Publications by beckmw
Integration take two – Shiny application
My last post discussed a technique for integrating functions in R using a Monte Carlo or randomization approach. The mc.int function (available here) estimated the area underneath a curve by multiplying the proportion of random points below the curve by the total area covered by points within the interval: The estimated integration (bottom plot...
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(Another) introduction to R
It’s Memorial Day and my dissertation defense is tomorrow. This week I’m phoning in my blog. I had the opportunity to teach a short course last week that was part of a larger workshop focused on ecosystem restoration. A fellow grad student and I taught a session on Excel and R for basic data analysis. I insisted on teaching the R portion...
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Variable importance in neural networks
If you’re a regular reader of my blog you’ll know that I’ve spent some time dabbling with neural networks. As I explained here, I’ve used neural networks in my own research to develop inference into causation. Neural networks fall under two general categories that describe their intended use. Supervised neural networks (e.g., multilaye...
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A nifty area plot (or a bootleg of a ggplot geom)
The ideas for most of my blogs usually come from half-baked attempts to create some neat or useful feature that hasn’t been implemented in R. These ideas might come from some analysis I’ve used in my own research or from some other creation meant to save time. More often than not, my blogs are motivated by data visualization techniques mean...
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Sensitivity analysis for neural networks
I’ve made quite a few blog posts about neural networks and some of the diagnostic tools that can be used to ‘demystify’ the information contained in these models. Frankly, I’m kind of sick of writing about neural networks but I wanted to share one last tool I’ve implemented in R. I’m a strong believer that supervised neural networks...
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Visualizing neural networks in R – update
In my last post I said I wasn’t going to write anymore about neural networks (i.e., multilayer feedforward perceptron, supervised ANN, etc.). That was a lie. I’ve received several requests to update the neural network plotting function described in the original post. As previously explained, R does not provide a lot of options for visualiz...
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A brief foray into parallel processing with R
I’ve recently been dabbling with parallel processing in R and have found the foreach package to be a useful approach to increasing efficiency of loops. To date, I haven’t had much of a need for these tools but I’ve started working with large datasets that can be cumbersome to manage. My first introduction to parallel processing was somewh...
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Brief introduction on Sweave and Knitr for reproducible research
A few weeks ago I gave a presentation on using Sweave and Knitr under the guise of promoting reproducible research. I humbly offer this presentation to the blog with full knowledge that there are already loads of tutorials available online. This presentation is specific and slightly biased towards Windows OS, so it probably has limited relevan...
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Process and observation uncertainty explained with R
Once up on a time I had grand ambitions of writing blog posts outlining all of the examples in the Ecological Detective.1 A few years ago I participated in a graduate seminar series where we went through many of the examples in this book. I am not a population biologist by trade but many of the concepts were useful for not only helping me bette...
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How much code have you written?
This past week I attended the National Water Quality Monitoring Conference in Cincinnati. Aside from spending my time attending talks, workshops, and meeting like-minded individuals, I spent an unhealthy amount of time in the hotel bar working on this blog post. My past experiences mixing coding and beer have suggested the two don’t mix, but ...
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