February 2011 Regression Workshop
Schedule
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)
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?
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
2/14: Linear and logistic regression examples (Alex Fine and Florian Jaeger)
- we'll go through some example data sets in more detail
- 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.
2/21: 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.
2/23: Advanced issues in mixed regression models (Florian Jaeger)