A practical guide to machine learning in ecology

Recently, I was exploring techniques to interpolate some missing environmental data, and stumbled across something called ‘random forest’ analysis. Random what now? I did a little digging and came across the massive and insanely complicated field of machine learning. I couldn’t find a concise guide to machine learning techniques, or when I might want to use one or the other, so I thought I would cobble together a brief guide on my own. Below is a rough stab at explaining and exploring different machine learning techniques, from CARTs to GBMs, using R.

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Piecewise structural equation modeling in ecological research

[Updated December 30, 2019: You can read more about the package, new functionality, and other approaches to SEM in my online book (work-in-progress): https://jslefche.github.io/sem_book/]

[Updated October 13, 2015: Active development has moved to Github, so please see the link for the latest versions of all functions: https://github.com/jslefche/piecewiseSEM/]

Nature is complex. This seems like an obvious statement, but too often we reduce it to straightforward models. y ~ x and that sort of thing. Not that there’s anything wrong with that: sometimes y is actually directly a function of x and anything else would be, in the words of Brian McGill, ‘statistical machismo.’

But I would wager that, more often that not, y is not directly a function of x . Rather, y may be affected by a host of direct and indirect factors, which themselves affect one another directly and indirectly. If only there was someway to translate this network of interacting factors into a statistical framework to better and more realistically understand nature. Oh wait, structural equation modeling.

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