Course Description
Many problems in ecology and evolutionary biology require understanding of the relationships among variables and examining their relative influences and responses. For example, over the last few decades ecologists have been trying to quantify the relative importance of top-down control by predation and herbivory vs. bottom-up control by nutrients and recruitment driving food web dynamics. Rather than arguing which of these forces are more important, we can examine the relative importance of each and how these forces interact to influence food web dynamics.
Material Covered (note: these will be revised before Sunday) Day 1 - Lectures: What is SEM? How can it be part of your research program? pdf SEM as a process: Creating multivariate causal models pdf Fitting piecewise models pdf Exercises: Creating causal conceptual models Piecewise model creation r file, data Readings: Grace 2010 (note: pdfs password protected, email me for info if you've lost it) Optional Reading: Matsueda 2012 (history) Day 2 - Lectures: Fitting Observed Variable models with covariance structures pdf What does it mean to evaluate a multivariate hypothesis?pdf ANCOVA revisited & Nonlinearities pdf Exercises: Fitting observed variable structural equation models in R New R Files and Data, model averaging script Readings: Grace and Bollen 2005, Shipley 2004 Optional Reading: Pearl 2009 Day 3 - Lecture: Multigroup models pdf Latent Variable models pdf Exercises: Multigroup analysis and the introduction of the latent variable R Files and Data Reading: Mancera et al. 2005 Optional Reading: Grace and Jutila 1999, Bollen and Pearl 2012 Day 4 - Lecture: Composite Variables pdf Revisiting piecewise approaches for nonlinear and hierarchical data pdf How to Fool Yourself with SEM (sensu Kline) pdf Exercises: Composites & Other Advanced Techniques R Files and Data Reading: Grace et al. 2010, Shipley 2009 [R appendix] Day 5 - Presentations and/or Open Consultation Optional Reading: Pearl 2012 Before the Class
Hello everyone! I'm looking forward to our upcoming journey into the wild world of Structural Equation Modeling together. Before the class begins, I want to make sure you all are prepared so that you can get the most out of it and to have you work through this preclass-excercise and tutorial.
- R - http://www.r-project.org/
- R Studio, a fantastic cross-platform interface for R - http://www.rstudio.org/
- The lavaan package for analysis of Structural Equation Models - http://www.lavaan.org
- Plyr - http://had.co.nz/plyr
- Ggplot2 - http://had.co.nz/ggplot2
Additional Useful Links Reading Bollen, K.A. and Pearl, J. (2012) Eight myths about causality and Structural Equation Models. In Handbook of Structural Equation Modeling. R. Hoyle, ed. [pdf] Byrnes, J.E., Reed, D.C., Cardinale, B.J., Cavanaugh, K.C., Holbrook, S.J. & Schmitt, R.J. (2011). Climate driven increases in storm frequency simplify kelp forest food webs. Global Change Biology, 17: 2513-2524.[pdf] Grace, J.B. (2010) Structural Equation Modeling for Observational Studies. Journal of Wildlife Management, 72:14-22 [pdf] Grace J.B., Anderson TM, Olff H, Scheiner SM (2010) On the specification of structural equation models for ecological systems. Ecological Monographs, 80, 67-87. [pdf] Grace J.B., Bollen KA (2005) Interpreting the Results from Multiple Regression and Structural Equation Models. Bulletin of the Ecological Society of America, 86, 283-295.[pdf] Grace, J.B. & Jutila, H. (1999). The relationship between species density and community biomass in grazed and ungrazed coastal meadows. Oikos, 398-408. [pdf] Mancera, J.E., Meche, G.C., Cardona-Olarte, P.P., CastaĆ±eda-Moya, E., Chiasson, R.L., Geddes, N.A., et al. (2005). Fine-scale spatial variation in plant species richness and its relationship to environmental conditions in coastal marshlands. Plant Ecol., 178, 39-50. [pdf] Matsueda, R.L. (2012) Key advances in the history of structural equation modeling. In Handbook of Structural Equation Modeling. R. Hoyle, ed. [pdf] Pearl, J. (2009) Defending the causal interpretation of SEM. In Causality, 2nd Ed. [pdf] Pearl, J. (2012) The causal foundations of Structural Equation Modeling. In Handbook of Structural Equation Modeling. R. Hoyle, ed. [pdf] Shipley, B. (2004) Analysing the allometry of multiple interacting traits. Perspect Plant Ecol, 6, 235-241. [pdf] Shipley, B. (2009) Confirmatory path analysis in a generalized multilevel context. Ecology, 90: 363-368.[pdf] [R appendix] See also the pdfs in this folder for more topic-specific readings. Useful Books James B. Grace. 2006. Structural Equation Modeling and Natural Systems. Cambridge University Press. [amazon] Ken A. Bollen. 1989. Structural Equations with Latent Variables. Wiley Press.[amazon] Rex B. Kline. 2010. Principles and Practice of Structural Equation Modeling. The Guilford Press. [amazon] Bill Shipley. 2000. Cause and Correlation in Biology. Cambridge University Press. [amazon] Rick H. Holyle, ed. 2012. Handbook of Structural Equation Modeling. The Guilford Press. [amazon] |