Difference between revisions of "SMHS LinearModeling"
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Review and demonstration of computing and visualizing the regression-model coefficients (effect-sizes), (fixed-effect) linear model assumptions, examination of residual plots, and independence. | Review and demonstration of computing and visualizing the regression-model coefficients (effect-sizes), (fixed-effect) linear model assumptions, examination of residual plots, and independence. | ||
| − | ===[[ | + | ===[[SMHS_LinearModeling_LMM |Linear mixed effects analyses]]=== |
Scientific inference base don fixed and random effect models, assumptions, and mixed effects logistic regression. | Scientific inference base don fixed and random effect models, assumptions, and mixed effects logistic regression. | ||
Revision as of 10:02, 1 February 2016
Contents
Scientific Methods for Health Sciences - Linear Modeling
The following sub-sections represent a blend of model-based and model-free scientific inference, forecasting and validity.
Statistical Software
This section briefly describes the pros and cons of different statistical software platforms.
Quality Control
Discussion of data Quality Control (QC) and Quality Assurance (QA) which represent important components of data-driven modeling, analytics and visualization.
Multiple Linear Regression
Review and demonstration of computing and visualizing the regression-model coefficients (effect-sizes), (fixed-effect) linear model assumptions, examination of residual plots, and independence.
Linear mixed effects analyses
Scientific inference base don fixed and random effect models, assumptions, and mixed effects logistic regression.
Machine Learning Algorithms
Data modeling, training , testing, forecasting, prediction, and simulation.
References
- Bates, D.M., Maechler, M., & Bolker, B. (2012). lme4: Linear mixed-effects models using S4 classes. R package version 0.999999-0.
- Baayen, R.H. (2008). Analyzing Linguistic Data: A Practical Introduction to Statistics Using R. Cambridge: Cambridge University Press.
- Baayen, R.H., Davidson, D.J., Bates, D.M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59, 390-412.
- Barr, D.J., Levy, R., Scheepers, C., & Tilly, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68, 255–278.
- Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R., Stevens, M. H. H., & White, J. S. S. (2009). Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology & Evolution, 24(3), 127-135.
- Wike, E.L., & Church, J.D. (1976). Comments on Clark’s “The language-as-fixed-effect fallacy”. Journal of Verbal Learning & Verbal Behavior, 15, 249-255.
- Winter, B. (2013). Linear models and linear mixed effects models in R with linguistic applications arXiv:1308.5499.
- SOCR Home page: http://www.socr.umich.edu
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