2 factor anova

from Dallals page Little Handbook of Statistical Practice

dairy farmer wished to determine which type of feed will produce the greatest yield of milk. From the research literature she is able to determine the mean milk output for each of the breeds she owns for each type of feed she is considering. As a practical matter, she can use only one type of feed for her herd.
Since she can use only one type of feed, she wants the one that will produce the greatest yield from her herd. She wants the feed type that produces the greatest yield when averaged over all breeds, even if it means using a feed that is not optimal for a particular breed. (In fact, it is easy to construct examples where the feed-type that is best on average is not the best for any breed!) The dairy farmer is interested in what the main effects have to say even in the presence of the interaction. She wants to compare
where the means are obtained by averaging over breed.
For the sake of rigor, it is worth remarking that this assumes the herd is composed of equal numbers of each breed. Otherwise, the feed-types would be compared through weighted averages with weights determined by the composition of the herd. For example, suppose feed A is splendid for Jerseys but mundane for Holsteins while feed B is splendid for Holsteins but mundane for Jerseys. Finally, let feed C be pretty good for both. In a mixed herd, feed C would be the feed of choice. If the composition of the herd were to become predominantly Jerseys, A might be the feed of choice with the gains in the Jerseys more than offsetting the losses in the Holsteins. A similar argument applies to feed B and a herd that is predominantly Holsteins.

Linar mixed models - resources

Linar mixed models - resources

West, Galecki: Practical Approach

Linear Mixed Models: A Practical Guide Using Statistical Software

http://books.google.com/books?id=LSJ__7lDSdgC&printsec=frontcover&dq=west+galecki&ei=nPzOR7f0OJy8zATGpqmwBQ&sig=rI8DjvFAgy8W-5lcgbZX0WZTiXY
book-accompanying site with datasets, code, errata etc
http://www-personal.umich.edu/~bwest/almmussp.html


books google: Designing Experiments and Analyzing Data....
http://books.google.com/books?id=h-bMhmQMifsC&printsec=frontcover&dq=designing+experiments+and+analyzing+data&ei=pxfQR6mWHJPAzATw04iwBQ&sig=DlC1utBSuL1magJSjkdDxa-_-xw#PRA2-PA145,M1
- provides explanation of both ANOVA based mixed models with univariate and multivariate approach, as well as linear mixed models in the frame of multilevels modeling (chapters ,,,,)
- uses some mathematics, but in aN understandable form.

LIST of from site dedicated to HIERARCHICAL modeling
http://www.hlm-online.com/books/


multilevel data analysis - from basics - WEBCAST by Leroux
uwtv.org Multilevel data analysis


www.geocities.com/joophox/publist/whenwhy.pdf


Gelman

Data Analysis Using Regression and Multilevel/Hierarchical Models

Eugen Demidenko LMM: Theory and Application

MULTILEVEL MODELING - SOFTWARE
http://www.cmm.bristol.ac.uk/learning-training/multilevel-m-software/index.shtml

MM and R
http://www.cmm.bristol.ac.uk/learning-training/multilevel-m-software/r.shtml
package lme (Pinheiro, Bates 2000)
more recent version - lmer, lme4 (2005)
www.r-project.org/doc/Rnews/Rnews_2005-1.pdf

for Splus, R by Fox (2002)
cran.r-project.org/doc/contrib/Fox-Companion/appendix-mixed-models.pdf

LINEAR MIXED MODELS
http://www2.chass.ncsu.edu/garson/pa765/multilevel.htm
no mathematical formulas
mainly refers to SPSS, but can can be useful as introduction for any platform

Hubbard Alan longitudinal data analysis
marginal models, generalized estimating equations GEE, with different link functions - Poisson regression etc.
provides lecture notes, and chapters from his textbook on the analysis of longitudinal data (quite readable, but at moments requires to think about vectors, matrix albebra etc., last chapter on Mixed models is not (yet?) available online), includes examples of code for STATA, SAS.



www.nyu.edu/its/socsci/Docs/SPSSMixed.ppt
comparison of mixed models with GLM - some formulas, many PRACTICAL points

www.spss.ch/upload/1107355943_LinearMixedEffectsModelling.pdf

www.spss.ch/upload/1126184451_Linear%20Mixed%20Effects%20Modeling%20in%20SPSS.pdf

gsic.syr.edu/manuals/spss/SPSS%20Advanced%20Models%2015.0.pdf

longitudinal data analysis - mixed models,

COURSES
ONLINE -
statistics.com - Galecki see above - also author of a textbook
www.statistics.com/ourcourses/mixedmodels/
also check - http://www.hlm-online.com/books/

http://www.csc.fi/english/csc/courses/archive/GLM2008
september 2008 - helsinki - Mixed models in R

MAXIMUM LIKELIHOOD ESTIMATION
explained - BGIM - Purcell
http://statgen.iop.kcl.ac.uk/bgim/mle/sslike_1.html


http://tigger.uic.edu/~hedeker/ml.html
SAS, SPSS

PPT
blog.case.edu/jjw17/2006/04/27/ZyzanskiSeminar4bPowerpoint.ppt

SURVIVAL mixed models for survival analysis
doi.wiley.com/10.1002/bimj.4710390102
jas.fass.org/cgi/reprint/77/E-Suppl_2/147.pdf
http://www.ingentaconnect.com/content/bpl/biom/2001/00000057/00000001/art00012
http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V8V-4KFV26C-1&_user=949847&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000049130&_version=1&_urlVersion=0&_userid=949847&md5=8f89a4cc66b9fe62715703c0323305f0
http://www.wiwi.uni-bielefeld.de/~kauermann/survival/kauermann.pdf
http://links.jstor.org/sici?sici=0006-341X(200103)57%3A1%3C96%3AMMFSAW%3E2.0.CO%3B2-M

generalized linear model

online courses
XXXXXXX generalized - for quantitative, categorical, binary, count data XXXXXXXXXXXXX
http://genetics.agrsci.dk/statistics/courses/phd07/
in R