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RStudio Update

October 27, 2011
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RStudio Update

With R 2.14 slated to be released next week we wanted to encourage everyone planning to upgrade to also update to the latest release of RStudio (v0.94.110). For R 2.14 users this release includes tweaks related to compatibility with the R 2.14 graphics engine as well as compatibility with the new parallel package. There are

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R Cookbook with examples

October 27, 2011
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R Cookbook with examples

An R Cookbook can be found at http://code.ca-net.org/R%20Cookbook. It is a short web document presenting dozens of examples on - Accessing Database with packages RSQLite, RMySQL, RdbiPgSQL and RODBC; - Reading and Writing Data; - Date/Time variable; - Graphics; - … Continue reading →

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Controlling multiple risk measures during construction of efficient frontier

October 26, 2011
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Controlling multiple risk measures during construction of efficient frontier

In the last few posts I introduced Maximum Loss, Mean-Absolute Deviation, and Expected shortfall (CVaR) and Conditional Drawdown at Risk (CDaR) risk measures. These risk measures can be formulated as linear constraints and thus can be combined with each other to control multiple risk measures during construction of efficient frontier. Let’s examine efficient frontiers computed

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Catching up faster by switching sooner

October 25, 2011
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Catching up faster by switching sooner

Here is our discussion (with Nicolas Chopin) of the Read Paper of last Wednesday by T. van Erven, P. Grünwald and S. de Rooij (Centrum voor Wiskunde en Informatica, Amsterdam), entitled Catching up faster by switching sooner: a predictive approach to adaptive estimation with an application to the Akaike information criterion–Bayesian information criterion dilemma. It

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Expected shortfall (CVaR) and Conditional Drawdown at Risk (CDaR) risk measures

October 25, 2011
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Expected shortfall (CVaR) and Conditional Drawdown at Risk (CDaR) risk measures

In the Maximum Loss and Mean-Absolute Deviation risk measures post I started the discussion about alternative risk measures we can use to construct efficient frontier. Another alternative risk measures I want to discuss are Expected shortfall (CVaR) and Conditional Drawdown at Risk (CDaR). I will use methods presented in Comparative Analysis of Linear Portfolio Rebalancing

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Approximate Bayesian computational methods on-line

October 25, 2011
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Approximate Bayesian computational methods on-line

Fig. 4 – Boxplots of the evolution of ABC approximations to the Bayes factor. The representation is made in terms of frequencies of visits to models MA(1) and MA(2) during an ABC simulation when ε corresponds to the 10,1,.1,.01% quantiles on the simulated autocovariance distances. The data is a time

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Vanilla C code for the Stochastic Simulation Algorithm

October 24, 2011
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Vanilla C code for the Stochastic Simulation Algorithm

The Gillespie stochastic simulation algorithm (SSA) is the gold standard for simulating state-based stochastic models. If you are a R buff, a SSA novice and want to get quickly up and running stochastic models (in particular ecological models) that are not … Continue reading →

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Show me your WAR face!

October 24, 2011
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Show me your WAR face!

Below is a chart of the top 20 offensive players based on FanGraphs WAR for the 2011 season.  The various features and their corresponding metric are clear in the image. I’ve also included the leader and last place for each … Continue reading →

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understanding computational Bayesian statistics: a reply from Bill Bolstad

October 23, 2011
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understanding computational Bayesian statistics: a reply from Bill Bolstad

Bill Bolstad wrote a reply to my review of his book Understanding computational Bayesian statistics last week and here it is, unedited except for the first paragraph where he thanks me for the opportunity to respond, “so readers will see that the book has some good features beyond having a “nice cover”.” (!) I simply processed

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Generating sets of permutations

October 21, 2011
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Generating sets of permutations

In previous posts I discussed how to generate a single permutation from a fully-randomised or restricted permutation design using shuffle(). Here I want to briefly mention the shuffleSet() function and illustrate it’s usage. Every time you call shuffle() it has to interpret the … Continue reading →

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