lme4 versus nlme
lmer is really good for crossed random effects models. They are easy to fit and fast, as Harold said. However, lmer still (? Doug?) lacks the ability to incorporate complex covariance structures for the random effects and the residuals. I find I need that facility a lot, so I generally stick with the nlme package. You should only trust the F tests for the fixed effects for nested, balanced designs. lme is set up to make nested designs easy to code, and crossed random designs a lot more difficult to code. So including p-values for the fixed effects in lme seems reasonable. HTH, Simon.
On Thu, 2008-03-13 at 15:02 -0400, Doran, Harold wrote:
Kevin: lmer is a bit more general, and a heck of a lot faster. The nlme function is designed for cases where there is a strict nesting structure to the data. You can code for situations in which there are crossed random effects, but it is clunky and slooooooow. lmer, on the other hand, is specifically optimized for fully crossed or partially crossed data and is very fast. It also works for data that are strictly nested. There are some vignettes in the lme4 package that you can read to see examples of how to use the lmer function. There are also some nifty functions that go along with lmer that do not exists for nlme like mcmcsamp. Harold
-----Original Message----- From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Kevin E. Thorpe Sent: Thursday, March 13, 2008 1:54 PM To: r-sig-mixed-models at r-project.org Subject: [R-sig-ME] lme4 versus nlme I'm a bit confused. I have started reading Pinheiro and Bates, which is excellent. I am trying to rationalize when one would use lmer() and when one would use lme() (I know lmer() is not covered in the book). I have read a number of the treads about why p-values have been taken out of lmer() stuff. Naturally, they still exist in nlme. Presumably, I should not trust those p-values either. The problem still remains that I am encountered more and more situations where it seems I need a mixed model. The investigator wants to know if the groups differ. What is the recommended approach to answering those kinds of questions? Thank you, Kevin -- Kevin E. Thorpe Biostatistician/Trialist, Knowledge Translation Program Assistant Professor, Department of Public Health Sciences Faculty of Medicine, University of Toronto email: kevin.thorpe at utoronto.ca Tel: 416.864.5776 Fax: 416.864.6057
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Simon Blomberg, BSc (Hons), PhD, MAppStat. Lecturer and Consultant Statistician Faculty of Biological and Chemical Sciences The University of Queensland St. Lucia Queensland 4072 Australia Room 320 Goddard Building (8) T: +61 7 3365 2506 http://www.uq.edu.au/~uqsblomb email: S.Blomberg1_at_uq.edu.au Policies: 1. I will NOT analyse your data for you. 2. Your deadline is your problem. The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. - John Tukey.