schedule.Rmd

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title: "Course Schedule"
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The general schedule for the course is below. Please note, for readings the u/p is semclass for both. For code, please see [this dropbox](https://www.dropbox.com/sh/objbunt4wxdlnxb/AAB38yRdxi62BlMC1ca_SYPJa?dl=0) for a static view. During the course itself, I'll give you share access so that you can update files as things change. Please [run this script](./files/scripts/Install_Required_Packages.R) in R to install the packages you will need for the course before day 1.  
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For the course, we'll also use a number of common data sets which you can download (and put into a data folder) [here](files/data.zip).   
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## Day 1. Introduction to SEM
__Overview:__  We discuss just what is SEM. Along the way we'll discuss it's origins, give a general example of moving from a traditional ANOVA-esque framework, and finally discuss how to build a well-justified causal model. Causality will be central, and we won't pull any punches! We'll end the day by starting to talk about how to fit SEMs using likelihood using covariance-based estimation with likelihood.  
__Lectures:__  [What is SEM? A Practical and Historical Overview](lecture_pdfs/SEM_Intro_And_History.pdf)  
	            
	[Anatomy of SEM](lecture_pdfs/Anatomy_of_SEM.pdf)  
	            
	[Building Multivariate Causal Models](lecture_pdfs/Multivariate_Causal_Models.pdf), [code](files/scripts/multivariate_causal_models.R)  
__Readings:__  [Grace 2010](http://byrneslab.net/classes/sem_pdfs/General/Grace%20et%20al%202010%20Ecological%20Monographs.pdf) (overview), [Whalen et al. 2013](http://byrneslab.net/classes/sem_pdfs/examples/Whalen_et_al_2013_Ecology.pdf) (example) (note: semclass/semclass for pdfs)  
__Optional Reading:__ [Matsueda 2012](http://byrneslab.net/classes/sem_pdfs/General/Matsueda%202012%20history.pdf) (history), [Pearl 2012a](http://byrneslab.net/classes/sem_pdfs/causality/pearl-causality-review.pdf) (history of causality)
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## Day 2. Covariance Based SEM  
__Overview:__  We'll pick up where we left off discussing covariance-based estimation. We'll spend time discussing how we evaluate an SEM and what we report. We'll end the day exploring the concept of latent variables - variables for which we do not actually have a measured variable, but for which we have one or more indicators.  
__Lectures:__  	[Engines of SEM: Covariance-Based Estimation](lecture_pdfs/Intro_to_Likelihood_Fitting.pdf), [code](files/scripts/Likelihood_Fitting.R)    
	            [What does it mean to evaluate a multivariate hypothesis?](lecture_pdfs/Assesing_Likelihood_Fits.pdf), [code](files/scripts/Assessing_Likelihood.R), [fitted_lavaan.R](files/scripts/fitted_lavaan.R)  
	            [Latent Variable models](lecture_pdfs/Latent_Variables.pdf), [code](files/scripts/Latent_variables.R)  
__Readings:__  [Grace and Bollen 2005](http://byrneslab.net/classes/sem_pdfs/General/Grace & Bollen 2005 Bull Ecol Soc Amer.pdf)
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## Day 3. Piecewise SEM  
__Overview:__  We move beyond covariance based techniques into local estimation techniques that embrace the graph theoretic approach to SEM via the `piecewiseSEM` package by Jon Lefcheck.  
__Lectures:__  	[Engines of SEM: Local Estimation with piecewiseSEM](lecture_pdfs/Local_Estimation.pdf), [code](files/scripts/Local_Estimation.R)      
	            [A Nonlinear, Non-normal world](lecture_pdfs/Nonlinear_nonnormal.pdf), [code](files/scripts/nonlinear_nonnormal.R)      
	            [Categorical Variables and Multigroup Analysis](lecture_pdfs/Categorical_Variables.pdf), [code](files/scripts/Categorical_Vars.R), [multigroup_margins.R](files/scripts/multigroup_margins.R)  
	            [Mixed Models in SEM](lecture_pdfs/Mixed_Models.pdf), [code](files/scripts/Mixed_Models.R)    
__Readings:__  [piecewiseSEM vignette](http://jslefche.github.io/piecewiseSEM/articles/piecewiseSEM.html), [Shipley 2009](http://byrneslab.net//classes/sem_pdfs/d-sep/Shipley%202009%20dsep%20with%20multilevel%20models.pdf), [Lefcheck 2016](http://byrneslab.net//classes/sem_pdfs/d-sep/Lefcheck_2016_piecewiseSEM.pdf)
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## Day 4. Special Topics in SEM  
__Overview:__  This day varies quite a bit, depending on the current class. It's a potpurri of more advances topics, not all of which will be covered.  
__Lectures:__  [Engines of SEM: Bayesian SEM](lecture_pdfs/bayesian_sem.pdf), [code](files/scripts/bayesian_sem.R)  
	            [Spatial Data in SEM](lecture_pdfs/Spatial_Autocorrelation.pdf), [code](files/scripts/Spatial_autocorrelation.R), [helper functions](files/scripts/residuals.R)   
	            [Timseries and Temporal Autocorrelation in SEM](lecture_pdfs/Temporal_Autocorrelation.pdf), [code](files/scripts/Temporal_Autocorrelation.R)   
	            [Composite Variables ](lecture_pdfs/Composite_Variables.pdf), [code](files/scripts/Composites.R)   
	            Whole-System Prediction with SEM  
__Readings:__   [brms and SEM](https://rpubs.com/jebyrnes/brms_bayes_sem). [causal model structure and random effects](https://rpubs.com/jebyrnes/causal_mods)
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## Day 5. Open Lab and Presentations  
__Overview:__  In the morning, we'll have an open lab. Students will work on their own data and projects with an aim to building a 2-3 slide powerpoint presentation detailing 1. The problem/system, 2. The model they built, and 3. The Final result and any challenges. We'll present these in the afternoon, hopefully in a convivial atmosphere, after a brief primer on warming up for a talk you are nervous about!  
__Lectures:__  How to fool yourself with SEM!
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