Monthly Archives: January 2013

Decomposition: The Statistics Software Signal

January 8, 2013
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From: Decomposition: The Statistics Software Signal http://seanjtaylor.com/post/39573264781/the-statistics-software-signal"When you don't have to code your own estimators, you probably won't understand what you're doing. I'm not saying that you defini...

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Data science = failure of imagination

January 8, 2013
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From: http://www.r-bloggers.com/data-driven-science-is-a-failure-of-imagination/I think I like this distinction between Bayesian and Frequentist statistics: "we are nearly always ultimately curious about the Bayesian probability of the hypothesis ...

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Interactive MDS visualisation using D3

January 8, 2013
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Interactive MDS visualisation using D3

Here’s a sneak peak into upcoming visualisation work. I’ve been working a bit on MDS (Multi-dimensional scaling), a classical technique for visualising distance data. Classical MDS is useful, but interactive MDS is *much* more useful. Using D3, a Javascript visualisation framework, it’s relatively easy to make interactive MDS plots. This example shows how basic interaction

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R-package: Wilcox’ Robust Statistics updated (WRS v0.20)

January 8, 2013
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Rand Wilcox constantly updates the functions accompanying his books on robust statistics. Recently, they have been updated to version 20. The functions are available in the WRS package for R – for installation simply type install.packages("WRS", repos="http://R-Forge.R-project.org") In version 0.20, a number of functions dealing with ANCOVA have been added and some others improved. Unfortunately,

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Remembering server installation details

January 8, 2013
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Remembering server installation details

I’ve been moving part of my work to university servers, where I’m just one more peasant user with little privileges. In exchange, I can access the jobs from anywhere and I can access multiple processors if needed. Given that I have a sieve-like memory, where configuration details quickly disappear through many small holes, I’m documenting

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Speeding up R computations Pt III: parallelization

January 8, 2013
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In two previous posts, I have written about how you can speed up your R computations either by using strange notation and non-standard functions or by compiling your code. Last year my department bought a 64-core computational server, which allowed me ...

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Open Science Challenge

January 8, 2013
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Open Science Challenge

Open Science Science is becoming more open in many areas: publishing, data sharing, lab notebooks, and software. There are many benefits to open science. For example, sharing research data alongside your publications leads to increased citation ra...

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Handling Strings with Rcpp

January 8, 2013
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This is a quick example of how you might use Rcpp to send and receive R ‘strings’ to and from R. We’ll demonstrate this with a few operations. Sort a String with R Note that we can do this in R in a fairly fast way: my_strings <-...

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Using Rcout for output synchronised with R

January 8, 2013
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The Writing R Extensions manual, which provides the gold standard of documentation as far as extending R goes, suggests to use Rprintf and REprintf for output (from C/C++ code) as these are matched to the usual output and error streams maintained by R ...

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Open Science Challenge

January 8, 2013
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Open Science Challenge

Open Science Science is becoming more open in many areas: publishing, data sharing, lab notebooks, and software. There are many benefits to open science. For example, sharing research data alongside your publications leads to increased citation ra...

Read more »