[R-sig-Epi] Course on Structural Equation Modelling (SEM) for Biologists - Jarret Byrnes and Jon Lefcheck - October 2017

Wed Jun 21 14:49:58 CEST 2017

Although not directly related someone suggest I post this course the 
r-sig-epi list

"Structural Equation Modelling for Ecologists and Evolutionary 
Biologists”

Delivered by Dr. Jarret Byrnes and Dr. Jon Lefcheck

www.prstatistics.com/course/structural-equation-modelling-for-ecologists-and-evolutionary-biologists-semr01/

This course will run from 30th October – 3rd November 2017 at Margam 
Park
Discovery Centre, Wales (please ignore the dates in the last post saying
30th November, should of read 3rd of November).

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

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-- 
Oliver Hooker PhD.
PR statistics

2017 publications -

Ecosystem size predicts eco-morphological variability in post-glacial 
diversification. Ecology and Evolution. In press.

The physiological costs of prey switching reinforce foraging 
specialization. Journal of animal ecology.

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