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= February 2011 Regression Workshop =

== Prerequisites ==
Please be sure to come with a laptop with a recent version of [[http://cran.r-project.org/|R]] installed (i.e. 2.12.0 or higher). You should also have the ```languageR```, ```Design```, ```reshape```, and ```ggplot2``` packages and their dependencies installed. 

n.b. A quick and easy way to do this at the R prompt:
{{{
install.packages(c("languageR", "Design", "reshape", "ggplot2"))
}}}


== Location ==
Meliora 269 (Kresge) for 2/2 and Meliora 366 thereafter.

== Schedule ==

----
'''Week 1'''

2/2: Introduction to R for regression (Alex Fine and Dave Kleinschmidt)

    * What format should your data be in? How to get your data into the right format, how to import it into R?
    * How to inspect variables, add/name/rename/delete variables? 
    * How to do simple data summaries in R?
    * How to prepare your data for regression analysis, understanding your dependent and independent variables (histograms, outliers)
    * Here are the datasets Dave and Alex will be working with: [[attachment:regdata]] (and more for Alex: [[attachment:alexdat]])
    * A lot of what we're going to talk about (plus more!) is covered in the first chapter of [[http://www.ualberta.ca/~baayen/publications/baayenCUPstats.pdf|Baayen (2008) [PDF]]].  If you get bored, read through this and work through the exercises on page 20.

----
'''Week 2'''

2/7: Conceptual Introduction to linear and logistic regression with code examples in R (Florian Jaeger)

    * what's regression: mathematical and geometric intro to ordinary regression
    * how to run ordinary regressions in R
    * conceptual introduction to multilevel/mixed, interpretation of random effects.
    * what are the assumptions made by these models? How do they differ from and related to analysis of variance?
    * slides: [[attachment:Rochester-IntroToRegressionModels.pdf]] and code: [[attachment:Code-Rochester-IntroToRegressionModels.R]]

2/9: Some practical issues in ordinary and mixed regression analyses (Florian Jaeger)

    * collinearity: what is it? why is it a problem? what to do about it?
    * understanding model comparison

----
'''Week 3'''

2/14: Linear and logistic regression examples (Alex Fine and Florian Jaeger)

    * we'll go through some example data sets in more detail (please download this:  [[attachment:exampledata.zip]])
    * you're also welcome to bring your own data to analyze
    * this meeting will also serve to collect questions to be addressed in the remaining sessions

2/16: Visualization of your data with ggplot2 in R (Judith Degen and Florian Jaeger)

    * the ggplot2 package provides a powerful way to create plots, ranging from simple bar and line plots, to predictions of non-linear models, smoothers, etc. 
    * we will go through some simple example, but also show how to use ggplot to visualize eye-tracking data or complex relationships between covariates for various types of dependent variables.
    * some code that shows some of what you can do with the different geoms: [[attachment:ggplot_tutorial.R]]
    * for above code to work, you will need to run these functions first: [[attachment:myFunctions.R]]
    * slides from the first ggplot session (containing mostly some example plots): [[attachment:ggplot_tutorial.pdf]]

----
'''Week 4'''

2/21: Coding your contrasts to test the hypothesis you really mean to test (Maureen Gillespie, Northeastern University)[[attachment:fakedata.txt]] [[attachment:CodingLectureRochester.R]]

2/23: Relaxing the linearity assumption - within and beyond the generalized linear mixed model framework (Florian Jaeger) 

    * sometimes the linearity assumption of generalized linear mixed models is not warranted. 
    * what ways are there to test for non-linear trends in the data? what type of non-linearity can we include in our models within the generalized linear mixed models framework?
    * what if there is a specific non-linear model we're interested in testing? non-linear mixed models provide a powerful extension to the generalized linear mixed models, but they come with a new set of challenges that we discuss in this session.
    * Florian's slides: [[attachment:Rochester-CommonIssuesAndSolutionsWithRegressionModels.pdf]]

----
'''Week 5'''

''Note: there is no session on Monday. The session planned for 2/28 is moved to 3/2''

3/02: Non-linear mixed models (Ting Qian)


[[attachment:slides]]
[[attachment:script]]
[[attachment:discourse-info.tab]]