Difference between revisions of "StatsWAP2009July17"
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{{h2| Mixed Effects Models in MRI Research}} | {{h2| Mixed Effects Models in MRI Research}} | ||
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<li>''Fitting Linear Mixed Models in R'' : http://cran.osmirror.nl/doc/Rnews/Rnews_2005-1.pdf#page=27 | <li>''Fitting Linear Mixed Models in R'' : http://cran.osmirror.nl/doc/Rnews/Rnews_2005-1.pdf#page=27 | ||
| − | <li>''Using R and lmer to fit linear mixed models with crossed random effects'' : http://www.stat.wisc.edu/~burgette/lme4-4.pdf | + | <li>'''(old way)''' ''Using R and lmer to fit linear mixed models with crossed random effects'' : http://www.stat.wisc.edu/~burgette/lme4-4.pdf |
| + | <li>'''(new way)''' ''Fitting Mixed-Effects Models Using the lme4 Package in R'' : http://www.stat.wisc.edu/~bates/IMPS2008/lme4D.pdf | ||
| + | <li>Tips on using mixed models in R: http://psy-ed.wikidot.com/glmm | ||
| + | </ul> | ||
| + | |||
| + | |||
| + | <h2>Suggested Books</h2> | ||
| + | <ul> | ||
| + | <li>Applied Longitudinal Data Analysis : http://books.google.com/books?id=DIlsijNIYtUC&dq=Applied+Longitudinal+data+analysis&printsec=frontcover&source=bn&hl=en&ei=pdVgSq6QNtTktgf2h4DoDA&sa=X&oi=book_result&ct=result&resnum=4 | ||
| + | <li>Linear Mixed Models for Longitudinal Data : http://www.amazon.com/Linear-Mixed-Models-Longitudinal-Data/dp/0387950273 | ||
| + | <li>Analysis of Longitudinal Data : http://www.amazon.com/Analysis-Longitudinal-Data-Peter-Diggle/dp/0198524846 | ||
</ul> | </ul> | ||
Latest revision as of 01:14, 3 July 2022
Mixed Effects Models in MRI Research
Summer Seminar Series: Statistics without the Agonizing Pain (WAP Series)
Time/Location: Friday, July 17, 2009 – 3 pm – 4 pm Clark Hall 314
Speaker: Yang An, National Institute on Aging - Intramural Research Program
Laboratory of Personality and Cognition - http://www.grc.nia.nih.gov/branches/lpc/lpc.htmv
Cognition section - http://www.grc.nia.nih.gov/branches/lpc/sresnick.htm
Mixed effects models (continuous outcomes) - also known as random effects models
Generalized estimating equations (discontinuous outcomes)
General topics
- Ordinary regression model - each data point comes from a separate "person" - L2 fit - Fixed effects
- ME model - each subject has its own slope. Find population slope.
- REML - restricted maximum likelihood -- preferred for model estimation. Not suitable for model comparison with differing random effects.
- Empirical Bayes (EB) for estimation of individual trajectories
Links about R
- Fitting Linear Mixed Models in R : http://cran.osmirror.nl/doc/Rnews/Rnews_2005-1.pdf#page=27
- (old way) Using R and lmer to fit linear mixed models with crossed random effects : http://www.stat.wisc.edu/~burgette/lme4-4.pdf
- (new way) Fitting Mixed-Effects Models Using the lme4 Package in R : http://www.stat.wisc.edu/~bates/IMPS2008/lme4D.pdf
- Tips on using mixed models in R: http://psy-ed.wikidot.com/glmm
Suggested Books
- Applied Longitudinal Data Analysis : http://books.google.com/books?id=DIlsijNIYtUC&dq=Applied+Longitudinal+data+analysis&printsec=frontcover&source=bn&hl=en&ei=pdVgSq6QNtTktgf2h4DoDA&sa=X&oi=book_result&ct=result&resnum=4
- Linear Mixed Models for Longitudinal Data : http://www.amazon.com/Linear-Mixed-Models-Longitudinal-Data/dp/0387950273
- Analysis of Longitudinal Data : http://www.amazon.com/Analysis-Longitudinal-Data-Peter-Diggle/dp/0198524846
