Ecological researches mainly have complicated statistical design. Mixed models can test complicated designs with different crossed or nested factors. For instance lme4 and nlme could be used in R. However, still decision about statistical design is complicated for most researchers.
Mixed models are quit an advanced method for most researchers and recent publications still use simple statistical designs. For instance most publications in high ranked journals related to plant ecophysiology and seed researches still apply only factorial design for the statistical analysis and not mixed models including random and fixed factors.
Could you recommend some references or lectures introducing nested, crossed design with simple examples and simple codes in R?. I mean some simple explanation without details which start from simple design to complicated design like http://conjugateprior.org/2013/01/formulae-in-r-anova/
Graphical designs showing these crossed and nested would also be useful. This information would be very helpful for both teachers and researchers for application correct statistical analysis.
Dear Mehdi Abedi,
I strongly suggest you to look at this book
http://www.highstat.com/book2.htm
Mixed Effects Models and Extensions in Ecology with R (2009). Zuur, Ieno, Walker, Saveliev and Smith.
In this book, mixed models are introduced in R, easy to understand and with very useful examples!
Regards,
Ludovico
ِDear Ludovico,
Thanks for introducing Zuur et al (2009)!. I read this book and as you said this is great book without mathematical explanation. In this book you can find great explanation and examples for different linear, GLM,GAM and GLMM... they explained statistic concepts with fluent english and i also suggest his books for every r user and mixed models.
Zuur explain difficult concepts in simple flowcharts like page 273 for ziro, page 293 for count data324, 460. Also you can find nice flowchart in pages 21- 23 in analysing ecological data book (2007).
But I guess you didn't get my point! ;)
My question is about linking experimental design which well explained in classic book and application as part of mixed models than details of mixed models like link function, response distribution, stochastic/bayesian... in mixed models.
Defining random effects and also nested and crossed some times is confusing. In zuur book only chapter 5 explained nested and design and you can get usefull information by examples across book but not in one chapter. I think they dont explained with details because you can find this topic in classic experimental design books and classical ANOVAs. I dont expect explanation of designs but just several examples, graphical explanation and possible simple code for models.
My question is exactly this linkage between classical ANOVA, experimental designs and mixed models!.
It would be my pleasure to know this explanation somewhere in Zuur books or other references due to it's important role in building model.
Regards
Mehdi
Jaime Ashander introduced nice article which related to this question:
H Schielzeth, S Nakagawa (2013) Nested by design:model fitting and interpretation in a mixed model era Methods in Ecology and Evolution 4:14-24. http://dx.doi.org/10.1111/j.2041-210x.2012.00251.x
Hi!
You might also benefit from this article:
Piepho, H.P., Buchse, A. & Emrich, K. (2003) A Hitchhiker's guide to mixed models for randomized experiments. - J. Agronomy & Crop Science 189:310-322.
This article doesn't give R scripts, though. However, it gives some useful hints about building the models, how to define the correct structure for the blocking effects. Naturally, one needs to know the model structure before one can code it in R.
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