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| 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``` and ```Design``` packages and their dependencies installed. | 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. |
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| install.packages(c("languageR", "Design")) | install.packages(c("languageR", "Design", "reshape", "ggplot2")) |
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| ---- '''Week 1''' |
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| * 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''' |
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| * slides: [[attachment:Rochester-IntroToRegressionModels.pdf]] and code: [[attachment:Code-Rochester-IntroToRegressionModels.R]] | |
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| ---- '''Week 3''' |
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| * we'll go through some example data sets in more detail | * we'll go through some example data sets in more detail (please download this: [[attachment:exampledata.zip]]) |
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| * 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]] |
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| ---- '''Week 4''' |
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| 2/21: Relaxing the linearity assumption - within and beyond the generalized linear mixed model framework (Florian Jaeger and Ting Qian) | 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) |
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| * 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. | * 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]] |
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| 2/23: Advanced issues in mixed regression models (Florian Jaeger) | ---- '''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]] |
February 2011 Regression Workshop
Prerequisites
Please be sure to come with a laptop with a recent version of 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: regdata (and more for Alex: alexdat)
A lot of what we're going to talk about (plus more!) is covered in the first chapter of 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: Rochester-IntroToRegressionModels.pdf and code: 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: 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: ggplot_tutorial.R
for above code to work, you will need to run these functions first: myFunctions.R
slides from the first ggplot session (containing mostly some example plots): ggplot_tutorial.pdf
Week 4
2/21: Coding your contrasts to test the hypothesis you really mean to test (Maureen Gillespie, Northeastern University)fakedata.txt 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: 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)