Dynamic models in R

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Dynamic Models in R:

Programming, parameter estimation and model selection

8, 14, 15, 21, 22 March and 12 April 2019

Scope

Picture.pngEcological modelling, based on field data, has become an indispensable tool in ecological research. It consists of a number of steps: analysing data,  proposing plausible mechanistic models and mechanistic explanations for observed phenomena, and selecting models based on maximum likelihood or an information criterion. This course presents a conceptual framework for ecological modelling: covering elementary growth models and probability distributions needed to mathematically model processes.  The models are confronted with the data, using state of the art statistical methods. The course presents techniques for dynamic simulation, model fitting, parameter estimation, and model selection based on maximum likelihood and information theory. While the theory has emerged from the field of ecology, it has shown to be widely applicable in the life sciences. The course is taught with R as the programming language because it is freeware and it allows flexibility in handling and modelling data.

Teaching method

The conceptual framework is taught from the book “Ecological Models and Data in R” by Ben Bolker, Princeton University Press, 2008. Examples are drawn from the field of (agro-)ecology. The course consists of theory lectures, hands-on practical sessions and a case study to synthesize the acquired skills and knowledge. The case study is presented and discussed on the last day of the course.

Learning outcomes

Picturen average.pngAfter completing the course, the participant:

  1. is able to program a discrete time dynamic model in R
  2. is able to program a continuous time dynamic model in R
  3. is able to add stochastic variation to a dynamic model in R
  4. is able to choose appropriate probability models to represent biological variation
  5. understands the concept of maximum likelihood
  6. is able to program a maximum likelihood estimation for a single parameter in R
  7. understands conceptually the difference between process error and observation error
  8. understands the principles of different methods of parameter estimation of ecological models
  9. is able to program a parameter search algorithm for ecological model calibration in R
  10. understand and apply the principles of model selection to select the best model
  11. is able to fit and select an ecological model to an actual problem in R
Pre-requisites:
  • Basic knowledge of R (e.g. installing packages, reading data-files, linear regression. See PE&RC course “Introduction to R for statistical analysis”)
  • Basic knowledge on statistics and modelling (e.g. PE&RC courses: “Basic statistics” or ”linear models”, ”The art of modelling”)
Programme

Day 1: Dynamic simulation in R

  • Simple growth models
  • Making dynamic loops in R
  • Simple population models (discrete and continuous time)

Day 2: Stochastic simulation

  • Probability distributions for stochastic processes (Chapter 4 of Bolker, 2008)
  • Stochastic simulations (e.g. exercises Zuur et al. 2009 and Chapter 5.2 of Bolker, 2008)

Day 3: Fitting an ecological model with maximum likelihood

  • Probability distributions and maximum likelihood
  • Fitting an ecological model with maximum likelihood (Chapter 6 of Bolker, 2008)
  • Short description of case studies

Day 4: Parameter optimization and model selection

  • Methods for parameter estimation (Chapters 6.6 and 7 of Bolker, 2008)
    • Newton-Raphson
    • Grid search
    • Simplex (Nelder-Mead)
    • Price algorithm
  • Model selection
  • Confidence intervals

Day 5: Case studies

  • Find or generate a simple data set
  • Postulate 3 candidate models, including a deterministic and stochastic component (very simple models!)
  • Fit the three models to the data
  • Select the best model using AIC

Day 6: Case study presentations

Literature
  • Bolker BM (2008) Ecological Models and Data in R. Princeton University Press, 396 pp.
  • Zuur AF, Ieno EN, Walter NJ, Saveliev AA & Smith Gm (2009) Mixed Effects Models and Extensions in Ecology with R. Springer, 574 pp.
  • Bahlai CA, van der Werf W, O’Neal MW, Hemerik L, Landis DA (2015) Shifts in dynamic regime of an invasive ladybeetle are linked to the invasion and insecticidal management of its prey. Ecological Applications 25(7), 1807–1818. https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1890/14-2022.1
General information
Target Group The course is aimed at PhD candidates and other academics.
Group Size Min. 20, max. 30 participants
Course duration 6 days
Language of instruction English
Frequency of recurrence Once a year, or once every two years
Number of credits 1.8 ECTS
Lecturers Dr.ir. Bob Douma (Centre for Crops System Analysis, Wageningen University & Research), Dr. Lia Hemerik (Biometris, Wageningen University & Research) and Dr.ir. Wopke van der Werf (Centre for Crops Systems Analysis, Wageningen University & Research)
Prior knowledge Basic knowledge of R (e.g. installing packages, reading data-files, linear regression. See PE&RC course “Introduction to R for statistical analysis”)
Basic knowledge on statistics and modelling (e.g. PE&RC courses: “Basic statistics” or ”Linear models”, ”The art of modelling”)
Location Wageningen University Campus
Options for accommodation Accommodation is not included in the fee of the course, but there are several possibilities in Wageningen. For information on B&B's and hotels in Wageningen please visit proefwageningen.nl. Another option is Short Stay Wageningen. Furthermore Airbnb offers several rooms in the area. Note that besides the restaurants in Wageningen, there are also options to have dinner at Wageningen Campus.

 

Fees 1
  EARLY-BIRD FEE 2 REGULAR FEE 2
PE&RC  / SENSE PhD candidates with an approved TSP € 350,- € 400,-
All other PhD candidates
Postdocs and staff of the above mentioned Graduate Schools
€ 750,- € 800,-
All others € 1.100,- € 1.150,-

1The course fee includes a reader, coffee/tea, and lunches. It does not include accommodation (NB: options for accommodation are given above)
2The Early-Bird Fee applies to anyone who REGISTERS ON OR BEFORE 8 FEBRUARY 2019

Note:

  • If you need an invoice to complete your payment, please send an email to office.pe@wur.nl, including ALL relevant details that should be mentioned on the invoice (e.g., purchase order no., specific addresses, attendees, etc.).
  • The Early-Bird policy is such that the moment of REGISTRATION (and not payment) is leading for determining the fee that applies to you.
  • Please make sure that your payment is arranged within two weeks after your registration.
  • It is the participant's responsibility to make sure that he/she (or his/her secretary) completes the payment correctly and in time.
 
PE&RC Cancellation Conditions
  • Up to 4 (four) weeks prior to the start of the course, cancellation is free of charge.
  • Up to 2 (two) weeks prior to the start of the course, a fee of € 350,- will be charged.
  • In case of cancellation within two weeks prior to the start of the course, a fee of € 750,- will be charged.
  • If you do not show at all, a fee of € 1.100,- will nevertheless be charged.

Note: If you would like to cancel your registration, ALWAYS inform us (and do note that you will be kept to the cancellation conditions)

More information

Dr. Claudius van de Vijver (PE&RC)
Phone: +31 (0) 317 485116
Email: claudius.vandevijver@wur.nl

Registration

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