Kaisa_2012_3_photo by Veikko Somerpuro

Enrol
5.2.2020 at 09:00 - 19.2.2020 at 23:59

Timetable

Here is the course’s teaching schedule. Check the description for possible other schedules.

DateTimeLocation
Mon 9.3.2020
09:00 - 15:30
Tue 10.3.2020
09:00 - 15:30
Wed 11.3.2020
09:00 - 15:30
Thu 12.3.2020
09:00 - 15:30
Fri 13.3.2020
09:15 - 15:45

Description

Pre-required knowledge

  • Basic statistics (e.g. mean, variance, normality)
  • No R knowledge is required. You will learn R ‘on the fly’.

The course is very intensive so it will require the participants' full attention. Please, do not schedule other courses or other appointments during the course week.

Participants must bring their own laptop. Please ensure that you have system administration rights to install R and R packages on your computer. Instructions what to install will be provided before the start of the course.

Basic statistics.

We begin with an introduction to R and provide a protocol for data exploration to avoid common statistical problems. We will discuss how to detect outliers, deal with collinearity and transformations.

An important statistical tool is multiple linear regression. Various basic linear regression topics will be explained from a biological point of view. We will discuss potential problems and show how generalised linear models (GLM) can be used to analyse count data, presence-absence data and proportional data. Sometimes, parametric models (linear regression, GLM) do not quite fit the data and in such cases generalised additive models (GAM; a smoothing technique) can be used.

During the course several case studies are presented, in which the statistical theory is integrated with applied analyses in a clear and understandable manner.

Key words

  • Introduction to R
  • Outliers
  • Transformations
  • Collinearity (correlation between covariates)
  • Multiple linear regression
  • Model selection
  • Visualising results
  • Poisson GLM
  • Overdispersion
  • Negative binomial GLM
  • Binary and proportional data
  • ggplot2
  • Logistic regression

Monday - Thursday

09.00am to 16.00pm including 1 hour lunch break and a 20 minutes break both morning and afternoon.

Friday

09.00am to 15.30pm.

Monday

  • General introduction
  • Introduction to R
  • Theory presentation on data exploration (outliers, collinearity, transformations, relationships, interactions)
    • Based on Zuur et al. (2010) and Ieno and Zuur (2015)
  • Two exercises

Tuesday & Wednesday morning

  • Theory presentation on linear regression
    • Different strategies for model selection
    • Interactions
    • Dealing with categorical covariates
    • Sketching model fit
  • Two exercises

Wednesday afternoon and Thursday

  • Theory presentation on Poisson, negative binomial, Bernoulli and binomial distributions
    • Based on Chapter 1 in Zuur et al. (2013)
  • Theory presentation on GLM
    • Poisson GLM
    • Negative binomial GLM
    • Bernoulli GLM
    • Based on Chapter 1 in Zuur et al. (2013)
    • How to deal with overdispersion
  • Three exercises

Friday

  • Theory presentation on GAM
    • Two exercises using Gaussian GAM and Poisson and negative binomial GAMs
    • Based on various chapters in Zuur (2012)
    • What to present in a paper

Time allowing

  • Short theory presentation on binomial GLM and gamma GLM
  • Two exercises for binomial and gamma GLM
    • Video solution files are available

Lectures and exercises during classes.

Course material

Pdf files of all powerpoint presentations are provided. The powerpoint files are based on various chapters from:

  • Chapters 4 - 5 from Zuur, Ieno, Smith (2007). Analysing Ecological Data
  • A Beginner’s Guide to GLM and GLMM using MCMC with R. (2013)
  • A Beginner’s Guide to Zero Inflated Models with R. (2016)
  • Chapter 3 in Beginner’s Guide to GAM with R. Zuur (2013)

Books are not included in the course fee. The course can be followed without purchasing these books.

Recommended literature

  • Zuur, Hilbe, Ieno (2013). A Beginner’s Guide to GLM and GLMM with R.
  • Ieno, Zuur (2015) A Beginner’s Guide to Data Exploration and Visualisation with R.
  • Zuur (2013). A Beginner’s Guide to GAM with R.

These books are available from www.highstat.com

Pass/Fail

In English.

Teachers from Highland statistics:

  • Dr. Alain Zuur
  • Dr. Elena Ieno

Contact person: Anni Tonteri, senior advisor, YEB doctoral school