Posts Tagged ‘ Rstats ’

Online R and Plotly Graphs: Canadian and U.S. Maps, Old Faithful with Multiple Axes, & Overlaid Histograms

February 6, 2014
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Online R and Plotly Graphs: Canadian and U.S. Maps, Old Faithful with Multiple Axes, & Overlaid Histograms

Guest post by Matt Sundquist of plot.ly. Plotly is a social graphing and analytics platform. Plotly’s R library lets you make and share publication-quality graphs online. Your work belongs to you, you control privacy and sharing, and public use is free (like GitHub). We are in beta, and would love your feedback, thoughts, and advice. […]

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What’s Warren Buffett’s $1 Billion Basketball Bet Worth?

January 23, 2014
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What’s Warren Buffett’s $1 Billion Basketball Bet Worth?

A friend of mine just alerted me to a story on NPR describing a prize on offer from Warren Buffett and Quicken Loans. The prize is a billion dollars (1B USD) for correctly predicting all 63 games in the men’s Division I college basketball tournament this March. The facebook page announcing the contest puts the odds at 1:9,223,372,036,854,775,808, […]

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Simudidactic

November 23, 2013
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Simudidactic

auto·di·dact n. A self-taught person. From Greek autodidaktos, self-taught : auto-, auto- + didaktos, taught; + sim·u·late v. To create a representation or model of (a physical system or particular situation, for example). From Latin simulre, simult-, from similis, like; = (If you can get past the mixing of Latin and Greek roots) sim·u·di·dactic adj. To learn by creating a representation or model of a physical system or […]

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Montreal R User Group – Dr. Ramnath Vaidyanathan on his rCharts package

October 27, 2013
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Montreal R User Group –  Dr. Ramnath Vaidyanathan on his rCharts package

Monday, October 28, 2013. 6:00pm at Notman House 51 Sherbrooke W., Montreal, QC. We are very pleased to welcome back Dr. Ramnath Vaidyanathan for a talk on interactive documents as it relates to his excellent rCharts package. Bringing a laptop to follow along is highly encouraged. I would recommend installing rCharts prior to the workshop. library(devtools) pkgs <- c(‘rCharts’, ‘slidify’, ‘slidifyLibraries’) install_github(pkgs, ‘ramnathv’, ref […]

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Follow up to Johnson et al Post

October 21, 2013
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Follow up to Johnson et al Post

Last week I posted a comment on a paper by Neil Johnson and colleagues that I now regret. The comment amounted to a bit of statistical pedantry on my part regarding some of the wording in the paper. It was my wording in this post, and specifically the title, which would have benefited from some […]

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P-value fallacy alive and well: Latest case in Scientific Reports

October 17, 2013
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P-value fallacy alive and well: Latest case in Scientific Reports

Erratum (10/17/13): The paper was published in Scientific Reports, an OA journal from the publishers of Nature, and not in the Journal Nature as originally reported. Clarification (10/17/13): The paper discussed here is quite good overall and very interesting. I do not believe that anything in this post calls into question any of its main […]

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Calculating AUC the hard way

October 10, 2013
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Calculating AUC the hard way

The Area Under the Receiver Operator Curve is a commonly used metric of model performance in machine learning and many other binary classification/prediction problems. The idea is to generate a threshold independent measure of how well a model is able to distinguish between two possible outcomes. Threshold independent here just means that for any model […]

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Marginal likelihood from tempered Bayesian posteriors

October 1, 2013
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Marginal likelihood from tempered Bayesian posteriors

Introduction In the previous post I showed that it is possible to couple parallel tempered MCMC chains in order to improve mixing. Such methods can be used when the target of interest is a Bayesian posterior distribution that is difficult to sample. There are (at least) a couple of obvious ways that one can temper […]

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Parallel tempering and Metropolis coupled MCMC

September 29, 2013
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Parallel tempering and Metropolis coupled MCMC

Introduction Parallel tempering is a method for getting Metropolis-Hastings based MCMC algorithms to work better on multi-modal distributions. Although the idea has been around for more than 20 years, and works well on many problems, it still isn’t routinely used in applications. I think this is partly because relatively few people understand how it works, […]

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Summary stats for ABC

September 1, 2013
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Summary stats for ABC

Introduction In the previous post I gave a very brief introduction to ABC, including a simple example for inferring the parameters of a Markov process given some time series observations. Towards the end of the post I observed that there were (at least!) two potential problems with scaling up the simple approach described, one relating […]

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