Blog Archives

(Another) introduction to R

May 27, 2013
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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.

Integration take two – Shiny application

May 13, 2013
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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) is

Poor man’s integration – a simulated visualization approach

April 29, 2013
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$Poor man’s integration – a simulated visualization approach$

Every once in a while I encounter a problem that requires the use of calculus. This can be quite bothersome since my brain has refused over the years to retain any useful information related to calculus. Most of my formal training in the dark arts was completed in high school and has not been covered

How long is the average dissertation?

April 15, 2013
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The best part about writing a dissertation is finding clever ways to procrastinate. The motivation for this blog comes from one of the more creative ways I’ve found to keep myself from writing. I’ve posted about data mining in the past and this post follows up on those ideas using a topic that is relevant

A nifty line plot to visualize multivariate time series

April 1, 2013
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A few days ago a colleague came to me for advice on the interpretation of some data. The dataset was large and included measurements for twenty-six species at several site-year-plot combinations. A substantial amount of effort had clearly been made to ensure every species at every site over several years was documented. I don’t pretend

Animating neural networks from the nnet package

March 19, 2013
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My research has allowed me to implement techniques for visualizing multivariate models in R and I wanted to share some additional techniques I’ve developed, in addition to my previous post. For example, I think a primary obstacle towards developing a useful neural network model is an under-appreciation of the effects model parameters have on model

Visualizing neural networks from the nnet package

March 4, 2013
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Neural networks have received a lot of attention for their abilities to ‘learn’ relationships among variables. They represent an innovative technique for model fitting that doesn’t rely on conventional assumptions necessary for standard models and they can also quite effectively handle multivariate response data. A neural network model is very similar to a non-linear regression

Data fishing: R and XML part 3

February 18, 2013
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I’ve recently posted two blogs about gathering data from web pages using functions in R. Both examples showed how we can create our own custom functions to gather data about Minnesota lakes from the Lakefinder website. The first post was an example showing the use of R to create our own custom functions to get

Collinearity and stepwise VIF selection

February 5, 2013
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$Collinearity and stepwise VIF selection$

Collinearity, or excessive correlation among explanatory variables, can complicate or prevent the identification of an optimal set of explanatory variables for a statistical model. For example, forward or backward selection of variables could produce inconsistent results, variance partitioning analyses may be unable to identify unique sources of variation, or parameter estimates may include substantial amounts

Data fishing: R and XML part 2

January 21, 2013
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I’m constantly amazed at what can be done using free software, such as R, and more importantly, what can be done with data that are available on the internet. In an earlier post, I confessed to my sedentary lifestyle immersed in code, so my opinion regarding the utility of open-source software is perhaps biased. None