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 and Ting Qian)
- 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
2/28: Advanced issues in mixed regression models (Florian Jaeger)