Dear R developers and users: I have the following data, x is the response vaiable, nsample(individual) nested within trt, and subsample nested within nsample, I want to fit trt as fixed effect, and block, nsample(trt) as random effects using lme, is the following coding correct? dat$vgrp <- getGroups(dat, form = ~ 1|trt/nsample, level = 2) ge.lme1 <- lme(fixed=x~trt, data=dat,

Greetings: I'm running R-1.9.1 on Fedora Core 2 Linux. I tested a proportional odds logistic regression with MASS's polr and Design's lrm. Parameter estimates between the 2 are consistent, but the standard errors are quite different, and the conclusions from the t and Wald tests are dramatically different. I cranked the "abstol" argument up quite a bit in the polr

Dear all, I'm using R 2.8.1 under Windows Vista on a dual core 2,4 GhZ with 4 GB of RAM. I'm trying to reproduce a result out of "Analysis of Financial Time Series" by Ruey Tsay. In R I'm using the fGarch library. After fitting a ar(3)-garch(1,1)-model > model<-garchFit(~arma(3,0)+garch(1,1), analyse) I'm saving the results via > result<-model

Version of R: Windows Version 2.0.0 The experimental design contains two plant lines - a control (C) and a mutant (M) - grown out three separate times in plots A, B, C. The design is unbalanced: In plot A, 9 control plants were grown with 29 mutant plants. In plot B, 8 control plants were grown with 20 mutant plants. In plot C, 8 control plants were grown with 22 mutant plants. The

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

Hi, i've been trying to apply the lme apprach to growth curves of children, but lme keeps running for ever and ever as soon as I use a reasonable basis. First Example: Data are 39 boys from the Berkeley growth study, each one measured 31 times at the ages of 1.00 1.25 1.50 1.75 2.00 3.00 4.00 5.00 6.00 7.00 8.00 8.50 9.00 9.50 10.00 10.50 11.00 11.50 12.00 12.50 13.00 13.50

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