Mixed Effects Regression with R

Mixed Effects Regression with R#

Course Description#

This course builds on your existing knowledge of regression to fit more complex models that can handle more complicated data sets. In this session you will learn about mixed effects regression model, when to use them, and how to interpret the results.

While it is delivered as a stand alone session, it is designed as a part of a series of Regression with R workshops where the content develops the ideas further to give you a comprehensive understanding how regression can be used to address a broad range of questions.

The complete series includes:

  1. Introduction to Regression with R

  2. Regression Analysis in R: Adapting to Varied Data Types

  3. Mixed Effects Regression with R

Course Objectives#

  • Use regression answer to answer a wide range of research questions .

  • Fit a regression model with interactions between predictor variables.

  • Fit multi-level regression models

  • Extract and interpret the results from a range of regression models.

  • Design a regression model appropriate for addressing a specific research question.

Pre-requisite Knowledge#

This course will not include an introduction to R, or how to setup and use R or Rstudio. It is assumed you are comfortable coding in R and are familiar with:

  • How to write and execute commands in the R console.

  • What type of variables are available in R and how to work with these.

We also assume that you are comfortable with fitting in R and interpreting the output of:

  • Simple linear regression.

  • Multiple linear regression with categorical, binary or continuous predictor variables.

  • Logistic regression.

If not, we recommend that you consult our pre-requisite courses Introductory Regression Analysis with R and Regression Analysis in R: Adapting to Varied Data Types

The interactive network visualisation below displays the prerequisite structure for this course within the training program. Each node represents a course that you may need to complete beforehand, and the arrows show the recommended order in which to take them, leading up to your selected course. You can click on any course node to view more information about that course. This interactive tool helps you clearly see the learning path required to access this course, making it easier to plan your progress with the Coding for Reproducible Research Training (CfRR) initiative.

Pre-Reqs Subnetwork

Sign-up#

To check for upcoming course dates and to register, please visit the Workshop Schedule and Sign-up page.

Installation Guide#

All CfRR courses require attendees to use their own computer/laptop to follow workshop activities and take effective notes.

As this course extends upon Introduction to R, the installation instructions for the base R elements are the same, available Intro To R.

Alongside having R itself, three packages are needed for this workshop. The first two (devtools & learnr) are available from CRAN. The third is a package we have developed with the course materials and is available from GitHub (cfrrRTutorials).

This code will install these three packages.

install.packages("devtools") 
install.packages("learnr") 
library(devtools)
devtools::install_github("coding-for-reproducible-research/cfrrRtutorials")

If you encounter any difficulties with installation, please reach out.

Developers#

This workshop was developed by Eilis Hannon.

License Info#

The file ../individual_modules/advanced_regression_analysis_with_R/LICENSE.txt was not found.
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