Jetsam 9: Walking amongst the trees
Page 43 of 69
GM-fed pigs, chance and how research works
Following my post on GM-fed pigs I received several comments, mostly through Twitter. Some people liked having access to an alternative analysis, while others replied with typical anti-GM slogans, completely ignoring that I was posting about the technical side of the paper. This post is not for the slogan crowd (who clearly are not interested in understanding), but for people that would like to know more about how one would evaluate claims from a scientific article. While I refer to the pig paper, most issues apply to any paper that uses statistics.
In general, researchers want to isolate the effect of the treatments under study (diets in this case) from any other extraneous influence. We want control over the experimental conditions, so we can separate the effects of interest from all other issues that could create differences between our experimental units (pigs in this case). What could create ‘noise’ in our results? Animals could have different genetic backgrounds (for example with different parents), they could be exposed to different environmental conditions, they could be treated differently (more kindly or harshly), etc.
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Jetsam 8: swimming geometry
Ordinal logistic GM pigs
This week another ‘scary GMO cause disease’ story was doing the rounds in internet: A long-term toxicology study on pigs fed a combined genetically modified (GM) soy and GM maize diet. Andrew Kniss, a non-smokable weeds expert, mentioned in Twitter that the statistical analyses in the study appeared to be kind of dodgy.
Curious, I decided to have a quick look and I was surprised, first, by the points the authors decide to highlight in their results, second, by the pictures and captioning used in the article and, last, by the way of running the analysis. As I’m in the middle of marking assignments and exams I’ll only have a quick go at part of the analysis. As I see it, the problem can be described as ‘there is a bunch of pigs who were fed either non-GM feed or GM feed. After some time (approximately 23 weeks) they were killed and went through a CSI-like autopsy’, where part of the exam involved the following process:
Continue readingA 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.