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PNS0224
Dealing with some complexities of GLMs
Unit 5: Dealing with some complexities of GLMs Teacher: Matt Low (matthew.low@slu.se ). May 31 and June 1 2021
1. Understanding and comparing the Normal, Poisson and Binomial distributions.
2. What is overdispersion and what effect does it have on your results? How can it be quantified, corrected for, and what does it all mean biologically?
3. What is zero inflation? How zero-inflation can be thought of as a form of overdispersion (and hence dealt with using the previous solutions). How zero- inflation can be considered as a combination of two separate processes and modelled using either hurdle models or mixture models.
The course activites includes initial readings, classroom discussions, hands-on activities, followed by home exercises. This unit corresponds to one theme/workshop and is planned for two days, equaling one credit (ca 27 hours of work for the PhD student). Example datasets will be provided but students could use their own datasets as well.
1. Understanding and comparing the Normal, Poisson and Binomial distributions.
2. What is overdispersion and what effect does it have on your results? How can it be quantified, corrected for, and what does it all mean biologically?
3. What is zero inflation? How zero-inflation can be thought of as a form of overdispersion (and hence dealt with using the previous solutions). How zero- inflation can be considered as a combination of two separate processes and modelled using either hurdle models or mixture models.
The course activites includes initial readings, classroom discussions, hands-on activities, followed by home exercises. This unit corresponds to one theme/workshop and is planned for two days, equaling one credit (ca 27 hours of work for the PhD student). Example datasets will be provided but students could use their own datasets as well.
Syllabus and other information
Syllabus
PNS0224 Dealing with some complexities of GLMs, 1.0 Credits
Subjects
Mathematical StatisticsEducation cycle
Postgraduate levelGrading scale
Pass / Failed
Prior knowledge
Admitted to PhD-studies. Basic knowledge of statistics and RObjectives
This course is a separate unit of the course theme Advanced Statistics in Practice that aims to fill potential knowledge gaps by preparing students to analyze, interpret and report their data using the most up-to-date methods in R. Special attention will be given to discussion of questions springing from the students’ own work and the biological interpretation of data. The course is intended to deepen the students’ understanding of all aspects of ecological inferences, not as a cookbook of "how to". Learning outcomes 1-Demonstrate the ability to identify relevant functions and packages in R for analyzing their own datasets. 2-Analyze data using R, including reporting advanced statistics, univariate and multivariate modelling and generation of graphs. 3-Interpret, think critically and draw conclusions on data analysis results.Content
Unit 5: Dealing with some complexities of GLMs Teacher: Matt Low (matthew.low@slu.se ). May 31 and June 1 2021 1. Understanding and comparing the Normal, Poisson and Binomial distributions. 2. What is overdispersion and what effect does it have on your results? How can it be quantified, corrected for, and what does it all mean biologically? 3. What is zero inflation? How zero-inflation can be thought of as a form of overdispersion (and hence dealt with using the previous solutions). How zero- inflation can be considered as a combination of two separate processes and modelled using either hurdle models or mixture models. The course activites includes initial readings, classroom discussions, hands-on activities, followed by home exercises. This unit corresponds to one theme/workshop and is planned for two days, equaling one credit (ca 27 hours of work for the PhD student). Example datasets will be provided but students could use their own datasets as well.Additional information
This course is the fifth independent course unit of five in the course theme Advanced Statistics in Practice organized by Mohammad Bahram in collaboration with the NJ-faculty research schools ’Ecology-basics and applications’ and ’Focus on Soils and Water’. The course plan for the theme and this course unit was accepted 20200922 by the steering group of the research school Ecology-basics and application.Course units of the theme Advanced Statistics in Practice 2021
•Time series analysis: Jonas Knape and Örjan Östman, February 1
•GIS and spatial analysis: Alistair Auffret and Mohammad Bahram, Feb. 15 and 18
•Meta-analysis: Julia Koricheva, April 12-13
•Getting more out of community data, Tomas Roslin and Giovanni Strona, May 3 & May 6
•Dealing with complexities of GLMs: Matt Low, May 31 & June 1
Responsible department
Department of Ecology