Longitudinal data analysis using R
>eR-BioStat
Topics
The course in slides format and and online book
The course consists of 7 chapters. The slides cover the complete course while the online course covers a part of the topics from the slides using different set of examples.
All the examples are illustrated using R and the code is available in the R program of the chapter.
Useful R functions include:
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lme()
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gee()
Slides for the course
The slides for the course consists of the following chapters:
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Chapter 1: Introduction to Longitudinal Data Analysis.
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Chapter 2: Exploratory Data Analysis.
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Chapter 3: Models for Longitudinal Gaussian Data.
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Chapter 4: Practical Guide Using R.
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Chapter 5: Introduction to Longitudinal non-Gaussian data.
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Chapter 6: Models for Correlated Binary Data.
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Chapter 7: Models for Correlated Count Data.
External datasets and R program are available as a part of the slides or in the Data/R program repository.
The Tex file that was used to produce the slides is available in the link below. You can use this Tex file to re-produce the slides or to modified the slides for your own course/education program. Note that other files (figures and pictures are needed to re-produce the slides and available in the Zip file link).
Online course
This online course presents the basic concepts of linear and nonlinear mixed models using R. This is an online course which was developed by Marc Lavielle within his initiative statistics in action with R (http://sia.webpopix.org/). R code for all examples illustrated in the course is available online.
The course structure:
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Introduction.
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The orthodont data.
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Fitting linear models to the data.
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Mathematical definition of a linear mixed effects models.
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Statistical inference in linear mixed effects models.
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Fitting linear mixed effects models to the orthodont data.
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Some examples of models and designs:
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One factor (or one-way) classification
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Two factors block design
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