Monthly Archives: September 2013

Sentiment Analysis on Twitter with Datumbox API

September 9, 2013
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Sentiment Analysis on Twitter with Datumbox API

Hey there! After my post about sentiment analysis using the Viralheat API I found another service. Datumbox ist offering special sentiment analysis for Twitter. But this API doesn´t just offer sentiment analysis, it offers a much more detailed analysis. „The currently supported API functions are: Sentiment Analysis, Twitter Sentiment Analysis, Subjectivity Analysis, Topic Classification, Spam …

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First Derivative of the Multivariate Normal Densities with RcppArmadillo

September 9, 2013
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First Derivative of the Multivariate Normal Densities with RcppArmadillo

There is a great RcppArmadillo implementation of multivariate normal densities. But I was looking for the first derivative of the multivariate normal densities. Good implementations are surprisingly hard to come by. I wasn’t able to find any online and my first solutions in R were pretty slow. RcppArmadillo might be a great alternative particularly because I am not...

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Using Arial in R figures destined for PLOS ONE

September 9, 2013
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Despite the refreshing change that the journal PLOS ONE represents in terms of open access and an refreshing change to the stupidity that is quality/novelty selection by the two or three people that review a paper, it’s submission requirements are far less progressive. Yes they make you jump through a lot of hoops getting your figures and...

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analyze the survey of business owners (sbo) with r

September 9, 2013
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each year ending in a two or a seven, census bureau employees stow away their eponymous decennial population count to focus on the economic census.  and when charged with gauging the health of the american economy, who better to survey than busine...

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Animating the Metropolis algorithm

September 8, 2013
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Animating the Metropolis algorithm

The Metropolis algorithm, and its generalization (Metropolis-Hastings algorithm) provide elegant methods for obtaining sequences of random samples from complex probability distributions. When I first read about modern MCMC methods, I had trouble visualizing the convergence of Markov chains in higher dimensional cases. So, I thought I might put together a visualization in a two-dimensional case. I’ll use...

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Generate and Retrieve Many Objects with Sequential Names

September 8, 2013
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Generate and Retrieve Many Objects with Sequential Names

While coding ensemble methods in data mining with R, e.g. bagging, we often need to generate many data and models objects with sequential names. Below is a quick example how to use assign() function to generate many prediction objects on the fly and then retrieve these predictions with mget() to do the model averaging.

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Mixed models; Random Coefficients, part 1

September 8, 2013
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Mixed models; Random Coefficients, part 1

Continuing with my exploration of mixed models I am now at the first part of random coefficients: example 59.5 for proc mixed (page 5034 of the SAS/STAT 12.3 Manual). This means I skipped examples 59.3 (plotting the likelihood) and 59.4 (known G and R)...

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Rforecastio – Simple R Package To Access forecast.io Weather Data

September 8, 2013
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Rforecastio – Simple R Package To Access forecast.io Weather Data

It doesn’t get much better for me than when I can combine R and weather data in new ways. I’ve got something brewing with my Nest thermostat and needed to get some current wx readings plus forecast data. I could have chosen a number of different sources or API’s but I wanted to play with

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Maximum Likelihood Estimation and the Origin of Life

September 8, 2013
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Maximum Likelihood Estimation and the Origin of Life

# Maximum likelihood Estimation (MLE) is a powerful tool in econometrics which allows for the consistent and asymptotically efficient estimation of parameters given a correct identification (in terms of distribution) of the random variable. # It i...

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The Problem with Percentiles

September 8, 2013
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The Problem with Percentiles

The Problem with Percentiles Percentiles (or, more accurately, quantiles) are deeply embedded in the psyche of actuaries, statisticians and similar beasts. They are referred to implicitly in the Solvency 2 directive (Article 100, Value at Risk) without explanation. They are so ingrained...

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