Difference between revisions of "SMHS LinearModeling"

From SOCR
Jump to: navigation, search
(Linear mixed effects analyses)
(Scientific Methods for Health Sciences - Linear Modeling)
 
Line 17: Line 17:
 
===[[SMHS_LinearModeling_MachineLearning|Machine Learning Algorithms]]===
 
===[[SMHS_LinearModeling_MachineLearning|Machine Learning Algorithms]]===
 
Data modeling, training , testing, forecasting, prediction, and simulation.
 
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 [http://arxiv.org/pdf/1308.5499.pdf arXiv:1308.5499].
 
 
  
 
<hr>
 
<hr>
 
* SOCR Home page: http://www.socr.umich.edu
 
* SOCR Home page: http://www.socr.umich.edu
 
{{translate|pageName=http://wiki.socr.umich.edu/index.php?title=SMHS_LinearModeling}}
 
{{translate|pageName=http://wiki.socr.umich.edu/index.php?title=SMHS_LinearModeling}}

Latest revision as of 07:47, 19 May 2016

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 based on fixed and random effect models, assumptions, and mixed effects logistic regression.

Machine Learning Algorithms

Data modeling, training , testing, forecasting, prediction, and simulation.




Translate this page:

(default)
Uk flag.gif

Deutsch
De flag.gif

Español
Es flag.gif

Français
Fr flag.gif

Italiano
It flag.gif

Português
Pt flag.gif

日本語
Jp flag.gif

България
Bg flag.gif

الامارات العربية المتحدة
Ae flag.gif

Suomi
Fi flag.gif

इस भाषा में
In flag.gif

Norge
No flag.png

한국어
Kr flag.gif

中文
Cn flag.gif

繁体中文
Cn flag.gif

Русский
Ru flag.gif

Nederlands
Nl flag.gif

Ελληνικά
Gr flag.gif

Hrvatska
Hr flag.gif

Česká republika
Cz flag.gif

Danmark
Dk flag.gif

Polska
Pl flag.png

România
Ro flag.png

Sverige
Se flag.gif