3175 search results for "GIS"

The Bayesian Counterpart of Pearson’s Correlation Test

August 19, 2013
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The Bayesian Counterpart of Pearson’s Correlation Test

Except for maybe the t test, a contender for the title “most used and abused statistical test” is Pearson’s correlation test. Whenever someone wants to check if two variables relate somehow it is a safe bet (at least in psychology) that the first thing to be tested is the strength of a Pearson’s correlation. Only if that doesn’t...

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Is the Tax Code the longest Title?

August 19, 2013
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Is the Tax Code the longest Title?

  Last week, I shared that Dan Katz and I had finally published a draft of our paper, Measuring the Complexity of the Law: The U.S. Code.  We’d previewed this research on Computational Legal Studies years ago.  Since then, we’ve received great… Read more ›

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Mapping Australian electoral divisions with ggplot2

August 18, 2013
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Mapping Australian electoral divisions with ggplot2

I’ve seen some creative visualisations of issues surrounding the Australian election recently though not as many maps as I expected. ‘ggplot2′ is the go-to package for plotting in R so I thought I’d see if I could plot the Australian electoral divisions with ggplot2. By using the Australian Electoral Commission’s GIS mapping coordinates and mutilating

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Working with climate data from the web in R

August 17, 2013
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Working with climate data from the web in R

I recently attended ScienceOnline Climate, a conference in Washington, D.C. at AAAS. You may have heard of the ScienceOnline annual meeting in North Carolina - this was one of their topical meetings focused on Climate Change. I moderated a session on working with data from the web in R, focusing on climate data. Search Twitter for...

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Working with climate data from the web in R

August 17, 2013
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Working with climate data from the web in R

I recently attended ScienceOnline Climate, a conference in Washington, D.C. at AAAS. You may have heard of the ScienceOnline annual meeting in North Carolina - this was one of their topical meetings focused on Climate Change. I moderated a session on working with data from the web in R, focusing on climate data. Search Twitter for...

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Anomaly Detection Using The Adobe Analytics API

August 15, 2013
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Anomaly Detection Using The Adobe Analytics API

As digital marketers & analysts, we’re often asked to quantify when a metric goes beyond just random variation and becomes an actual “unexpected” result. In cases such as A/B..N testing, it’s easy to calculate a t-test to quantify the difference between two testing populations, but for time-series metrics, using a t-test is likely not appropriate. Anomaly Detection Using...

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Golf Scramble Simulation in R

August 14, 2013
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Golf Scramble Simulation in R

Golf Scramble Simulation Golf Scramble SimulationThis is a simulation of a standard best-ball golf scramble. Conventional wisdom has it that the best golfer (A) should hit last, the idea being that one of the lesser golfers may have a decent shot already so the...

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predictNLS (Part 1, Monte Carlo simulation): confidence intervals for ‘nls’ models

August 14, 2013
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predictNLS (Part 1, Monte Carlo simulation): confidence intervals for ‘nls’ models

Those that do a lot of nonlinear fitting with the nls function may have noticed that predict.nls does not have a way to calculate a confidence interval for the fitted value. Using confint you can obtain the error of the fit parameters, but how about the error in fitted values? ?predict.nls says: “At present se.fit

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Free R Graphics Workshop, Copenhagen, Denmark, 26th August

August 13, 2013
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Mango Solutions are pleased to announce a free R Graphics Workshop in Copenhagen on Monday 26th August (6-8pm). The workshop is open to all and any interested R users or those wishing to learn more about R.   The workshop will focus on using R to create powerful graphics, specifically covering: •             An introduction to R •             Getting data into...

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Variable importance in neural networks

August 12, 2013
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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., multilayer feed-forward networks)

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