02 Mar 2018
This series of tutorials and workshops will gradually work through an extensive range of frequentist and Bayesian
graphical and statistical theory and practice (focusing on R or JAGS interfaced from R).
It is advisable that you initially work through the following tutorials and associated workshops sequentially.
The tutorial series makes use of artificial data. The reasons for doing so are:
- simulating data allows us to fabricate the true underlying trends responsible for the data
and therefore enable us to evaluate how accurately the analyses tools subsequently reveal these trends.
- the process of simulating data is typically the reverse of analysing the data.
There must be consideration for how the response is to
relate to the predictors, the scale (normal, binomial, Poisson etc) of variables and parameters as well as how to incorporate sensible variability (noise).
Thus the process of simulating data specifically for a particular statistical analysis
can be as informative as a description of the analysis itself.
Where possible and appropriate, the workshop series users real research scenarios and data gleaned
largely from the worked examples of major Biostatistical texts so as to take advantage of the history and information surrounding
those examples as well as any familiarity that users may have with those examples.
These workshops are designed to provide extensive guided practice of the concepts and techniques highlighted in the tutorials.
The use of real data in the workshops:
- provides a greater familiarity and appreciation of the nature and issues surrounding real data
- provide insights into the diversity of analyses options and challenges presented by real data.
- allows researchers to better associate the links between data and research features with analysis decisions
Hence together, the tutorials and workshops provide a rich mixture of generic and specific analytical demonstration
and practice. I hope you find them useful - Enjoy!
- Installation of R
- Basic syntax
- Data types
- Object manipulation
- R Editors
- Constructing dataframes
- Importing (reading) and exporting (writing) dataTutorial
- Vectors within dataframesTutorial
- Manipulating dataframesTutorial
- Reshaping dataframes
- Merging dataframes
- Aggregating dataframes
- Transformations and derivatives
- List manipulations
- More complex manipulations
- Simulated data sets - random data generation
Introductory statistical principles
- Basic principlesTutorial
- Probability theory
- Measures of location and variability
- Degrees of freedom
- Opposing philosophiesTutorial
- Estimation and inferenceTutorial
- Least squares (LS)
- Maximum likelihood (ML)
R data summaries - numerical and graphical
- An introduction to variance structures in linear modelsTutorial
- Dealing with variance heterogeneity
- Dealing with temporal autocorrelation
- Dealing with spatial autocorrelation