Hi Daniel, You might want to review further advances by Doug Bates with lme4 since the post you show in your email.
http://tolstoy.newcastle.edu.au/R/e2/help/06/10/3565.html In this thread Doug Bates discusses fitting using maximum likelihood for testing purposes. There is now an anova() method for lmer() and lmer2() fits performed using method="ML". You can compare different models and get p-values for p-value obsessed journals using this approach. I believe I read a thread discussing the use of maximum likelihood fitting for use in ANOVA tests, and then REML fits on final models for better parameter estimates - but I can't find that thread. Hopefully Doug Bates or anyone involved in that level of discussion can chime in. See also for example this wiki http://wiki.r-project.org/rwiki/doku.php?id=guides:lmer-tests and the new listserv at https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models Also check the latest documentation for lme4 and the lmer() and lmer2() functions at http://cran.r-project.org/ in the Packages ... lme4 pages. Hope this helps Steven McKinney Statistician Molecular Oncology and Breast Cancer Program British Columbia Cancer Research Centre email: smckinney +at+ bccrc +dot+ ca tel: 604-675-8000 x7561 BCCRC Molecular Oncology 675 West 10th Ave, Floor 4 Vancouver B.C. V5Z 1L3 Canada -----Original Message----- From: [EMAIL PROTECTED] on behalf of Daniel Lakeland Sent: Wed 8/15/2007 9:34 AM To: r-help@stat.math.ethz.ch Subject: [R] lmer coefficient distributions and p values I am helping my wife do some statistical analysis. She is a biologist, and she has performed some measurements on various genotypes of mice. My background is in applied mathematics and engineering, and I have a fairly good statistics background, but I am by no means a PhD level expert in statistical methods. We have used the lmer package to fit various models for the various experiments that she has done (random effects from multiple measurements for each animal or each trial, and fixed effects from developmental stage, and genotype etc). The results are fairly clear cut to me, and I suggested that she publish the results as coefficient estimates for the relevant contrasts, and their standard error estimates. However, she has read the statistical guidelines for the journal and they insist on p values. I personally think that p values, and sharp-null hypothesis tests are misguided and should be banned from publications, but it doesn't much matter what I think compared to what the editors want. Based on searching the archives, and finding this message: https://stat.ethz.ch/pipermail/r-help/2006-May/094765.html I am aware of the theoretical difficulties with p values from lmer results. I am also aware of the mcmcsamp function which performs some kind of bayesian sampling from the posterior distribution of the coefficients based on some kind of prior (I will need to do some more reading to more fully understand this). Is this the primary way in which we can estimate the distribution of the model coefficients and calculate a p value or a confidence interval? What can I do with the t statistic provided by lmer? If as the above message suggests, we are uncertain of the correct F and by extension t distributions to use, what help are the t statistics? I suppose I could test them against a very low degree of freedom t distribution (say 3) and publish those p values? Again, I'm content to ignore p values and stick to estimates, but the journal isn't. BTW: thanks to all on this list, I've benefitted greatly from R and from the archives of help topics. -- Daniel Lakeland [EMAIL PROTECTED] http://www.street-artists.org/~dlakelan ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
