2020 Advanced Statistics Workshop Open for Application
Title: 2020 Advanced Statistics Workshop， XTBG
Venue: XTBG Environmental Education Center Meeting room 218 (tentative)
Dates: 23 – 28 March 2020 (6 days)
Organiser: Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences
Registration: Registration is open until 10 March 2020
Sponsor: Environmental Education Centre，Xishuangbanna Tropical Botanical Garden, CAS
Fee: 1200 RMB/person (including lecture room, materials, allowance for instructor and other staffs; not including transportation between your organization and XTBG, hotel and food during the workshop etc.)
About: This advanced workshop aims at introducing students to linear modelling methods that are commonly used in Ecology today. The workshop consists of a series of modules that build on each other towards more complex linear models, starting from simple linear models, through generalised linear models, to mixed effects models and ending with linear models with generalised least squares and phylogenetically correlated data. We expect participants should be competent at using these methods after the workshop and understanding when they are appropriate to use.
Workshop outline (tentative):
Module 1: Revision of linear models and generalised linear models
Go over the lm() and glm() functions, discuss assumptions of the models, run diagnostics, and make predictions with the models.
Module 2: Dealing with grouped data: Linear mixed models
Go over the concepts of grouped data and how to deal with it using linear mixed models (LMM).
Module 3: Dealing with grouped data: generalized linear mixed models
Extend the LMM analysis to the GLMM case. Main goal here is to imprint the differences between lm, glm, lmm and glmm.
Module 4: Dealing with non-independent data that cannot be grouped: generalized least squares
Deal with non-independence between data points when the data cannot be easily grouped. Also cover the problem using spatial-dependence between data points.
Module 5: Dealing with phylogenetically correlated data
Comparative data analyses routinely correct for autocorrelation among species’ data caused by shared evolutionary history between the species. Common methods to deal with this problem and some of the considerations when dealing with autocorrelation will be covered.
Module 6: Exercise and revision
Assuming timeous completion of the previous modules, on the final day applicants will be given several problems for which they will need to figure out appropriate analyses to deal with the questions and data provided.
Requirements for applicants: 1). Experience with conducting analyses in R; 2). Familiar with linear models and generalised linear models.
Instructor: Professor Kyle Tomlinson, PI of Community Ecology and Conservation Group of XTBG, working on landscape conservation, forest ecology, savanna ecology, and functional trait diversity. Kyle is a very good and experienced statistic instructor and has been invited to run statistic workshops during ATBC-Asia chapter annual meetings since 2014, and during Advanced Fieldcourse in Ecology and Conservaton (XTBG’s annual international training program) since 2013.