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Computing and Modelling with R

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Computing and Modelling with R

Day 1: An Introduction to Statistical Computing with R 

Day 2: Applied Statistical Modelling with R 

Day 3: Data Visualisation with R.

The course is split into three days; participants can attend one day or more. All days will consist of interactive workshops, together with  time for guided computational practice on the material, supported by the lecturer and additional experts on the R language. Lunch will be provided on each day. Computers are provided, or participants can use their own laptop.

 Day 1 is suitable for people with no experience of R, and will be an introduction to programming in R. There is little mathematical statistical knowledge assumed, and will be an introduction to the programming language.

 Day 2 will be suitable for those that have attended Day 1, or who have some previous experience in  R. It will give an overview of statistical modelling in R.

 Day 3 will focus on more advanced techniques for programming in R. It will focus on methods for visualisation in data science, with applications driven from Biological applications, and assumes some programming knowledge in R, such as that from Day 1 of the course.

Course Code

S3RICMR

Course Dates

24th May 2017 – 26th May 2017

Places Available

Course Leader

Dr Ben Parker, Dr Helen Ogden, Dr Yves Berger, Prof Ben Macarthur
Course Description

Day 1 Aims:

To introduce a range of statistical methods implemented on computers; to give practice in applying methods and interpreting results from them; to develop the use of computers in the collection, validation, analysis and presentation of data; to help develop the knowledge and experience necessary to implement statistical computing methods.

  • enter and manipulate data within R;
  • perform basic statistical analyses using R and interpret the output;

 Day 1 Syllabus:

  • Data manipulation in R
  • Using the Help menu
  • Writing functions in R
  • Conditional execution and loops in R
  • Graphics in R
  • Apply the above programming skills in R to problems arising in data analysis
  • Interpretation of R output
  • Introduction to Linear Modelling in R
 Day 2 Aims: To introduce, via a hands-on approach, the basic concepts and principals in statistical modelling in a computational paradigm.

After taking this module, students should understand

  • why statistical modelling is important,
  • the terminology and statistical principles associated with modelling,
  • sufficient theory to deal with simple examples and have gained practical hands-on experience in more complex examples,
  • how to use R to fit, explore and exploit a variety of statistical models

 Day 2 Syllabus:

  • Revision on R: Data input, plotting and summaries
  • Standard statistical distribution
  • Principles of statistical inference
  • Likelihood

Regression: linear and generalised linear modelling

  • Model construction and estimation
  • Model selection and information criteria
  • Shrinkage regression (Lasso and ridge methods)

Random effects, mixed models, and data with complex correlation structures

  • Grouping structures in data
  • Interpretation of random effects and mixed models
  • Discrete data and generalised linear mixed models
  • Estimation of mixed models
  • Autoregression models

Smoothing and nonparametric regression

  • Kernel density estimation
  • Splines and penalised splines
  • Generalised additive models
  • Linear smoothing

 Day 3 Aims:

To introduce a range of data visualisation, dimensionality reduction and clustering techniques and their implementation in R and to give practice in applying these methods to a range of different datasets, primarily arising from the biological sciences.

Day 3 Syllabus:


Introduction to dimensionality reduction methods, including:

  • Principal components analysis
  • Multidimensional scaling 
  • t-Distributed Stochastic Neighbour Embedding (t SNE)

 

Introduction to clustering methods, including: 

  • k-means clustering
  • Hierarchical clustering and heatmaps
  • Network clustering 

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