2009 search results for "Map"

R is Hot: Part 4

October 28, 2010
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This is Part 4 of a five-part article series, with new parts published each Thursday. You can download the complete article from the Revolution Analytics website. High Quality Graphics, Made Easy R is especially useful for generating charts and graphics, quickly and easily. The ability to create visual plots of complex data is more than just a handy trick;...

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Where People Share Links About NYC

October 27, 2010
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Where People Share Links About NYC

Last week I participated in bit.ly’s fourth hackabit hack-a-thon, which is a wonderful opportunity for NYC area hackers to get together, eat pizza, drink energy drinks, and stay up late hacking with some of the best data geeks around. I was lucky enough to saddle up next to Hilary Mason, bit.ly’s lead scientist, recently named

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Bayesian Model Averaging (BMA) with uncertain Spatial Effects

October 27, 2010
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Bayesian Model Averaging (BMA) with uncertain Spatial Effects

This file illustrates the computer code to use spatial filtering in the context of Bayesian Model Averaging (BMA). For more details and in case you use the code please cite Crespo Cuaresma and Feldkircher (2010). In addition, this tutorial exists as ...

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Handling Large Datasets in R

Handling large dataset in R, especially CSV data, was briefly discussed before at Excellent free CSV splitter and Handling Large CSV Files in R. My file at that time was around 2GB with 30 million number of rows and 8 columns. Recently I started to collect and analyze US corporate bonds tick data from year...

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Algorithmic Trading with IBrokers

October 25, 2010
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Algorithmic Trading with IBrokers

Kyle Matoba is a Finance PhD student at the UCLA Anderson School of Management.  He gave a presentation on Algorithmic Trading with R and IBrokers at a recent meeting of the Los Angeles R User Group.  The discussion of IBrokers begins near th...

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What’s that 5km from the station “location”

October 21, 2010
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What’s that 5km from the station “location”

In our last installment we looked at stations which were pitch black. The case I examined, Middlesboro Kentucky illustrated 1. The station location data used by Hansen2010 has inaccuracies. 2. While the purported station location was pitch dark, nearby within a couple 1/100ths of a degree there were urban lights. What this example illustrated was

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Finding presence data for species distribution modelling (SDM)

October 20, 2010
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Finding presence data for species distribution modelling (SDM)

Getting presence data of species is often not easy and can be a major obstacle when attempting to model the distribution of species. One way is using the GBIF data base. Here I show one way how to obtain presence … Continue reading →

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Transactions, and Pondering their Use in Casinos

October 20, 2010
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Transactions, and Pondering their Use in Casinos

A couple of weeks ago, Bradford Cross of FlightCaster posted in Measuring Measures that transactions are the next big data category. I argue that they already are, and from reading his blog post, he seems to suggest this as well but I will admit that I think I missed his point. There are some clear examples of transactions and...

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Middlesboro Kentucky: Pitch Black?

October 19, 2010
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Middlesboro Kentucky: Pitch Black?

In his august draft of Hansen2010, Dr. Hansen makes the following claim: “We present evidence here that the urban warming has little effect on our standard global temperature analysis.  However, in the Appendix we carry out an even more rigorous test. We show there that there are a sufficient number of stations located in “pitch

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Listing gene IDs from hyperGTest

October 19, 2010
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hyperGTest compute Hypergeomtric p-values for over or under-representation of each GO term in the specified category among the specified gene set.*geneSample* was used as an example.Read More: 1329 Words Totally

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