Workshops

Workshop in Generalised Additive Models

Presentations:

Claudia von Brömssen: Introduction to General Additive Models

Claudia von Brömssen: Generalised and mixed models in the GAM context

Michal Zmihorski: GAM in ornothological studies

Henrik Thurfjell: GAM in modelling bear populations

Sven Adler: GAM in species habitat modelling

Stefan Widgren: GAM for modelling the prevalence of infectious diseases

Mikael Franko: GAM for modelling excess mortality of infectious diseases

Valerio Bartolino: GAM in fish ecology

Willem Dekker: GAM for modelling the annual recruitment data of young eels

Jens Fölster: GAM for trend analysis in water quality data

Claudia von Brömssen: GAM for modelling time-varying relationships

Invitation to the 2016 workshop and instruction for registration (pdf)

Workshop in Mixed models

Presentations:

Ulf Olsson: Mixed Models (first day)

Lin Shi, Food Science: The application of mixed model in exploring time dependent postprandial metabolic changes - a randomized, cross-over study (second day)

Andrew Allen, Wildlife, Fish and Environmental Studies: Understanding intraspecific variation in movement patterns of moose: A multi-scale approach (second day)

Wiebke Neumann, Wildlife, Fish and Environmental Studies: Using mixed models to analyze autocorrelated data in nested design. – Examples from the analyses of moose GPS positions across species' latitudinal range (second day)

Johan Pihel, Landscape Architecture, Planning and Management: Mixed effect models in Eye tracking and visual assessment studies of forest landscapes  (second day)

Johannes Forkman, Crop Production Ecology: Randomized block trials with spatial correlation (second day)

Exercises:

Forest data:

• Description (first example in file)
• Data as SAS file
• Data as text file 
• R-script solution

Dogs data:

• Description (second example in file)
• Data as SAS file
• Data as text file
• R-script solution

Ladybirds data:

• Description (first example) 
• Data as SAS file
• Data as text file
• R-script solution

Bayisa data:

• Description (second example)
• Data as SAS file
• Data as text file

Workshop in generalised linear models  

Föreläsningar

Ulf Olsson: Generaliserade linjära modeller

Ulf Olsson/Jonas Oliva Palau: Pseudo-binomial data

Johannes Forkman: Overdispersion

Johannes Forkman: Overdispersion - code and references

Data sets for computer labs:

Exercise 1: Lichen data, logistic regression

Description

Data as Excel-file

Data as SAS-program code

SAS solutions

Exercise 2 and 3: Butterfly data, Poisson regression

Description

Data as Excel-file

Data as SAS-program code

SAS solutions - exercise 2

 SAS solutions - both exercises

Exercise 4: Sawfly data, logistic mixed model

Description

Data as Excel-file

Data as SAS-program code

 SAS solutions

Exercise 5: Cod data, zero-inflated poisson regression

Description

Data as .txt-file

Data as SAS-program code

SAS solutions

R solutions

Workshop in Mixed Models

Presentations:

Ulf Olsson: Mixed Models (first day)

Jan-Eric Englund: Does it matter if you use Mixed Models? (second day)

Johannes Forkman: Randomised block trials with spatial korrelation (second day)

Exercises:

Forest data:

• Description (first example in file)
• Data as SAS file
• Data as text file 
• SAS solution

Dogs data:

• Description (second example in file)
• Data as SAS file
• Data as text file
• SAS solution

Ladybirds data:

• Description
• Data as SAS file
• Data as text file
• Solutions in SAS

Bayisa data:

• Description and solution in SAS and R
• Data as SAS file
• Data as test file
• SAS code
• R code