Monthly Archives: July 2012

Edge Prediction in a Social Graph: My Solution to Facebook’s User Recommendation Contest on Kaggle

July 30, 2012
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Edge Prediction in a Social Graph: My Solution to Facebook’s User Recommendation Contest on Kaggle

A couple weeks ago, Facebook launched a link prediction contest on Kaggle, with the goal of recommending missing edges in a social graph. I love investigating social networks, so I dug around a little, and since I did well enough to score one of the coveted prizes, I’ll share my approach here. (For some background, the...

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Split-plot 2: let’s throw in some spatial effects

July 30, 2012
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Split-plot 2: let’s throw in some spatial effects

Disappeared for a while collecting frequent flyer points. In the process I ‘discovered’ that I live in the middle of nowhere, as it took me 36 hours to reach my conference destination (Estoril, Portugal) through Christchurch, Sydney, Bangkok, Dubai, Madrid … Continue reading →

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Big data, big analytics, big opportunity

July 30, 2012
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Big data, big analytics, big opportunity

Data, data, every where Nor any byte to think The world today is awash with data. Corporations, governments, and individuals are busy generating petabytes of data on culture, economy, environment, religion, and society.  While data has become abundant and ubiquitous, data analysts needed to turn raw data into knowledge are in fact in short...

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Forecasting the Olympics

July 30, 2012
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Forecasting sporting events is a growing research area. The International Journal of Forecasting even had a special issue on sports forecasting a couple of years ago. The London 2012 Olympics has attracted a few forecasters trying to predict medal counts, world records, etc. Here are some of the articles I’ve seen. Which Olympic records get shattered?, Nate Silver, New...

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A prediction for the Olympic men’s 100m sprint

July 30, 2012
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A prediction for the Olympic men’s 100m sprint

R user Markus Gesmann used the gold-winning times from the Olympic Men's 100m sprint since 1990 as the basis of the following prediction for the London Games: My simple log-linear model forecasts a winning time of 9.68 seconds, which is 1/100 of a second faster than Usain Bolt's winning time in Beijing in 2008, but still 1/10 of a...

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Archetypal Analysis

July 30, 2012
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Archetypal Analysis

Thinking Strategically about Customer HeterogeneityIronically, market segmentation, whose motto is "one size does not fit all," seems to rely almost exclusively on one definition of what constitutes a segment.  Borrowing its definition f...

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Machine learning for better homicide counts in Ciudad Juarez

July 30, 2012
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Machine learning for better homicide counts in Ciudad Juarez

Photo Credit: Jesús Villaseca Pérez Ever since March 2008 Ciudad Juárez began to register an alarming number of homicides becoming Mexico's most violent city. According to the Mexican vital statistics system Ciudad Juárez (coterminous with the Juárez municipality) went from having just 202 murders in 2007 to 1,616 in 2008, 2,397 in...

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Blue Jay and Scrub Jay : Using rvertnet to check the distributions in R

July 30, 2012
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Blue Jay and Scrub Jay : Using rvertnet to check the distributions in R

As part of my Google Summer of Code, I am also working on another package for R called rvertnet. This package is a wrapper in R for VertNet websites. Vertnet is a vertebrate distributed database network consisting of FishNet2, MaNIS, HerpNET, and ORNIS. Out of that currently Fishnet, HerpNET and ORNIS have their v2 portals serving data. rvertnet has functions now to access

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Blue Jay and Scrub Jay : Using rvertnet to check the distributions in R

July 30, 2012
By
Blue Jay and Scrub Jay : Using rvertnet to check the distributions in R

As part of my Google Summer of Code, I am also working on another package for R called rvertnet. This package is a wrapper in R for VertNet websites. Vertnet is a vertebrate distributed database network consisting of FishNet2, MaNIS, HerpNET, and ORNIS. Out of that currently Fishnet, HerpNET and ORNIS have their v2 portals serving data. rvertnet has functions now to access

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Returns with negative net asset values

July 30, 2012
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Returns with negative net asset values

How are returns calculated when net asset value goes negative? Previously In “A tale of two returns” we highlighted the similarities and differences of log returns versus simple returns. Positive valuation We create — in R — an example of net asset value at four times: > nav1 <- c(1000, 900, 950, 1010) > nav1 … Continue reading...

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