Recently talking to a colleague, had contact with a problem that I had never worked with before: modeling with genetic The post Genetic data, large matrices and glmnet() appeared first on Flavio Barros .

The next Cologne R user group meeting is scheduled for tomorrow, 26 February 2014. We are delighted to welcome: Diego de Castillo: R and databases Kim Kuen Tang: Hands on using R and kdb+ together Frank Celler: ArangoDB (Lightning Talk) Further details and the agenda are available on our KölnRUG Meetup site. Please sign up if you would like to come...

Symmetry is what we see at a glance (Blaise Pascal) Ladies and gentlement, the beautiful Marilyn Monroe: There are several image processing packages in R. In this experiment I used biOps, which turns images into 3D matrices. The third dimension is a 3-array corresponding to (r, g, b) color of pixel defined by two other

In order to celebrate my Gmisc-package being on CRAN I decided to pimp up the forestplot2 function. I had a post on this subject and one of the suggestions I got from the comments was the ability to change the default box marker to something else. This idea had been in my mind for a while and I therefore...

High dimensional biological data shares many qualities with other forms of data. Typically it is wide (samples << variables), complicated by experiential design and made up of complex relationships driven by both biological and analytical sources of variance. Luckily the powerful combination of R, Cytoscape (< v3) and the R package RCytoscape can be used

In the video below from The Atlantic, the differences in the way US citizens describe or pronounce various things is illustrated in a series of phone calls (via Sullivan): If you're wondering how your dialect fits in, you can try the New York Times Dialect Quiz. Answer 25 questions, and it will identify the 3 US cities that most...

Graham Williams is the Lead Data Scientist at the Australian Taxation Office, and the creator of Rattle, an open-source GUI for data mining with R. (Check out some recent reviews/demos of Rattle on this blog here and here.) Dr Williams continues his many contributions to the R community with One Page R, a "Survival Guide to Data Science with...

The probably most frequent criticism of Bayesian statistics sounds something like “It’s all subjective – with the ‘right’ prior, you can get any result you want.”. In order to approach this criticism it has been suggested to do a sensitivity analysis (or robustness analysis), that demonstrates how the choice of priors affects the conclusions drawn

It is common to want forecasts to be positive, or to require them to be within some specified range . Both of these situations are relatively easy to handle using transformations. Positive forecasts To impose a positivity constraint, simply work on the log scale. With the forecast package in R, this can be handled by specifying the Box-Cox parameter...

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