Monthly Archives: June 2013

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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analyze the pesquisa de orcamentos familiares (pof) with r

June 17, 2013
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for the unlucky among us born without a portuguese mother tongue, the pesquisa de orcamentos familiares (pof) translates to survey of household budgets.  this data set captures brazilian family consumption habits, allocation of expenses, and incom...

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Annotating select points on an X-Y plot using ggplot2

June 16, 2013
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Annotating select points on an X-Y plot using ggplot2

or, Is the Seattle Mariners outfield a disaster?The BackstoryEarlier this week (2013-06-10), a blog post by Dave Cameron appeared at USS Mariner under the title “Maybe It's Time For Dustin Ackley To Play Some Outfield”. In the first paragraph, Cameron describes to the Seattle Mariners outfield this season as “a complete disaster” and Raul Ibanez as...

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Exploratory Data Analysis: Combining Box Plots and Kernel Density Plots into Violin Plots for Ozone Pollution Data

Exploratory Data Analysis: Combining Box Plots and Kernel Density Plots into Violin Plots for Ozone Pollution Data

Introduction Recently, I began a series on exploratory data analysis (EDA), and I have written about descriptive statistics, box plots, and kernel density plots so far.  As previously mentioned in my post on box plots, there is a way to combine box plots and kernel density plots.  This combination results in violin plots, and I

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Dynamic Data Visualizations in the Browser Using Shiny

June 16, 2013
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Dynamic Data Visualizations in the Browser Using Shiny

After being busy the last two weeks teaching and attending academic conferences, I finally found some time to do what I love, program data visualizations using R. After being interested in Shiny for a while, I finally decided to pull the trigger and build my first Shiny app! I wanted to make a proof of

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General Regression Neural Network with R

June 16, 2013
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General Regression Neural Network with R

Similar to the back propagation neural network, the general regression neural network (GRNN) is also a good tool for the function approximation in the modeling toolbox. Proposed by Specht in 1991, GRNN has advantages of instant training and easy tuning. A GRNN would be formed instantly with just a 1-pass training with the development data.

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