It's not a stand-alone resource, but Schielzeth and Nakagawa (2013) walks
through nested vs crossed. The paper has some nice graphics illustrating
the designs, and tables breaking down what is estimated in each, but no
example R code.
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
- Jaime
On Fri, Nov 14, 2014 at 3:00 AM, <r-sig-mixed-models-request at r-project.org
Date: Fri, 14 Nov 2014 13:51:45 +0330
From: Mehdi Abedi <abedimail at gmail.com>
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Crossed and nested factors in experimental
designs: Are there any flowcharts for decision making?
Message-ID:
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CADGhaghBEVKNYyRrhgStopbFTJxx4zNV+fG417jmGrAk-jwSBA at mail.gmail.com>
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Dear all,
I am following your nice discussion in this group. Considering your high
level discussion in this group i was not sure to ask basic problems in the
mailing list!, therefore, i asked this question in researchgate.
I think several researchers have this kind of question which your guidance
can also help them using mixed models in their researches. I can imagine
that teaching advanced statistic with simple language is not easy. However
in addition to advanced statistic books and codes, introducing
experimental
design and their link to mixed models using simple codes would be valuable
as well.
It would be my pleasure to have your suggestion here or in this link:
Warm regards
Mehdi:
https://www.researchgate.net/post/With_regards_to_crossed_and_nested_factors_in_experimental_designs_Are_there_any_flowcharts_for_decision_making
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/
<
https://www.researchgate.net/go.Deref.html?url=http%3A%2F%2Fconjugateprior.org%2F2013%2F01%2Fformulae-in-r-anova%2F
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.
--
*Mehdi Abedi Department of Range Management*
*Faculty of Natural Resources & Marine Sciences *
*Tarbiat Modares University (TMU) *
*46417-76489, Noor*
*Mazandaran, IRAN *
*mehdi.abedi at modares.ac.ir <Mehdi.abedi at modares.ac.ir>*
*Homepage
<http://www.modares.ac.ir/en/Schools/nat/Academic_Staff/~mehdi.abedi>*
*Tel: +98-122-6253101 *
*Fax: +98-122-6253499*
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