Seminar series and workshops

Seminar serie: Environmental Statistics

August 27, Claudia von Brömssen, SLU: Statistical methods for evaluation of temporal trends in environmental data. Recorded seminar

September 10, James Weldon, SLU:  Change, stability and atmospheric pollutant effects in European forest vegetation (Defense of doctoral thesis)

September 17, Xin Zhao, SLU: Design-based sampling methods for environmental monitoring (Defense of doctoral thesis)

September 24, Arne Pommerening, SLU: Individual-based tree modelling for remote sensing data.

October 1, Jesper Rydén, SLU: Modelling extreme values: problems and concepts

October 15, Martin Sköld, Naturhistoriska riksmuseet, Disentangling effort and density in non-invasive genetic sampling by volunteers, the case of the Swedish Brown Bear monitoring programme

October 22, Annica de Groote, SLU: An introduction to sampling for natural resources

October 29, Anders Grimvall, Havsmiljöinstitutet and Linköpings university: How far can the evaluation of monitoring data be automated?

Seminar serie: The digital SLU

August 28: Dorota Anglart, SLU & DeLaval: Generalized additive model for dairy cow somatic cell count predictions using sensor data

September 4: Aakash Chawade, Dept of Plant Breeding, SLU: Challenges and opportunities for analysis of omics data

September 11: Mats Söderström & Kristin Piikki. Dept. of soil and environment: Spatial data for mapping of crop and soil characteristics: Digital soil mapping, modelling crop status from remote sensing data, data fusion, multi-scale modelling, sampling strategies, validation

September 18: Keni Ren, Umeå University: Zoom in on the precision livestock farming

September 25: Måns Thulin, consultant in statistics and AI: An introduction to statistical learning

October 2: Johanna Bergman, AI innovations of Sweden: AI Innovation of Sweden and SLU

October 9: Moudud Alam, Dalarna University: Monitoring reindeer activities in their natural environment

October 16, Johan Holmgren, Dept. for forest resource management, SLU: Forest remote sensing on the individual tree level 

October 23: Bo Stenberg & Johanna Wetterlind, Dept. of soil and environment: Spectral data from proximal sensors for analysis of soil properties: Instruments, data collection, data preparation, modelling, validation

Workshop in Machine Learning

The term machine learning hides a variety of different statistical methods. The basic aim of these methods is to recognize or disclose structures / patterns in data. What is often sold a bit like highly complex mathematics usually has simple ideas as a basis.

Some of the theoretical ideas behind machine learning will be presented. However, the focus is on the implementation of these methods in R and the interpretation of the results. In the first part of the workshop, we will deal with continuous response variables, whereas in the second part we will work with category data.

We would like to invite Ph.D. students and researchers to a two-part workshop in R machine learning given by Sven Adler. Part one on 4-5 november (13:00-16:00 and 9:00-12:00), part two on 14-15 december (13:00-16:00 and 9:00-12:00) in Umeå.

Link to Apply

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