An opinion piece on Calculus and statistics by Daniel Kaplan, on teaching a different version of your typical introductory calculus course, so it is useful for statistics. He goes as far as teaching calculus using R. There is more information in Project MOSAIC.
Biased and Inefficient, Thomas Lumley’s personal statistics blog (he insists that posting 75% of Statschat is not enough to qualify as personal). You may know Thomas from the survey package (or a few others).
If you are a postgrad student in New Zealand you can apply for a NeSI (New Zealand eScience Infrastructure) postgraduate allocation to access high performance computing facilities.
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.
‘No estaba muerto, andaba the parranda’† as the song says. Although rather than partying it mostly has been reading, taking pictures and trying to learn how to record sounds. Here there are some things I’ve come across lately.
I can’t remember if I’ve recommended Matloff’s The Art of R Programming before; if I haven’t, go and read the book for a good exposition of the language. Matloff also has an open book (as in free PDF, 3.5MB) entitled ‘From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science’. The download link is near the end of the page. He states that the reader ‘must know calculus, basic matrix algebra, and have some minimal skill in programming’, which incidentally is the bare minimum for someone that wants to get a good handle on stats. In my case I learned calculus partly with Piskunov’s book (I’m a sucker for Soviet books, free DjVu), matrix algebra with Searle’s book and programming with… that’s another story.
I’ve ordered a couple of books from CRC Press, which I hope to receive soon (it depends on how long it takes for the parcel to arrive to the middle of nowhere):
Stroup’s Generalized Linear Mixed Models: Modern Concepts, Methods and Applications, which according to the blurb comes ‘with numerous examples using SAS PROC GLIMMIX’. You could be wondering Why is he reading a book that includes SAS as a selling point? Well, SAS is a very good statistical thinking that still has a fairly broad installed based. However, the real selling point is that I’ve read some explanations on mixed models written by Stroup and he has superb understanding of the topic. I’m really looking forward to put my paws on this book.
Lunn et al.’s The BUGS Book: A Practical Introduction to Bayesian Analysis. I don’t use BUGS but occasionally use JAGS and one of the things that irks me of programs like BUGS, JAGS or INLA is that they follow the ‘here is a bunch of examples’ approach to documentation. This books is supposed to provide a much more detailed account of the ins and outs of fitting models and a proper manual. Or at least that’s what I’m hoping to find in it.
This is one of those times of the year: struggling to keep the head above the water, roughly one month before the last lecture of the semester. On top trying to squeeze trips, meetings and presentations in between while dealing with man flu.
Reached mid-semester point, with quite a few new lectures to prepare. Nothing extremely complicated but, as always, the tricky part is finding a way to make it meaningful and memorable. Sometimes, and this is one of those times, I sound like a broken record but I’m a bit obsessive about helping people to ‘get’ a topic.
I gave knitr a go while preparing model answers for an assignment. I liked the integration with RStudio and the possibility of using several markup languages. For simple documents Markdown + R is an excellent combination to generate HTML output. However, I wanted to generate PDF output with some math, so I went for latex integration. Jeromy Anglim’s site provides a very good starting point for using knitr to document reproducible analyses.
Nature Genetics published ‘A mixed-model approach for genome-wide association studies of correlated traits in structured populations’, where variance components were estimated using ASReml. Via Christophe Lalanne.
Back teaching a couple of subjects and it’s the constant challenge to find enough common ground with students so one can push/pull them to the other side of new concepts. We are not talking about complex hierarchical models using mixed models or Bayesian approaches, but multiple linear regression or similar. What do students actually learn in first year stats…?
I’m enjoying reading Machine Learning for Hackers by Drew Conway and John Myles White. There isn’t a lot of stuff new for me in the book—although working with text is not something I usually do—but I have chosen to read the book with newbie eyes. I’m (repeating myself) looking for enough common ground with students so one can push/pull them to the other side of new concepts and, let’s face it, I was 20 quite a few years ago.
Observation on teaching a lab for STAT202, in which many students are using R for the first time. Do you remember your first steps in S+/R? Some students see the light quickly while others are struggling to get their heads around giving commands to a computer (without clicking on icons).
This tweet by @isomorphisms resonated with me: ‘Someday I hope to be reading more Penguin Classics than John Wileys & Springer Verlags’.
Tom points to an explanation of ‘What really shoots out of spiderman’s modified forelimbs, and why this causes such consternation’.
I have to convince College IT guys to install R-Studio in a few hundred computers. R-Studio is becoming better all the time, making it obscene to subject students to the naked R for Windows installation without syntax highlighting.
The end is near! At least the semester is coming to an end, so students have crazy expectations like getting marks back for assignments, and administrators want to see exam scripts. Sigh! What has been happening meanwhile in Quantum Forest?
Tom cracked me up with “…my data is so fucking patchy. I’m zipoissoning the place up like a motherfucker, or something”. I probably need to embark in some zipoissoning, and he was kind enough to send me some links.
People keep on kicking this guy called “p-value” when he is still unconscious on the floor. Bob O’Hara declares that p-values are evil. Not funny! John Cook reminds us that “The language of science is the language of probability, and not of p-values.” —Luis Pericchi”. Actually, these days the language of Science is English or whatever passes for English in a press release.
Discussion with Mark about the canonical pronunciation for MCMCglmm: mac-mac-glim, em-see-em-see-glim or Luis’s dumb em-see-em-see-gee-el-em-em. We need a press release from Jarrod Hadfield to clear the air!
RStudio now supports knitr; I’m looking forward to being able to send email from it. Wait, then it would be like a pretty Emacs.
Did you know? There is life beyond R. Pandas keeps on growing (if Python is your thing). Douglas Bates keeps on digging Julia. I ‘discovered’ Bartosz Milewski‘s blog, which I enjoy reading although I understand a small fraction of what he’s taking about. I came across Bartosz while looking for information on using supercomputers.
It has been month and a half since I compiled a list of statistical/programming internet flotsam and jetsam.
Via Lambda The Ultimate: Evaluating the Design of the R Language: Objects and Functions For Data Analysis (PDF). A very detailed evaluation of the design and performance of R. HT: Christophe Lalanne. If you are in statistical genetics and Twitter Christophe is the man to follow.
Attributed to John Tukey, “without assumptions there can be no conclusions” is an extremely important point, which comes to mind when listening to the fascinating interview to Richard Burkhauser on the changes of income for the middle class in USA. Changes to the definition of the unit of analysis may give a completely different result. By the way, does someone have a first-hand reference to Tukey’s quote?
Nature news publishes RNA studies under fire: High-profile results challenged over statistical analysis of sequence data. I expect to see happening more often once researchers get used to upload the data and code for their papers.
It has been a strange last ten days since we unexpectedly entered grant writing mode. I was looking forward to work on this issue near the end of the year but a likely change on funding agency priorities requires applying in a few weeks; unfortunately, it means that all this is happening at the same time I am teaching.
As usual I got involved in a strange, for me, project which will require semantic analysis of international treaties. I will start having a look at Latent Semantic Analysis using lsa in R and gensim in Python. I’ll have to retrieve documents from the web and process them in quite a few ways.
The success of some of Hadley Wickham’s packages got me thinking about underlying design issues in R that make functions so hard to master for users. Don’t get me wrong, I still think that R is great, but why are there so many problems to understand part of the core functionality? A quick web search will highlight that there is, for example, an incredible amount of confusion on how to use the apply family of functions. The management of dates and strings is also a sore point. I perfectly understand the need for, and even the desirability of, having new packages that extend the functionality of R. However, this is another kettle of fish; we are talking about making sane design choices so there is no need to repackage basic functionality to make it usable.
Talking about failures, Andrew Gelman mentions the sempiternal problem of designers Turn(ing) a Boring Bar Graph into a 3D Masterpiece or, as a commenter put it, “Turn(ing) a Boring Bar Graph into a 3D Pile of Steaming Crap”. While it is always easy to have a laugh on designers, we should remember that the abundance of 3D piles[…] also reflects our failure to make the point on good data presentation clear. Well, that and the spawn of evil Microsoft Excel and PowerPoint.
Beware if you are going out for dinner with vegetarian friends. Besides vegetarians having an inordinate influence on the choice of restaurant you may end up subsidizing their meals. HT: @EricCrampton.