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HLP Lab Mini Course on Regression Methods

May 27 2008 - June 9 2008

Texts

[http://www.amazon.com/Analysis-Regression-Multilevel-Hierarchical-Models/dp/0521867061/ref=sr_1_1?ie=UTF8&s=books&qid=1211219851&sr=8-1 Data Analysis Using Regression and Multilevel/Hierarchical Models] by Gelman & Hill (2007). [http://www.stat.columbia.edu/~gelman/arm/ Online resources]. G&H07.
[http://www.amazon.com/Analyzing-Linguistic-Data-Introduction-Statistics/dp/0521882591/ref=sr_1_1?ie=UTF8&s=books&qid=1211219948&sr=8-1 Analyzing Linguistic Data: A Practical Introduction to Statistics using R] by Harald Baayen (2008). [attachment:baayen_analyzing_08.pdf Complete electronic draft]. Baa08.
[http://www.amazon.com/Introductory-Statistics-R-Peter-Dalgaard/dp/0387954759/ref=sr_1_1?ie=UTF8&s=books&qid=1211228905&sr=8-1 Introductory Statistics with R]. [http://staff.pubhealth.ku.dk/~pd/ISwR.html Online resources]. Dal04.
[http://www.amazon.com/Categorical-Analysis-Wiley-Probability-Statistics/dp/0471360937/ref=pd_bbs_1?ie=UTF8&s=books&qid=1211231537&sr=8-1 Categorical Data Analysis] by Alan Agresti. [http://www.stat.ufl.edu/~aa/cda/cda.html Online resources]. Agr02.

R packages

[http://cran.r-project.org/web/packages/Design/index.html Design]. Linear and generalized linear regression.
[http://cran.r-project.org/web/packages/lme4/index.html lme4]. Multilevel modeling.
[http://cran.r-project.org/web/packages/arm/index.html ARM]. Companion package for Gelman & Hill (2007).
[http://cran.r-project.org/web/packages/languageR/index.html languageR]. Companion package for Baayen (2008).

Session 0: Basics

Understanding of this material will be assumed throughout the course. Please read these introductory materials and make sure you understand them before beginning the readings for the first session.

Reading

Baa08	Chapter 1 (pp. 1-20)	Intro to R.
G&H07	Chapter 2 (pp. 13-26)	Intro to probability theory.
Dal04	???	???

Session 1: Linear regression

Reading

G&H07	Chapter 3 (pp. 29-49)	Linear regression: the basics
Baa08	Section 4.3.2 (pp. 91 - 105)	Functional relations: linear regression
	Sections 6 - 6.2.1 (pp. 181-198)	Regression Modeling (Introduction and Ordinary Least Squares Regression)
	Section 6.6 (pp. 258-259)	General considerations

Assignments

G&H07	Section 3.9 (pp. 50-51)	Exercises 3 and 5
Baa08	Section 4.7 (p. 126)	Exercises 3 and 7*

* (for Exercise 7, Baayen treats linear regression using lm or ols as the same as analysis of covariance (see section 4.4.1 (pp. 117-119))).

Session 2: Issues in linear regression

Reading

G&H07	Chapter 4 (pp. 53-74)	Linear regression: before and after fitting the model
Baa08	Sections 6.2.2-6.2.4 (pp. 198-212)	Collinearity, Model criticism, and Validation
	Section 6.4 (pp. 234-239)	Regression with breakpoints

Assignments

G&H07	Section 4.9 (p.76)	Exercise 4
Baa08	Section 6.7 (p. 260)	Exercise 1

In addition to the book problems, we will distribute a data set from the ongoing ngrams project.

Session 3: Multilevel (a.k.a. Hierarchical, a.k.a. Mixed ) Linear Models

Reading

G&H07	Sections 1.1-1.3 (pp. 1-3)	Intro, examples, motivation
	Chapter 11 (pp. 237-248)	Multilevel structures
	Chapter 12 (pp. 251-277)	Multilevel linear models: the basics

Assignments

Session 4: Logistic regression, Generalized Linear Multilevel Models

Reading

G&H07	Chapter 5 (pp. 79-105)	Logistic regression
Baa08	Section 6.3 (pp. 214-234)	Generalized Linear Models
	Section 6.4 (pp. 239.243)	end of Regression with breakpoints
Agr02	Section 16.3 (624-625)	???

Assignments

Session 5: Mixed logit models

Reading

G&H07

Chapter 14 (pp. 301-321)

Multilevel logistic regression*

* In this chapter, Gelman & Hill define some multilevel models in BUGS rather than in R. We will either provide translations for you, will do the translations together in class, or will assign the translations as an assignment.

Assignments

Session 6: Computational methods for model fitting

Reading

G&H07	Chapter 18 (pp. 387-413)	Likelihood and Bayesian inference and computation
Agr02	Section 15.2 (pp. 604-611)	???
lme4	implementation vignettes	attachment:Implementation.pdf attachment:Theory.pdf attachment:Notes.pdf

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-== Week 1:  Linear regression ==
=== Session 1:  ===
|| Reading || ||
|| Assignments || ||
=== Session 2:  ===
|| Reading || ||
|| Assignments || ||
+== Texts ==
 * [http://www.amazon.com/Analysis-Regression-Multilevel-Hierarchical-Models/dp/0521867061/ref=sr_1_1?ie=UTF8&s=books&qid=1211219851&sr=8-1 Data Analysis Using Regression and Multilevel/Hierarchical Models] by Gelman & Hill (2007).  [http://www.stat.columbia.edu/~gelman/arm/ Online resources].  G&H07.
 * [http://www.amazon.com/Analyzing-Linguistic-Data-Introduction-Statistics/dp/0521882591/ref=sr_1_1?ie=UTF8&s=books&qid=1211219948&sr=8-1 Analyzing Linguistic Data:  A Practical Introduction to Statistics using R] by Harald Baayen (2008).  [attachment:baayen_analyzing_08.pdf Complete electronic draft].  Baa08.
 * [http://www.amazon.com/Introductory-Statistics-R-Peter-Dalgaard/dp/0387954759/ref=sr_1_1?ie=UTF8&s=books&qid=1211228905&sr=8-1 Introductory Statistics with R].  [http://staff.pubhealth.ku.dk/~pd/ISwR.html Online resources].  Dal04.
 * [http://www.amazon.com/Categorical-Analysis-Wiley-Probability-Statistics/dp/0471360937/ref=pd_bbs_1?ie=UTF8&s=books&qid=1211231537&sr=8-1 Categorical Data Analysis] by Alan Agresti.  [http://www.stat.ufl.edu/~aa/cda/cda.html Online resources].  Agr02.
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+Line 15:
-== Week 2:  Logistic regression ==
=== Session 3:  ===
|| Reading || ||
|| Assignments || ||
=== Session 4:  ===
|| Reading || ||
|| Assignments || ||
+== R packages ==
 * [http://cran.r-project.org/web/packages/Design/index.html Design].  Linear and generalized linear regression.
 * [http://cran.r-project.org/web/packages/lme4/index.html lme4].  Multilevel modeling.
 * [http://cran.r-project.org/web/packages/arm/index.html ARM].  Companion package for Gelman & Hill (2007).
 * [http://cran.r-project.org/web/packages/languageR/index.html languageR].  Companion package for Baayen (2008).
-Line 25:
+Line 21:
-== Week 3:  Multilevel regression ==
=== Session 5:  ===
|| Reading || ||
|| Assignments || ||
=== Session 6:  ===
|| Reading || ||
|| Assignments || ||
+== Session 0:  Basics ==
Understanding of this material will be assumed throughout the course.  Please read these introductory materials and make sure you understand them before beginning the readings for the first session.
-Line 33:
+Line 24:
-== Week 4:  Implementations (optional) ==
=== Session 7:  lme4 implementation details ===
|| Reading || ||
|| Assignments || ||
+=== Reading ===
|| Baa08 || Chapter 1 (pp. 1-20) ||  Intro to R. ||
|| G&H07 || Chapter 2 (pp. 13-26) ||  Intro to probability theory.||
|| Dal04 || ??? || ??? ||
 
== Session 1:  Linear regression ==
=== Reading ===
|| G&H07 || Chapter 3 (pp. 29-49) || Linear regression:  the basics ||
|| Baa08 || Section 4.3.2 (pp. 91 - 105) || Functional relations:  linear regression ||
|| || Sections 6 - 6.2.1 (pp. 181-198) || Regression Modeling (Introduction and Ordinary Least Squares Regression) ||
|| || Section 6.6 (pp. 258-259) || General considerations ||

=== Assignments ===
|| G&H07 || Section 3.9 (pp. 50-51) || Exercises 3 and 5 ||
|| Baa08 || Section 4.7 (p. 126) || Exercises 3 and 7* ||
*  (for Exercise 7, Baayen treats linear regression using {{{lm}}} or {{{ols}}} as the same as analysis of covariance (see section 4.4.1 (pp. 117-119))).

== Session 2: Issues in linear regression ==
=== Reading ===
|| G&H07 || Chapter 4 (pp. 53-74) || Linear regression:  before and after fitting the model ||
|| Baa08 || Sections 6.2.2-6.2.4 (pp. 198-212) || Collinearity, Model criticism, and Validation ||
|| || Section 6.4 (pp. 234-239) || Regression with breakpoints ||

=== Assignments ===
|| G&H07 || Section 4.9 (p.76) || Exercise 4 ||
|| Baa08 || Section 6.7 (p. 260) || Exercise 1 ||

In addition to the book problems, we will distribute a data set from the ongoing ngrams project.

== Session 3:  Multilevel (a.k.a. Hierarchical, a.k.a. Mixed ) Linear Models ==
=== Reading ===
|| G&H07 || Sections 1.1-1.3 (pp. 1-3) || Intro, examples, motivation ||
|| || Chapter 11 (pp. 237-248) || Multilevel structures ||
|| || Chapter 12 (pp. 251-277) || Multilevel linear models:  the basics ||

=== Assignments ===

== Session 4:  Logistic regression, Generalized Linear Multilevel Models ==
=== Reading ===
|| G&H07 || Chapter 5 (pp. 79-105) || Logistic regression ||
|| Baa08 || Section 6.3 (pp. 214-234) || Generalized Linear Models ||
|| || Section 6.4 (pp. 239.243) || end of Regression with breakpoints ||
|| Agr02 || Section 16.3 (624-625) || ??? ||

=== Assignments ===

== Session 5:  Mixed logit models ==
=== Reading ===
|| G&H07 || Chapter 14 (pp. 301-321) || Multilevel logistic regression* ||
* In this chapter, Gelman & Hill define some multilevel models in BUGS rather than in R.  We will either provide translations for you, will do the translations together in class, or will assign the translations as an assignment.

=== Assignments ===

== Session 6:  Computational methods for model fitting ==
=== Reading ===
|| G&H07 || Chapter 18 (pp. 387-413) || Likelihood and Bayesian inference and computation ||
|| Agr02 || Section 15.2 (pp. 604-611) || ??? ||
|| lme4 || implementation vignettes || attachment:Implementation.pdf attachment:Theory.pdf attachment:Notes.pdf ||