I was searching for open data recently, and stumbled on Socrata. Socrata has a lot of interesting data sets, and while I was browsing around, I found a data set on federal bailout recipients. Here is the data set. However, data sets on Socrata are not always the most recent ... [Read more...]

Introduction This post incorporates parts of yesterday's post about bagging. If you are unfamiliar with bagging, I suggest that you read it before continuing with this article. I would like to give a basic overview of ensemble learning. Ensemble learning involves combining multiple predictions derived by different techniques in order ... [Read more...]

A few days ago, Romain François explained how to interface with the Facebook Graph API explorer with R. This was a low-level interface, giving the R programmer the ability to the raw data that Facebook can provide about your connections. Now, just four days later, the first application in ... [Read more...]

Quite a handy couple of lines of code to subset a list in R to just those elements which meet a certain condition. Here’s an example to return only those elements of a list which are a certain class. Thanks to this StackOverflow answer. [Read more...]

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Ever since R was born (evoked?) geeks have been trying to get it to talk HTML. A list of web interfaces for R is updated on CRAN here. Aims are various. Some seek to replace R with a traditional GUI. Others are more ambitious and open up a glimpse of ... [Read more...]

Introduction This post incorporates parts of yesterday's post about bagging. If you are unfamiliar with bagging, I suggest that you read it before continuing with this article. I would like to give a basic overview of ensemble learning. Ensemble learning involves combining multiple predictions derived by different techniques in order ... [Read more...]

So you want to run R in the cloud so you can set your Gibbs sampling off, forget about it, and not be paranoid about power cuts and reboots. Andrew Gelman hosted a good debate on the pros and cons of R in the cloud on his blog. The consensus ... [Read more...]

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With bonus R codeIt came as a shock to learn from PubMed that almost 900 papers were published with the word "microarray" in their titles last year alone, just 12 shy of the 2010 count. More alarming, many of these papers were not of the innocuous "Microarray study of gene expression in dog ... [Read more...]

I received the following email today: I am preparing a thesis … I need to conduct the widest possible poll, and it occurred to me that perhaps you could guide me toward an internet-based way in which this can be done easily. I have a ten-question questionnaire prepared, that I wish ... [Read more...]

In the revision of Bayesian Core on which Jean-Michel Marin and I worked together most of last week, having missed our CIRM break last summer (!), we have now included an illustration of what happens to an AR(p) time series when the customary stationarity+causality condition on the roots of ... [Read more...]

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The RInside package provides C++ classes that make it easier to embed R in C++ code on many platforms. According to a post from package co-maintainer Dirk Eddelbuettel, the package has just been updated to version 0.2.6 with improved support on Windows, and compatibility with R's standard random number generator. You ... [Read more...]

I have long used JAGS to do all of my Bayesian work on my mac. Early on, I tried to figure out how to install WinBUGS and OpenBUGS and their accompanying R libraries on my mac, but, to no avail. I just had too hard of a time getting them ... [Read more...]

Dear R people, It is a (VERY) rare event that I allow a non-R related content on R-bloggers. Today is one such rare a day. I do this in order to raise awareness of two bills [Read more...]

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It's actually already two month old news, but my research "Developement of the Universal Methodology for Assessement of Environmental Risk Caused by Fires at Illegal Dumps" (download in RUSSIAN), that was made special for Fire Monitoring Challenge (by GIS-Lab, Microsoft, NEXTGIS, several universities and GIS/spatial data corporations), was  awarded ... [Read more...]

Bagging, aka bootstrap aggregation, is a relatively simple way to increase the power of a predictive statistical model by taking multiple random samples(with replacement) from your training data set, and using each of these samples to construct a separate model and separate predictions for your test set. These predictions ... [Read more...]