Differences between revisions 2 and 5 (spanning 3 versions)
Revision 2 as of 2008-11-09 02:17:53
Size: 2553
Editor: cpe-67-240-134-21
Comment:
Revision 5 as of 2011-08-09 19:56:10
Size: 3395
Editor: echidna
Comment:
Deletions are marked like this. Additions are marked like this.
Line 3: Line 3:
#acl HlpLabGroup,TanenhausLabGroup:read,write,delete,revert,admin All:read #acl HlpLabGroup:read,write,delete,revert,admin All:read
Line 11: Line 11:
|| [wiki:/Session0 The week before the tutorial] || November 17 || Prepare ||
|| [wiki:/Session1 Session 1] || November 24 || Linear and logistic regression ||
|| [wiki:/Session2 Session 2] || November 25 || multilevel models ||
== Goal of this workshop ==
To provide sufficient background and a platform for a discussion of common issues in linear and logistic regression fitting, including applications of multilevel models.

Some of the specific questions that you have sent me:

 * Data distributions and transformations to increase normality
 * How robust are LME's in the face of normality violations (short answer: relatively robust; there are worse violations)
 * Evaluation of multiple logistic regression model
  * also: "Precision measures" -- I assume this refers to tests of the quality of fit of a model?
  * decision criteria to include or exclude variables. Why researchers sometimes keep insignificant predictors in the model?
 * When to use mixed-effects models
 * Conceptual background

|| [[DenmarkMiniCourseSession0|The week before the tutorial]] || November 17 || Prepare ||
|| [[DenmarkMiniCourseSession1|Session 1]] || November 24 || Linear and logistic regression ||
|| [[DenmarkMiniCourseSession2|Session 2]] || November 25 || multilevel models ||
Line 19: Line 32:
 * Analyzing Linguistic Data: A Practical Introduction to Statistics using R by Harald Baayen (2008). [http://www.amazon.com/Analyzing-Linguistic-Data-Introduction-Statistics/dp/0521882591/ hardback ($97)] [http://www.amazon.com/Analyzing-Linguistic-Data-Introduction-Statistics/dp/0521709180/ paperback ($35)] '''[attachment:baayen_analyzing_08.pdf Complete electronic draft]'''. Baa08.  * Analyzing Linguistic Data: A Practical Introduction to Statistics using R by Harald Baayen (2008). [[http://www.amazon.com/Analyzing-Linguistic-Data-Introduction-Statistics/dp/0521882591/|hardback ($97)]] [[http://www.amazon.com/Analyzing-Linguistic-Data-Introduction-Statistics/dp/0521709180/|paperback ($35)]] '''[[attachment:baayen_analyzing_08.pdf|Complete electronic draft]]'''. Baa08.
Line 22: Line 35:
 * [http://www.amazon.com/Analysis-Regression-Multilevel-Hierarchical-Models/dp/0521867061/ 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/Analysis-Regression-Multilevel-Hierarchical-Models/dp/0521867061/|Data Analysis Using Regression and Multilevel/Hierarchical Models]] by Gelman & Hill (2007). [[http://www.stat.columbia.edu/~gelman/arm/|Online resources]]. G&H07.
Line 25: Line 38:
 * [http://www.amazon.com/Introductory-Statistics-R-Peter-Dalgaard/dp/0387954759/ Introductory Statistics with R] by Peter Dalgaard (2004). [http://staff.pubhealth.ku.dk/~pd/ISwR.html Online resources]. [http://site.ebrary.com/lib/rochester/Doc?id=10047812 Electronic copy through U of R libraries]. Dal04.
 * [http://www.amazon.com/Categorical-Analysis-Wiley-Probability-Statistics/dp/0471360937/ Categorical Data Analysis] by Alan Agresti (2002). [http://www.stat.ufl.edu/~aa/cda/cda.html Online resources]. Agr02.
 * [[http://www.amazon.com/Introductory-Statistics-R-Peter-Dalgaard/dp/0387954759/|Introductory Statistics with R]] by Peter Dalgaard (2004). [[http://staff.pubhealth.ku.dk/~pd/ISwR.html|Online resources]]. [[http://site.ebrary.com/lib/rochester/Doc?id=10047812|Electronic copy through U of R libraries]]. Dal04.
 * [[http://www.amazon.com/Categorical-Analysis-Wiley-Probability-Statistics/dp/0471360937/|Categorical Data Analysis]] by Alan Agresti (2002). [[http://www.stat.ufl.edu/~aa/cda/cda.html|Online resources]]. Agr02.
Line 31: Line 44:
 * [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).
 * [[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).

HLP Lab Mini Course on Regression Methods

November 24 & 25, Copenhagen, Denmark

Goal of this workshop

To provide sufficient background and a platform for a discussion of common issues in linear and logistic regression fitting, including applications of multilevel models.

Some of the specific questions that you have sent me:

  • Data distributions and transformations to increase normality
  • How robust are LME's in the face of normality violations (short answer: relatively robust; there are worse violations)
  • Evaluation of multiple logistic regression model
    • also: "Precision measures" -- I assume this refers to tests of the quality of fit of a model?
    • decision criteria to include or exclude variables. Why researchers sometimes keep insignificant predictors in the model?
  • When to use mixed-effects models
  • Conceptual background

The week before the tutorial

November 17

Prepare

Session 1

November 24

Linear and logistic regression

Session 2

November 25

multilevel models

Readings

In the sessions, I refer to the following readings:

Obligatory:

Recommended:

Also useful:

R packages

Please make sure you have the following R packages installed before you attend the workshop. Please also note that I will assume R version 2.7.1, and it's easier if you have that same version (or at least that recent a version).

  • Design. Linear and generalized linear regression.

  • lme4. Multilevel modeling.

  • ARM. Companion package for Gelman & Hill (2007).

  • languageR. Companion package for Baayen (2008).

DenmarkMiniCourse (last edited 2011-08-09 19:56:10 by echidna)

MoinMoin Appliance - Powered by TurnKey Linux