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 ????? packages installed.

Location

Meliora 366???

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)