Monthly Archives: June 2013

Evaluating Optimization Algorithms in MATLAB, Python, and R

June 18, 2013
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Evaluating Optimization Algorithms in MATLAB, Python, and R

As those of you who read my last post know, I’m at the NIMBioS-CAMBAM workshop on linking mathematical models to biological data here at UT Knoxville. Day 1 (today) was on parameter estimation and model identifiability. Specifically, we (quickly) covered … Continue reading →

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googleVis 0.4.3 released with improved Geocharts

June 18, 2013
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The Google Charts Tools provide two kinds of heat map charts for geographical data, the Flash based Geomap and the HTML5/SVG based Geochart. I prefer the Geochart as it doesn't require Flash, but so far there have been two shortcomings with it: I couldn't add additional tooltip information and the default Mercator projection shows Greenland the...

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Software Packages for Graphs and Charts

June 17, 2013
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Software Packages for Graphs and Charts

Graphs can be an important feature of analysis. A graph that has been well designed and put together can make summary statistics much more readable and increase the interpretability. It also makes reports and articles looks more professional. There are many software packages that are available to design great graphs and charts.  This seems to

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Computerworld’s Beginners Guide to R

June 17, 2013
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Sharon Machlis is not only the online managing editor at Computerworld, she's also a budding data scientist who recently started learning the R language. To the benefit of all other new R users, she's shared her learnings in an excellent 6-part beginners guide to R, published by Computerworld. It's jam-packed with useful information for anyone getting started with R,...

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Zombie Apocalypse Survival Test – R-Powered (using Concerto)

June 17, 2013
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Zombie Apocalypse Survival Test – R-Powered (using Concerto)

This test is the first attempt to seriously assess the ability of individuals to survive a zombie apocalypse.  This test is administered using the R powered open-source testing platform Concerto developed at the University of Cambridge. The t...

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Bayesian computational tools

June 17, 2013
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Bayesian computational tools

I just updated my short review on Bayesian computational tools I first wrote in April for the Annual Review of Statistics and Its Applications. The coverage is quite restricted, as I took advantage of two phantom papers I had started a while ago, one with Jean-Michel Marin, on hierarchical Bayes methods and on ABC. (As

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Dave Harris on Maximum Likelihood Estimation

June 17, 2013
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Dave Harris on Maximum Likelihood Estimation

At our last Davis R Users’ Group meeting of the quarter, Dave Harris gave a talk on how to use the bbmle package to fit mechanistic models to ecological data. Here’s his script, which I ran throgh the spin function in knitr: # Load data library(emdbook) ## Loading required package: MASS Loading required package: lattice library(bbmle) ## Loading required package:...

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Oracle R Connector for Hadoop 2.1.0 released

June 17, 2013
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(This article was first published on Oracle R Enterprise, and kindly contributed to R-bloggers) Oracle R Connector for Hadoop (ORCH), a collection of R packages that enables Big Data analytics using HDFS, Hive, and Oracle Database from a local R environment, continues to make advancements. ORCH 2.1.0 is now available, providing a flexible framework while remarkably improving performance and...

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Model Selection in Bayesian Linear Regression

June 17, 2013
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Model Selection in Bayesian Linear Regression

Previously I wrote about performing polynomial regression and also about calculating marginal likelihoods. The data in the former and the calculations of the latter will be used here to exemplify model selection. Consider data generated by and suppose we wish to fit a polynomial of degree 3 to the data. There are then 4 regression The post Model...

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Stashing and playing with raw data locally from the web

June 17, 2013
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It is getting easier to get data directly into R from the web. Often R packages that retrieve data from the web return useful R data structures to users like a data.frame. This is a good thing of course to make things user friendly.However, what if you want to drill down into the data that's returned from a query...

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