Palimpsest

Evolving notes, images and sounds by Luis Apiolaza

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Flotsam 12: early June linkathon

2013-06-01 /

A list of interesting R/Stats quickies to keep the mind distracted:

  • A long draft Advanced Data Analysis from an Elementary Point of View by Cosma Shalizi, in which he uses R to drive home the message. Not your average elementary point of view.
  • Good notes by Frank Davenport on starting using R with data from a Geographic Information System (GIS). Read this so you get a general idea of how things fit together.
  • If you are in to maps, Omnia sunt Communia! provides many good tips on producing them using R.
  • Mark James Adams reminded us that Prediction ? Understanding, probably inspired by Dan Gianola‘s course on Whole Genome Prediction. He is a monster of Bayesian applications to genetic evaluation.
  • If you are in to data/learning visualization you have to watch Bret Victor’s presentation on Media for thinking the unthinkable. He is so far ahead what we normally do that it is embarrassing.
  • I follow mathematician Atabey Kaygun in twitter and since yesterday I’ve been avidly reading his coverage of the protests in Turkey. Surely there are more important things going on in the world than the latest R gossip.

I’m marking too many assignments right now to have enough time to write something more substantial. I can see the light at the end of the tunnel though.

Winter flying between Blenheim and Christchurch (Photo: Luis, click to enlarge).

Jetsam 7: the wing

2013-05-17 /
Winter flying between Blenheim and Christchurch.
Winter flying between Blenheim and Christchurch.

Analyzing a simple experiment with heterogeneous variances using asreml, MCMCglmm and SAS

2013-05-17 /

I was working with a small experiment which includes families from two Eucalyptus species and thought it would be nice to code a first analysis using alternative approaches. The experiment is a randomized complete block design, with species as fixed effect and family and block as a random effects, while the response variable is growth strain (in \( \mu \epsilon\)).

When looking at the trees one can see that the residual variances will be very different. In addition, the trees were growing in plastic bags laid out in rows (the blocks) and columns. Given that trees were growing in bags siting on flat terrain, most likely the row effects are zero.
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Subsetting data

2013-05-07 /

At School we use R across many courses, because students are supposed to use statistics under a variety of contexts. Imagine their disappointment when they pass stats and discovered that R and statistics haven’t gone away!

When students start working with real data sets one of their first stumbling blocks is subsetting data. We have data sets and either they are required to deal with different subsets or there is data cleaning to do. For some reason, many students struggle with what should be a simple task.
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