[R-sig-eco] 'STATS COURSE' - Structural Equation Modelling for Ecologists and Evolutionary Biologists (SEM) Jarret Byrnes and Jon Lefcheck
Oliver Hooker
oliverhooker at prstatistics.com
Thu Feb 16 16:09:26 CET 2017
"Structural Equation Modelling for Ecologists and Evolutionary
Biologists”
Delivered by Dr. Jarret Byrnes and Dr. Jon Lefcheck
http://www.prstatistics.com/course/structural-equation-modelling-for-ecologists-and-evolutionary-biologists-semr01/
This course will run from 30th October – 30th November 2017 at Margam
Park Discovery Centre, Wales.
Course only and all inclusive packages are available.
The course is a primer on structural equation modelling (SEM) and
confirmatory path analysis, with an emphasis on practical skills and
applications to real-world data.
Structural Equation Modelling (SEM) is a rapidly growing technique in
ecology and evolution that unites multiple hypotheses in a single causal
network. It provides an intuitive graphical representation of
relationships among variables, underpinned by well-described
mathematical estimation procedures. Several advances in SEM over the
past few years have expanded its utility for typical ecological
datasets, which include count data, missing observations, and nested or
hierarchical designs.
We will cover the basic philosophy behind SEM, provide approachable
mathematical explanations of the techniques, and cover recent extensions
to mixed effects models and non-normal distributions. Along the way, we
will work through many examples from the primary literature using the
open-source statistical software R (www.r-project.org). We will draw on
two popular R packages for conducting SEM, including lavaan and
piecewiseSEM.
Participants are encouraged to bring their own data, as there will be
opportunities throughout the course to plan, analyse, and receive
feedback on structural equation models.
Course content is as follows
Day 1
Introduction to SEM
Module 1: What is Structural Equation Modeling? Why would I use it?
Module 2: Creating multivariate causal models
Module 3: Fitting piecewise models
Readings: Grace 2010 (overview), Whalen et al. 2013 (example)
Day 2
SEM Using Likelihood
Module 4: Fitting Observed Variable models with covariance structures
Module 5: What does it mean to evaluate a multivariate hypothesis?
Module 6: Latent Variable models
Module 7: ANCOVA revisited & Nonlinearities
Readings: Grace & Bollen 2005, Shipley 2004
Optional Reading: Pearl 2012, Pearl 2009 (causality)
Day 3
Piecewise SEM
Module 8: Introduction to piecewise approach
Module 9: Incorporation of random effects models
Model 10: Autocorrelation
Reading: Shipley 2009; Lefcheck 2016
Day 4
Advanced Topics with Likelihood and Piecewise SEM
Module 11: Multigroup models and non-linearities
Module 12: Composite Variables
Module 13: Phylogenetically-correlated data
Module 14: Prediction using SEM
Module 15: How To Reject A Paper That Uses SEM
Readings: Grace & Julia 1999, von Hardenberg & Gonzalez‐Voyer 2013
Day 5
Open Lab and Final Presentations
Please email any inquiries to oliverhooker at prstatistics.com or visit our
website www.prstatistics.com
Please feel free to distribute this material anywhere you feel is
suitable
Our other courses
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28th Feb – 3rd Mar 2017, Scotland, Dr. Andrew Parnell, Dr. Andrew
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Oliver Hooker PhD.
PR statistics
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