Deep Thoughts and Silliness

On the Move

2008-02-21T12:02:00.002+02:00

I mentioned earlier this week that I had been invited to blog elsewhere, and it's now been sorted out, and I even have my first post up.

My new virtual residence is at the Nature Network, the network that Nature (the journal) has developed to create a community of scientists who can complain amongst each other that they don't get enough funding. I've kept the name, so pop along and see what develops:

Deep Thoughts and Silliness

Now at Nature Networks.

I probably won't post here any more, but I might use BPSDB if I feel like a particularly virulent rant against pseudo-science.

I guess I'll be able to claim that I write for Nature now.

My Phrase for the Day

2008-02-20T13:47:00.004+02:00

I wasn't going to post on this here, lest find myself turning into Denyse O'Leary, but yesterday I posted at BPSDB about Le Canard Noir having his website closed down because he was rather critical of someone.

But, I wanted to point out Dr.* T's post on the matter, where he presents Netcetera, Le Canard Noir's former web hosts, with an award for Spineless Caitiffery.

Spineless Caitiffery.

Wonderful. Try it out. Roll it around the tongue a few times. And then look at Dr.* T's post to find out what the hell it means.

By the way, buy my book.

Ooh, how dare thay!

2008-02-19T21:06:00.002+02:00

This might mean war

A couple of years ago a physicist at the university complained to me that he was in a building called Physicum, but that I was in the building next door, called Exactum. He really didn't appreciate the implication that they were inexact. I, with my statistician's had on (it's shaped a bit like a pith helmet), found this hilarious.

This is how Birthdays should be

2008-02-18T20:50:00.003+02:00

As it's already been revealed, I turned 38 today. And what a great day it's been!
This morning I was awarded the Bob O’Hara Award for Excellence in Blogging on Pseudoscience. The prize is that I'm allowed to post on the BPSPB webpage. I know, I know you're all envious. Later, I was invited to join another blog swarm, of which more later (if it happens). Even the editor of Heredity wanted to send me a birthday present (although I'll have to write an essay about it later).

Even lunch was exceptional. I chatted to a colleague about such things as making swords and using pizza to demonstrate theorems. We ended up working out how to use spaghetti to solve the travelling salesman problem.

Even The Beast has been great. This morning he enjoyed the wildlife outside, and after I fed him this evening, he demanded a heavy fussing session. What more could one ask for in life?

Oh, yes. This

Cheers!

ID Does Predict Something

2008-02-16T13:23:00.003+02:00

Well, that was a shock. About a month ago, Bill Dembski put up a post on his blog saying that he had some ID predictions, but what about other people? This generated a lot of discussion (over 200 comments), but little in the way of predictions. And we were all left wondering if Bill actually had any.

It turns out he did, and he revealed them this week. Bizarrely, he did this after being prodded by a couple of sock-puppets. And the predictions were...

(1) ID predicts that although there will be occasional degeneration of biological structures (both macroscopic and microscopic), most structures will exhibit function and thus serve a purpose. Thus most organs should not be vestigial, and most DNA should not be “junk DNA.” ...

Ah, this old canard. Afarensis has had a go at this one, and it also has a potted rebuttal in the Index To Creationist Claims. But I think there is one point worth adding. We're told that ID only asks whether there is a designer, but it says nothing about the identity or properties of the designer (which is good. They'll be embarrassed when they find out Ididit). But this prediction about junk DNA assumes a rather pragmatic designer who wouldn't put something into their design unless it was functional. What if the designer was slightly odd, and just wanted to throw something in for the hell of it. Perhaps the designer just wants to say 'Alu to us all. This is only really an ID prediction if one makes articular assumptions about the designer. For example, that it doesn't care what colour the wheel should be.

(2) Many systems inside the cell represent nanotechnology at a scale and sophistication that dwarfs human engineering. Moreover, our ability to understand the structure and function of these systems depends directly on our facility with engineering principles (both in developing the instrumentation to study these systems and in analyzing what they do). Engineers have developed these principles by designing systems of their own, albeit much cruder than what we find inside the cell. Many of these cellular systems are literally machines: electro-mechanical machines, information-processing machines, signal-transduction machines, communication and transportation machines, etc. They are not just analogous to humanly built machines but, as mathematicians would say, isomorphic to them, that is, they capture all the essential features of machines. ID predicts that the cell would have such engineering features; by contrast, Darwinian theory has consistently underestimated the sophistication of the machinery inside the cell.

Somewhere in there there might be a prediction.

Ah, found it. It's that "machines" in the cell and human-built machines have the same essential properties. Like being designed by humans, or having their primary structure determined by DNA. Any questions?

The analogy between the different types of machine is just that - an analogy. It isn't perfect - human designed machines aren't coded by DNA, and aren't parts of structures that start dividing and reproducing. A problem, then, is how do we know when the analogy has gone too far? If we take Dr. Dembski at face values, it never does.

(3) Conservation of information results (also referred to as No Free Lunch theorems, which are well established in the engineering and mathematical literature — see www.EvoInfo.org) indicate that evolution requires an information source that imparts at least as much information to evolutionary processes as these processes in turn are capable of expressing. In consequence, such an information source (i) cannot be reduced to materialistic causes (e.g., natural selection), (ii) suggests that we live in an informationally open universe, and (iii) may reasonably be regarded as intelligent. The conservation of information counts as a positive theoretical reason to accept intelligent design and quantifies the informational hurdles that neo-Darwinian processes must overcome. Moreover, ID theorists have applied these results to actual biological systems to show that they are unevolvable by Darwinian means. ID has always predicted that there will be classes of biological systems for which Darwinian processes fail irremediably, and conservation of information is putting paid to this prediction.

OK, Dembski works on evolutionary information, so I guess we should have expected this. But it's not clear what the NFL theorems have to do with conservation of information - they say that blind search does not better than any other search strategies when averaged over all fitness surfaces, but some of those fitness surfaces will look very strange. And in reality, evolution works on a small set of such surfaces, which similar properties (I'm in a bit of a rush, so I won't dig out any links just now). Dembski did produce one manuscript claiming conservation of information, but his proof was to assume "for consistency's sake" that p₁ <>2, and then prove log(p₁) <>2). I never did get an explanation for what he meant by "for consistency's sake".

So, there you go, about we would expect 0 out of 3. Of course, I'm biased, so Iäm suer the UDites will score differently.

Fame, Journals, and Blinding

2008-02-10T14:38:00.000+02:00

A couple of weeks ago I blogged about a paper on the (possible) effect of double blinding on the bias against female authors. The paper had stirred up a few other comments, which got me thinking a bit more about why I'm a bit sceptical about double-blind reviews. Then I started thinking too hard, and ended up playing around with a simple model.

It's generally agreed that there is a bias towards better-known authors, so that a well-known author is more likely to have a manuscript recommended for acceptance than someone unknown. The argument for double-blinding is that it removes this bias, because the referee doesn't know who the author is. The problem with this is that it is often possible to guess who the author is (hell, it's sometimes possible to guess who a reviewer is) - a study 10 years ago (Cho et al. 1998) found that reviewers could work out the identity of the authors in about 40% of cases.

Presumably the authors who are recognised are the better known ones. We therefore have a situation where fame (whatever it is exactly) affects both whether a paper will be recommended for acceptance, and also whether the authors will be recognised. What effect does this have on the pattern of acceptance? Rather than just indulging in arm-waving, we can build a model, and indulge in arm-waving with numbers!

The model is simple, but hopefully captures the main points. Each author of a manuscript (for simplicity I will assume that each paper only has one author) has a fame. If the author's identity is known to the reviewer, then the probability of acceptance increases with their fame (the solid black line below).

If the reviewing process is double binded, then the probability that the author is recognised increases with the fame (the red dotted line). Note that it starts from a lower point, but increases more rapidly. if the author's name is not recognised, then the probability of acceptance is equal to the minimum probability. This is the the solid red line.

The technical details are below, for those who care. I have also scaled the probabilities of acceptance, so you can see them.

What does this show? Well, if you're a nobody, then the double-blind process means that you do as well as anyone else who isn't recognised, i.e. all but the famous. The famous do well under both systems, as they're recognised anyway. The people who lose out are those in the middle: the ones who are just starting to make a name for themselves, but are yet to be well known. With single blinding, their fame is enough that it helps them. Under double-blinding, though, they are not famous enough that they are recognised, so they are treated the same as a novice.

What this suggests, then, is that double blinding doesn't remove the biases: it just shifts them. So, the very famous actually do better under double blinding, as do the very obscure. Playing around a bit with the model suggests that the general result is robust, but it depends on the probability of recognition starting lower and having a steeper slope.

This is a model, using numbers that were plucked out of the air. But how does it compare to reality? My guess is that the effects are not as severe as shown here, but what is needed is data which can be used to estimate the parameters of the model. In the mean time, I'm not going to submit to any double-blind journals until I have my FRS.

The Maths
Fame, f, is uniformally distributed between -1 and 1. The probability of acceptance for a fame f, p_a(f), is modelled like this:

if the identity of the author is known, otherwise it is the minimum value. If the manuscript is double-blind reviewed, then the probability that the reviewer correctly recognises the name of the author, p_r(f), is

If a manuscript is reviewed double-blind, the probability that it is accepted is proportional to

p_r(f)p_a(f) + (1-p_r(f))p_a(-1)

The final probabilities are normalised, so that they sum to 1, by dividing by the sum of the probabilities.

References
Cho, M.K. et al. (1998) J. Am. Med. Assoc. 280, 243–245.

Oops

2008-02-09T21:05:00.000+02:00

Things are getting interesting over at Uncommon Descent at the moment. DaveScot is trying out a bit of implicit racism, by pointing out that Barack Obama's middle name is Hussein. Whilst this is going on another poster there, who goes by the moniker "idnet.com.au" has decided to practice his quote-mining.

He found this paper in Nature:
Georgy Koentges (2008). Evolution of anatomy and gene control. Nature 451, 658-663. doi:10.1038/451658a

and decided to "blog" about it. This consisted of excerpting a few quotes, without any commentary or indication where he had cut the text. In all he took about 20% of the full article so it looks like he's breaching copyright, the naughty boy. This might be excusable if he's accurately representing the article. Well, let's take a look and see (you can guess the result, can't you?).

idnet.com.au (or 'Noel' as I'll call him) starts off badly. This is the first quote:

Since Darwin we know that we must explain organisms not only in mechanistic terms (of mutation, selection and adaptation on the population level) but also in historical terms, as ‘descent with modification’, evolution in phylogeny. Because phylogeny is the outcome of developmental processes in populations, all heritable morphological changes derive from developmental changes in molecular control hierarchies and networks.

The start is from the third paragraph of the paper:

Since Darwin we know that we must explain the elephant not only in mechanistic terms (of mutation, selection and adaptation on the population level) but also in historical terms, as 'descent with modification', evolution in phylogeny. Molecular changes hundreds of millions of years ago constrain the possibility of change here and now. Not everything is possible, and evolutionary history is as much a story of constraint as functionality. Leonardo's 'flying machines' didn't just fail because bodies of a human size and weight fall under physical scaling laws limiting how big muscles could become. The evolutionary history that led to our present body size also stops us acquiring wings, either now or any time soon.

OK, so he's changes "the elephant" into "organisms", which is forgivable as "the elephant" only makes sense with the first two paragraphs. But where is the "Because..." sentence? Oh, the middle of the next paragraph.

So it goes on.. Noel picks out sentences and makes small changes as he sees fit. We can see his intentions when, for example, he takes this sentence:

This idea has been refined over the years in cogent discussions of 'evolvability', but there are few specific examples in metazoans where we can assign major structural changes to specific gene-regulatory causes.

and changes it into this:

There are few specific examples in metazoans where we can assign major structural changes to specific gene-regulatory causes.

Hey, let's leave out the positive work that's been done, and leave the negative in. Another nice example. Noel writes:

Recent studies on vertebrates suggest that only a fraction of ‘ultra-conserved’ CRMs are active and absolutely required for the animal to survive. Some CRMs might modulate the activities of others, so their effects might not be apparent.

Oh noes!! We poor biologists are clueless about how ‘ultra-conserved’ CRMs are conserved. But in the original the first sentence is followed by

As CRMs were assayed at only a few embryonic time points, the absence of regulatory information cannot be construed as a lack of function. It is not clear when, and in which cell types, organ-system specific CRMs are expected to be active, so inferences from such studies should be treated with great caution.

The sentence Noel leaves us with is further down the paragraph, in a discussion of the effects of deleting CRMs - it's explaining why we might not see a big effect if we do simple knock-out experiments. (Ha! It implies that development is not be irreducibly complex, so I can see why he might want to leave that out).

I could give more other examples, but I think the point is clear. Noel has picked out the comments about how we are uncertain, whilst ignoring all the parts which explain what has been done, or pointing to areas of future research. The whole article is actually positive, in that it's showing the sorts of things that could be done in the near future, with as the new technology and levels of inquiry they allow become applied to evo-devo. But UDites only get a partially copied, mis-represented version of the paper, which might interest Nature's Lawyers as well.

Ah well, it could be worse for Noel. He might have used the "Blogging About Peer-Reviewed Research" icon.

EDIT: 1. Added Mister DNA's spiffy graphic, 2. The original post has been on a heavy diet: it's only a third the size it was, and makes even less sense. Some of the quotes highlighted above have disappeared. So now this post makes even less sense.

Another trailer!

2008-02-08T16:39:00.000+02:00

This might be another film worth watching:

I think the lead actor ended up with his own game-show, or talk-show or something, where he was always introduced with a huge cry of "Here's Johnny!!".

(HT: Henry)

Politicians Object to Surveillance State Shock

2008-02-03T10:28:00.001+02:00

Hand up, who can smell the double standard on this one?

BBC NEWS | UK | Probe into police 'bugging' of MP

An MP is unhappy that his discussions with a constituent was being bugged. Hey, it's all part of the War On Terror, designed to Keep Us Safe.

I think an argument can be defended that this sort of surveillance is a good idea, in which case MPs shouldn't be exempt - the security services are no doubt picking up all sorts of information that is more important personally than anything the MP could say (I'm sure the only reason they can have such a grand building is that they pay for its upkeep with money from betting and insider dealing). I'm sure politicians don't like being spied upon, so perhaps they should indulge in a bit of empathy with the rest of us.

Was the Intelligent Design Challenge Intelligently Designed?

2008-02-02T15:52:00.000+02:00

A couple of days ago, Ian Musgrave put up The Intelligent Design Challenge at The Panda's Thumb. He gave 6 DNA sequences, and the challenge was to work out which ones were designed by humans. What I thought was interesting about this was not so much finding the answer, but working out what Ian Musgrave was trying to show, and whether the intelligent design community would pass the test he was really setting. Or indeed if they even spotted it.

The reaction of the professional IDers was, apparently, to not enter. Bill Dembski posted the challenge at Uncommon Descent, but went no further (at least publically). Casey Luskin, attack-mouse of the Disco Institute (Casey, any chance of you sending mea signed photo I can put up here?) told us in the UD thread

Dembski’s methods of design detection can discriminate between informational patterns that are produced by chance/law, or alternatively were produced by intelligence. When there is real design to be detected, Dembski’s methods of design detection can work regardless of whether the designer was human or non-human.

and then complained that the other sequences might be of non-human design, so the method won't work. Of course, he doesn't actually try to apply the method, and then note that there might be false positives. Neither are apparently prepared to use Dembski's explanatory filter for anything other than making divine coffee.

One of the amateur IDers, Patrick, did some detective work. He discovered that four of the six sequences are found in the synthetic bacterium Mycoplasma genitalium. The rest of the UD crowd are ecstatic and declare victory. Oh, and complain about how it's an awful challenge. Even funnier, when Musgrave clarifies the challenge, they really pile on, claiming total victory using an appalling cricket analogy (my conclusion is that Patrick didn't read the final delivery and was undone by the wrong'un).

Now, before Musgrave reveals all, I'll try and make an intelligently designed prediction, based on my extensive research (5 minutes BLASTing the sequences). BLAST is an algorithm for efficiently searching DNA sequences for close matches - you give it a sequence, and it tells you what sequences are similar. There is a huge database called GenBank, which should contain all publically available sequences. If you plug in Ian Musgrave's sequences in (as the ironically named Teleological has also done), you find that the same four sequences as Patrick are in there as coming from the synthetic bacterium. But, the final sequence, number 6, is also found in a natural M. genitalium, so it isn't artificially designed. Nobody at UD seems to have noticed this yet.

Two points amused me. Firstly, I was not surprised that there was a little trap - what's the point in setting a challenge like this if you make it easy? The second one is the way Patrick found the solution. Before his post, DLH had pointed to Venter's work on making an artificial bacterium. Patrick acknowledges that he has to use extra information to decide which is designed, and indeed argues that he needs to have this extra information. In other words he needs to know the identity of the designer. This is not good because ID claims that it is possible to detect design without knowing anything about the designer. There is a strong insistence that one shouldn't say anything about the identity of the designer. A cynical interpretation of this is that it is an attempt to avoid admitting that the designer is their Christian God, so that ID is creationism, and hence religion. So, Patrick has demonstrated an important point - in practical design detection one immediately tries to use as much information as one has about the putative designers. And why not?

Oh, silly me. Politics.

Big Grant Deadline Today!

2008-01-31T21:38:00.000+02:00

Today is the deadline for applications to the Finnish Academy, so scientists all over the country are busy writing abstracts and trying to find an amusing acronym for their project. The bad news for them is that they are up against fierce competition.

Yes, the beast will soon send off the description of his project on Integrated Pest Management For The Removal of the Coarse-haired Wombat (Vombatus ursinus) from Finland.

35% is a reasonable proportion of the grant for me to charge as an overhead, don't you think?

Not a bluff

2008-01-28T22:22:00.000+02:00

I got home late today (I might blog about why later), and found two postcards from the post office through my letterbox. For those of you who don't know, if the Finnish post office can't deliver a package, they put a postcard through your letterbox telling you to pick it up.
Anyway, I was expecting one package, with some books in it (CRC Press/Chapman and Hall was having a sale). But two? Anyway, I wandered down to the post office, just before it closed and picked the up. One was large and light, the other smaller but heavier and presumably contained the books. How wrong I was! The larger package was full of statistical goodness, and clear air. The smaller package had several copies of the February issue of Bluff Europe, evidently a poker magazine (and not as exciting as Buff Europe...). I don't even play poker - it's too close to work.
So, if anyone in Helsinki wants some hot tips from Sammy Farha - the coolest man in poker (huh? I thought that was someone else), just get in contact with me. You too can follow Phil Hellmuth's Fantasy Final Table!

Me? I'm just a lawnmower. You can tell me by the way I walk.

Must See Film!

2008-01-25T22:06:00.000+02:00

I wonder if this film is any good:

Gender Differences: Need More Data!

2008-01-22T22:05:00.000+02:00

I was asked about this last week by a colleague, and now it's hit the blogosphere, so I thought I would publicly leap into a dispute about sexism in science. And make a plea for people to actually look at their data.

This was all started by a group of biologists who have been working at NCEAS on publication biases in ecology (the biggest bias is, of course, that not enough of my papers get accepted straight away). They managed to get their latest results published in TREE.

The received wisdom is that there is a bias against women in science. One area where this might be seen is in acceptance of papers for publication – referees and editors might have a bias (conscious or subconscious) against women. If this is true, the proportion of papers published by women should be higher in journals where the gender of the author is not known.

For this and other reasons there have been suggestions floating around that journals shift to a system of double-blind reviews. At the moment most journals have single blinding: the authors' identities are known to the referees, but the referees' identities are not revealed to the authors (unless the referees wish to do so). In double blinding, the referee doesn't know the identity of the author. Hence, any bias due to gender of the author should be removed. So, if a journal shifts from single blinding to double blinding, the proportion of papers by female authors should increase.

In 2001 the journal Behavioural Ecology moved to double blinding. But did this change the proportion of female authors? Or, more exactly, was there a bias against women that was removed? After all, the proportion of female authors might be changing in the rest of science – the null expectation is that the change in Behavioural Ecology should be the same as in similar journal, rather than there should be no change. So, the group gathered data on the number of papers by male and female first authors from before and after Behavioural Ecology switched to double blinding for five similar journals too. And then they compared the change in proportion of female authors in Behavioural Ecology to that in the other journals.

Err, no.

What they did was to compare the change in the proportion of female authors in each journal to zero. They found was that Behavioural Ecology and Biological Conservation. had increases that were significantly different, but not the other journals. They therefore concluded that there was an effect of double blinding, and that the increase in Biological Conservation must have been due to other factors. Oddly, though, at no point did they seem to make a direct comparison. It is not clear that they looked at the data either. Had they done so, they would have seen this:

The lines show the change from before Behavioural Ecology went double blind to afterwards. The vertical lines are the standard errors. Behavioural Ecology is the thick black line. We see that the proportion of female authors increases in all of the journals, but also that it is greatest in Behavioural Ecology. But is that increase significantly (in any sense) greater than in the other journals? Well, comparing it to zero obviously inflates the estimate of significance, because the other journals are all also increasing.

We can get an idea about if the data show anything with a more focussed analysis. This is also simplified, but I an ignoring some variation, and a more sophisticated analysis (=too much hassle to explain) comes to the same conclusion (and yes, for those who have read the paper, so does including the "don't knows").

What we can do is calculate the difference between the before and after proportions of female authors for the “control group”, and estimate the distribution of differences that would be expected if there was no double blinding implemented. Then we can ask if the difference in the proportion for Behavioural Ecology falls so far outside this distribution that it would be unlikely to explain the change.

These are the differences:

Journal	Percentage Before	Percentage After	Difference (%)
Behavioural Ecology	23.7	31.6	7.9
Behavioral Ecology & Sociobiology	25.1	26.3	1.3
Animal Behaviour	27.4	31.6	4.2
Biological Conservation	13.8	20.6	6.8
Journal of Biogeography	14.4	16.5	2.0
Landscape Ecology	19.5	23.4	3.9

For the journals in black, the mean difference is 3.65%, with a standard deviation of 2.15%. If these were exact, then there would be a 95% chance that the change for another, similar, journal would be between -0.6% and 7.9%. So, Behavioural Ecology is right on the edge.

But it assumes that the variance is known. In reality it is estimated, and only estimated from 5 data points (i.e. not a lot). If we take this into account, we find that the prediction for a journal would fall between -2.3% and 9.6% (with 95% probability). Now Behavioural Ecology is reasonably well inside the limits. Even someone wanting to do a one-sided test will find it inside.

So, the analysis shows little evidence for any effect of double blinding. But there are a couple of caveats, which could have opposite effects. The first is simply that there is not a lot of data – only 6 data points. We would really need more journals to be able to come to any conclusion. In particular, there may have been some other changes at Behavioural Ecology that could have had an effect.

The second caveat is more subtle. Suppose you were a journal editor, and you introduce a rule that authors have to admit that statisticians are the highest form of life in their acknowledgements. After a couple of years, you notice that the proportion of authors called Fisher has increased. You wonder if this is because of the new rule. So, you compare it with other journals, and find no increase. You therefore declare that Coxes appreciate statisticians, but other people don't. But what about all those other effects you didn't see? What about the changes in numbers of Boxes, Coxes, and Nelders? Humans are very good at detecting patterns, but very bad at judging whether they are random. And using the same data from which you spotted a pattern to assess whether it is real is naughty – of course you're going to see an effect, because you've already noticed it in the mass of all possible things that could happen. Now, I don't know if the authors are guilty here – they don't say how they came to decide to examine this particular aspect of the data, but the introduction is a bit arm-wavy about the effect of double-blinding on sex ratio.

Of course, the solution to both caveats is simple – get more data. Anyone fancy trawling through the literature this weekend?

EDIT: Oops. I should have hat-tipped Grrlscientist for her post, which encouraged me to write this. Hedwig - I'm sorry. Please don't set Orpheus onto me...

Reference
BUDDEN, A., TREGENZA, T., AARSSEN, L., KORICHEVA, J., LEIMU, R., LORTIE, C. (2008). Double-blind review favours increased representation of female authors. Trends in Ecology & Evolution, 23(1), 4-6. DOI: 10.1016/j.tree.2007.07.008

Damn Lies in So Many Languages

2008-01-21T17:03:00.001+02:00

The ISI Glossary of Statistical Terms

Isn't this just great? You too can find out what the Afrikaans is for heteroscedasticity.

Seriously, this is the sort of resource that is really useful for a small number of people. The web is ideal for it. If only it was easy to find with Google.

I wanted to use the site to find out what the Finnish for "ordinal regression" is. Of course, it's one of the few terms they don't have. So, please tell me! Or tell me what it is in any other language that might be useful - help the ISI!

The Truth Revealed

2008-01-14T19:39:00.000+02:00

On the way home this evening I was musing about quantum mechanics. Now, I know little more than a layman, but I know enough to be aware that nobody really understands it. It struck me that it must have destroyed several promising academic careers - for someone with an analytical mind, it looks like something that you don't understand, but if you just learn a little ore, and try to put the different bits together then eventually the penny will drop, and it will all become clear.

In reality, of course, what happens is that you get sucked into it more and more, until you realise that there is no way you or anyone else will understand it. If you're lucky, that's after you have retired from a successful career as a theoretical physicist. But then the truth hit me...

One of the points they make about quantum mechanics is that it happens at very small scales, so we can never see it directly. Hence, they have to use instruments to measure the effects. With these, all sorts of bizarre and counter-intuitive result are found. But they are never observed directly. Perhaps this is no accident. Perhaps it was designed.

Here's my ~~just-so story~~ theory. Everyone knew that physicists were the only real scientists, finding Great Truths, and telling us that philately will get us nowhere (*). So, as a joke some poor technician decided to set up the latest instrument they were toiling away at to produce non-sensical results. The experiment was performed, and the results published. Thus was quantum mechanics began.

Of course, the technician couldn't admit to it, but that didn't matter. He just persuaded his friends and colleagues to play along with the joke, and build ever bigger instruments to produce nonsense and allow them to laugh behind the backs of these great Nobel-winning scientists. Then, someone must have mockingly suggested digging out the inside of a Swiss alp. And they even fell for that.

So, now we have a whole subject that makes absolutely no sense, because it is a fiction brought about by a conspiracy of instrumentalists. They can't admit it, because they have invested too much in the scam (as, come to that, have several governments). Instead, they go on building their machines to produce random results, safe in the knowledge that whatever odd squiggle is seen as an output, some theoretical physicist will be able to explain it with some weird theory (just stringing us along, eh?).

Of course, biologists would never fall for something like this, would they? After all, what could we be given by a technician? A machine that magically produces all the DNA we want? Who would fall for that?

(*) There. Two obscure references for the price of one.

Dyed in a blogging accident

2008-01-11T16:27:00.001+02:00

This post is for the dyslexic xkcd fans.

At the moment, Google gives about 1,650 hits for "died in a blogging accident", but it seems nobody has yet changed colour as a result of a mishap whilst posting their thoughts to the web.

Black Hole Found by Finns

2008-01-10T22:15:00.000+02:00

A Finnish team makes the news:
BBC NEWS | Science/Nature | Huge black hole tips the scales

Yes, I was the one who told the where to find Scunthorpe.

The On-Flight Service Was Terrible

2008-01-07T18:54:00.000+02:00

Finally, a good reason to work/live in a skyscraper.

I must admit to occasionally wanting to stand outside the World Trade Centre in Helsinki and throw paper aeroplanes at it.

Testing, testing

2008-01-04T21:50:00.001+02:00

OK, this is a test to see if my €7 was well spent....

Do we get an equation here?

As you can see, it's a mean equation.

EDIT: Grrr.... OK. Let's see what happens with a picture:

OK, now let's try some graphics

Hmmm, no Turner prize for me, eh?

EDIT: OK, there should be an equation there now. Which means I have a workaround. I'll explain more later, once I've got some feedback from the guys who took my €7 from me...

A physicist stumbles into a statistical field

2007-12-31T21:41:00.000+02:00

By a virtual game of Chinese Whispers (in which no Chinese were involved), I found out about a paper on arΧiv where a poor unsuspecting physicist wanders into a curious part of statistics. I'm actually something of a bystander in this area, but it's not going to stop me commenting on it.

OK, so the paper is by a guy called Bruce Knuteson, from MIT. He's interested in working out the scientific worth of a piece of empirical work, and being a physicist, he wants to measure it.

So, the first problem is to decide what worth is. Knuteson decides to measure it in terms of "surprisal", i.e. how surprised we are by a result. So, if we collected data, and got a result (say, a measurement of a parameter) x_i, how shocked would we be by it? From this, Knuteson decides that ...

The scientific merit of a particular result x_i should thus (i) be a monotonically decreasing function of the expectation p(x_i) that the result would be obtained, and (ii) be appropriately additive.

and so suggests -log(Pr(x_i)) as a measurement, as it has these properties. He then suggests that the worth of an experiment can be estimated as the expected value of this, i.e. the sum of -Pr(x_i)log(Pr(x_i)). This is a measure called entropy: something beloved of physicists and engineers, but rather opaque to the rest of us. The idea is that a larger entropy will mean that the experiment is better - we will expect more surprising results.

But is this a good measure? Perhaps a good way of tackling this is to view it as a problem in decision theory. How can we decide what is the best course of action to take when we are uncertain what the results will be? For example, if we have a choice of experiments we can carry out, how can we decide which one to do? To do this we first need to define "best". This has to be measured, and the numerical value for each outcome is called the utility, U. This might, for example, be the financial gain or loss (e.g. if we are gambling), or might be something more prosaic, like one's standing in the scientific community (however that is measured. h-index?). All the effects of each action, both positive and negative, go into this number. So, for example, we would include the gain in prestige from publishing a good paper, and the cost (e.g. financial, or the effect on our notoriety if the results are a turkey). The second part of the decision analysis is to give a probability for each outcome, so for action A the probability might be 0.3 that we get a Nature paper, and 0.7 that we get a Naturens verden paper. For action B it might be 0.9 and 0.1 respectively. We then calculate the average utility for each action, i.e. sum the probability of each result multiplied by the utility for that result.

This is what Knuteson does to get his measure. The problem is that his only utility is surprisal, and in general this doesn't make sense. Two things are missing. Firstly, there is no cost element. So, if we want to measure the time it takes an apple to fall on a physicist's head, it makes no difference if we pay a couple of students $1 or £30,000,000 to do it. The second problem is that there is no measure of scientific worth. Finding out if the next toss of a €1 coin is treated exactly the same as finding out if the Higgs boson is green.

This leads to clearly nonsensical results. If there are only two possible outcomes of an experiment, then the maximum expected surprisal occurs if the probability of one is 0.5. Therefore the optimal experiment is one with this property. For example, tossing a €1 coin. According to Knutsen, then, we should fund lots of coin tossing experiments (hmm, there's an Academy of Finland application deadline coming up).

The second thing that is missing is where the probabilities come from. These are probabilities of outcomes that are not observed, so in general they cannot be measured (without doing the experiment...). Therefore one has to assign them based on one's subjective opinion. Now we are on familiar Bayesian ground, and is something that has been argued about for years. But here I think Knutsen can use a sneaky trick to sidestep the problems. Put simply, he could argue that in practice the estimation of merit is made by people, so they can assign their own probabilities. If someone else disagrees, fine. This way, it is clearer where the disagreement lies (e.g. which probabilities are being assigned differently).

So, estimating the merit of a piece of work before it is done can be problematic (and I haven't touched on comparing experiments with different numbers of possible outcomes!). But Knutsen develops his ideas even further. How about, he asks, worthing out the merit of an experiment after it has been done?

Before doing this, Knutsen sorts out a little wrinkle. It is not generally the experimental results themselves that are of interest - it is how they impact on our understanding of the natural world. We can think about this in the way that we have several models, M₁, M₂, ... M_k, for the world (these might correspond to theories, e.g. that the world is flat, or that it is a torus). The worth of an experiment could then be measured in terms of how it changes what we learn about these experiments, i.e. how M_j changes with the data, x_i. This can simply be measured as the entropy of the models, the M_k's, rather than the experimental outcomes.

Knutsen goes through the maths of this, and finds that the appropriate measure of the merit of an experiment is a measure of how far the probabilities of each model are shifted by the experiment. To be precise, it is a measure known as the Kullback-Leibler divergence (I will spare you the equations). Now, this again is something that is familiar. A big problem in statistics is deciding which model is, in some sense, best. This can be done by asking about how well it will predict an equivalent data set to the one being analysed. After going through a few hoops, we find that the appropriate tool is the K-L divergence between the fitted model and the "true" model. Of course, we don't know the true model, but there are several teaks and approximations that can be made so that we don't need to - it is the relative divergence of different possible models that is important. The result of this is a whole bunch of criteria that are all TLAs with IC at the end - AIC, BIC, DIC, TIC, and several CICs.

The optimal experiment is the one which will maximise the difference between our prior and posterior probabilities of the different models (yes, Bayesian again). The idea is natural - the greater the difference, the more we have learned, and hence the better the experiment is. Of course, we still have the same problems as above, i.e. assigning the probabilities, and getting the utility right, but we are in the right area. Indeed it turns out (after browsing wiki) that the idea is not original - the method proposed by Knutsen is the same as something called Bayesian D-optimality. And (after reading the literature), the idea goes back to 1956!

So, does this help? For the general problem of estimating scientific merit, I doubt it. There are too many problems with the measure. It may be useful for structuring thinking about the problem, but in that case it it little different from using a decision analytic framework.

In experimental design, it is of more use, but then the idea is not original. The other area it might be useful is in summarising the worth of an experiment for estimating a parameter, such as the speed of light. There will be cases where physical constants have to be measured. Previous measurements can then be used to form the prior (there are standard meta-analysis methods for this), and then the K-L divergence of several experiments can be calculated, to see which gives the largest divergence. This is some way from the ideas Knutsen is thinking about (he explicitly rejects estimating parameters as being of merit!). But I think more grandiose schemes will die because of naysayers like me nagging at the details.

Reference
Lindley, D.V. (1956). On a Measure of the Information Provided by an Experiment. The Annals of Mathematical Statistics, 27, 986-1005.

Goodwill To All Man and Beast

2007-12-25T12:21:00.000+02:00

It's Christmas, so even beasts get to enjoy some peace...

Alas, the bottom of the sofa does not. Words will be had later.

Hyvää Joulua!

2007-12-24T22:24:00.000+02:00

Yes, it's time to wish you all a
Merry Christmas!

This advert, like Julebryg is a tradition in Denmark. Around Christmas you go to the cinema, and this advert comes on to be greeted by a huge cheer. If only the films were greeted with such enthusiasm...

But anyway, have a good Christmas and New Year, especially to you three regular readers.

Finnish Exports, Pt. 2

2007-12-22T18:30:00.000+02:00

After I posted the video on the Finnish export business, I thought I should post a saftey video, just in case you were thinknig of ordering an item.

Finnish Exports, Pt. 1

2007-12-19T21:38:00.000+02:00

It's almost Christmas, but not yet. So it's still worth posting this educational video about one of the major Finnish exports.

Although I should complain that our 'O' level geography teacher never told us about this. I guess he just wasn't competent. *sigh*

"It's the wrong scanner, Bruce!"

2007-12-15T15:30:00.001+02:00

Ouch.
BBC NEWS | UK | England | Norfolk | Hospital's scanner goes walkabout

Someone at Philips sent the wrong scanner to the wrong Queen Elizabeth Hospital (I guess it's easy to mix up Kings Lynn and Adelaide). But anyway, the BBC report includes this:

"There's probably a shipping clerk sitting in an office very miserable," QEH spokesman Richard Humphries said.

Yes, but which Queen Elizabeth Hospital does he speak for?

Oooh! Struck by a blog meme!

2007-12-12T17:24:00.000+02:00

GrrrrrlScientist struck me with first ever blog meme (woo-hoo!). I had to participate, but it took me a bit of time thanks to hardware problems and an influx of spawning cod. So. Here we go...

The Rules are as follows:

Link to the person that tagged you and post the rules on your blog.

Share 7 random and or weird things about yourself.

Tag 7 random people at the end of your post and include links to their blogs.

Let each person know that they have been tagged by leaving a comment on their blog.

Seven Random or Weird Things About Me:

I have a reputation for wearing t-shirts (and nothing over them) when it's cold, i.e. above freezing. The secret is to keep moving, folks.

I haven't cut my hair for 15 years, and it's still barely shoulder-length.

I use to bake bread in Danish.

Some films I have never seen: Bambi, E.T., Gone With the Wind, Casablanca.

There's a mistake in the first line of the introduction of the first paper I ever published. The moment I saw the reprint, I spotted it. Of course, I missed it in all of the drafts before then.

I have two chairs for my computer. That way, when the Beast sits in one, I gently shift it to one side and sit in the other.

I'm intrigued by the idea that internet memes are SIR epidemics.

Seven Random Bloggers whom I read;

Hermagoras at Paralepsis

Andrew Gelman (or one of his minions) Statistical Modeling, Causal Inference, and Social Science

John Wilkins and his ridiculously funny Evolving Thoughts

The Highly Allochronic Chris

Martin på Aardvarchaeology

Kristine the Amused Muse, and witch.

S.A. Smith, the ERV (and modern artist)

BBC report filtered out

2007-12-10T15:36:00.000+02:00

Hmmm. If you're reading this, your globus pallidus is being underused.
Actually, it's somehow comforting that there is a part of the brain dedicated to ignoring things. And another dedicated to deciding what's irrelevant. But somehow Henry always seems to slip past that one.

Focus on DIC

2007-12-05T09:34:00.000+02:00

This is something that's only of interest to Bayesians, so the rest of you can look away.
In a couple of analyses with BUGS, I've seen comparisons of DIC from different models, and the values have been almost exactly the same. This sort of thing is suspicious, and eventually I worked out why. I thought it was worth posting about this, so that everyone else can share The Secret.

It's best to explain the problem with an example (the complete R code for this is below). I simulated a couple of simple hierarchical models, so

Y_ij ~ N(θ_i, σ_y²)
θ_i ~ N(μ_i, σ_θ²)

(j =1,...,n, i=1,...,m. There are m groups, each with n observations). I then had two models for μ_i:

Model 1
μ_i = φ + β (i-5.5)

Model 2
μ_i = φ

The first model has a covariate (cunningly equal to the identity of the group), and the second has none. The data are plotted below. The effect of the covariate is clear, so DIC should be able to pick it up.

Now, I fit each of the models to each data set. This is easy running it through the R2WinBUGS package in R. From this I can extract the DIC:

Data 1
Model 1 DIC: 1464.0 pD: 10.9
Model 2 DIC: 1464.4 pD: 11.2

Data 2
Model 1 DIC: 1407.7 pD: 10.9
Model 2 DIC: 1407.7 pD: 10.9

So, in both cases the DIC is the same (for Data 2 the difference is in the third decimal place!). But for Data 1, Model 1 should be better - hey, we can see it on the figure! So, what's going on?

We can get a clue from plotting the posteriors for μ_i from the groups, from the two models:

Only the error bars are plotted (i.e. plus/minus 1 posterior standard deviation), and the 1:1 line is drawn in a fetching shade called "hotpink". Obviously the models are predicting the same means for the groups, and hence we will get the same deviance. We can see why this is happening from the group-level standard deviations (σ_θ², posterior means and standard errors in parentheses):

Data 1
Model 1: 0.87 (0.280)
Model 2: 4.4 (1.31)

Data 2
Model 1: 1.1 (0.34)
Model 2: 1.1 (0.31)

So, for the data where there is a trend, but none is fitted, σ_θ² is much larger - essentially, the lack of the linear trend is compensated by the increase in variance. The difference is not in the model for θ at all, but higher in the hierarchy (or hier in the higherarchy?).

Of course, this is obvious from looking at the models. The solution is to change the focus, from θ to φ and &beta. This then means calculating the marginal deviance, marginalising over θ, i.e. looking at P(Y | φ, β) and integrating over P(θ | Y). This can be done analytically (Hat-tip to David Spiegelhalter for correcting my errors, and refraining from making any justified comments about my mathematical ability!), whence we find that the deviance can be calculated because

barY_i. ~ N(μ_i, σ_y²/n + σ_θ²) )

when we do this, we get these results:

Data 1
Model 1 DIC: 26.9 pD: 2.7
Model 2 DIC: 57.8 pD: 1.7

Data 2
Model 1 DIC: 30.9 pD: 2.6
Model 2 DIC: 30.3 pD: 1.7

Now this makes more sense, for the data with an effect, the DIC massively favours the correct model. Without the effect in the data, the DIC is pretty similar. In both cases, also note that pD is larger by 1 for the model with 1 extra parameter. Which is what should happen!

What lessons can we draw from this? Firstly, that DIC is not an automatic panacea - you do have to focus it on the right part of the model. If the focus is not at the level immediately above the data (i.e. θ here), then you can't use the DIC given by BUGS. The correctly focussed DIC is more complex to get at: you have to calculate it yourself. For more complex models this might be awkward, if there are no analytical results, then the parameters to be integrated out have to be simulated, for example by MCMC. But this requires some further thought...

library(R2WinBUGS)
# Simulate data
Cov=1:10
Group = rep(Cov, each=50)
beta = 1
# With covariate
GrpMean1=rnorm(length(Cov), beta*Cov, 1)
Value1=rnorm(length(Group), GrpMean1[Group], 1)
DataToBUGS1=list(N=length(Value1), NGrp=length(Cov), Group=Group, Y=Value1)
# Without covariate
GrpMean2=rnorm(length(Cov), 5.5, 1)
Value2=rnorm(length(Group), GrpMean2[Group], 1)
DataToBUGS2=list(N=length(Value2), NGrp=length(Cov), Group=Group, Y=Value2)

# Plot the data
# png("C:/Bob/Blog/Data.png", width = 960, height = 480)
par(mfrow=c(1,2), mar=c(2.1,2.1,1.1,1.1), oma=c(2,2,0,0), las=1)
plot(jitter(Group), Value1, pch=3, col="grey70")
points(Cov, GrpMean1, col=1, pch=3, cex=1.5)

plot(jitter(Group), Value2, pch=3, col="grey70")
points(Cov, GrpMean2, col=1, pch=3, cex=1.5)
mtext("Group", 1, outer=T)
mtext("Y", 2, outer=T)
#dev.off()

# Write BUGS models to files
model1 <- function(){
for (i in 1:N){ Y[i] ~ dnorm(muGrp[Group[i]], tau.y) }
for (j in 1:NGrp){
muGrp[j] ~ dnorm(muG[j], tau.Grp)
muG[j] <- mu0 + betaGrp*(j-5.5)
}
mu0 ~ dnorm (0.0, 1.0E-6)
betaGrp ~ dnorm (0.0, 1.0E-6)
tau.y <- pow(sigma.y, -2)
sigma.y ~ dunif (0, 1000)
tau.Grp <- pow(sigma.Grp, -2)
sigma.Grp ~ dunif (0, 1000)
}
write.model(model1, "C:/Bob/Blog/model1.txt")

model2 <- function(){
for (i in 1:N){ Y[i] ~ dnorm(muGrp[Group[i]], tau.y) }
for (j in 1:NGrp){ muGrp[j] ~ dnorm(mu0, tau.Grp) }
mu0 ~ dnorm (0.0, 1.0E-6)
tau.y <- pow(sigma.y, -2)
sigma.y ~ dunif (0, 1000)
tau.Grp <- pow(sigma.Grp, -2)
sigma.Grp ~ dunif (0, 1000)
}
write.model(model2, "C:/Bob/Blog/model2.txt")

# Initial values
Inits1=list(list(mu0=0, betaGrp=1, sigma.Grp=5, sigma.y=1), list(mu0=2, betaGrp=0, sigma.Grp=5, sigma.y=1) )
Inits2=list(list(mu0=0, sigma.Grp=5, sigma.y=1), list(mu0=2, sigma.Grp=5, sigma.y=1) )

# Fit the models
# Data 1, Model 1
Data1Model1.post=openbugs(DataToBUGS1, inits=Inits1, c("mu0", "muGrp", "betaGrp", "sigma.y", "sigma.Grp"), model.file = "C:/Bob/Blog/model1.txt", n.chains = 2, n.iter = 11000, n.burnin = 1000, n.thin = 10)
# Data 1, Model 2 (no slope in model)
Data1Model2.post=openbugs(DataToBUGS1, inits=Inits2, c("mu0", "muGrp", "sigma.y", "sigma.Grp"), model.file = "C:/Bob/Blog/model2.txt", n.chains = 2, n.iter = 11000, n.burnin = 1000, n.thin = 10)
# Data 2, Model 1
Data2Model1.post=openbugs(DataToBUGS2, inits=Inits1, c("mu0", "muGrp", "betaGrp", "sigma.y", "sigma.Grp"), model.file = "C:/Bob/Blog/model1.txt", n.chains = 2, n.iter = 11000, n.burnin = 1000, n.thin = 10)
# Data 2, Model 2 (no slope in model)
Data2Model2.post=openbugs(DataToBUGS2, inits=Inits2, c("mu0", "muGrp", "betaGrp", "sigma.y", "sigma.Grp"), model.file = "C:/Bob/Blog/model2.txt", n.chains = 2, n.iter = 11000, n.burnin = 1000, n.thin = 10)
print(Data1Model1.post)
print(Data1Model2.post)
print(Data2Model1.post)
print(Data2Model2.post)

# Plot mu
# png("C:/Bob/Blog/Theta.png", width = 960, height = 480)
par(mfrow=c(1,2), mar=c(2.1,2.1,1.1,1.1), oma=c(2,2,1,0), las=1)
plot(Data1Model1.post$mean$muGrp, Data1Model2.post$mean$muGrp, type="n", main="Data 1")
segments(Data1Model1.post$mean$muGrp,Data1Model2.post$mean$muGrp-Data1Model2.post$sd$muGrp, Data1Model1.post$mean$muGrp, Data1Model2.post$mean$muGrp+Data1Model2.post$sd$muGrp)
segments(Data1Model1.post$mean$muGrp-Data1Model1.post$sd$muGrp, Data1Model2.post$mean$muGrp, Data1Model1.post$mean$muGrp+Data1Model1.post$sd$muGrp, Data1Model2.post$mean$muGrp)
abline(0,1, col="hotpink")

plot(Data2Model1.post$mean$muGrp, Data2Model2.post$mean$muGrp, type="n", main="Data 2")
segments(Data2Model1.post$mean$muGrp, Data2Model2.post$mean$muGrp-Data2Model2.post$sd$muGrp, Data2Model1.post$mean$muGrp, Data2Model2.post$mean$muGrp+Data2Model2.post$sd$muGrp)
segments(Data2Model1.post$mean$muGrp-Data2Model1.post$sd$muGrp, Data2Model2.post$mean$muGrp, Data2Model1.post$mean$muGrp+Data2Model1.post$sd$muGrp, Data2Model2.post$mean$muGrp)
abline(0,1, col="hotpink")
mtext(expression(paste(theta[i], ", Model 1", sep="")), 1, outer=T)
mtext(expression(paste(theta[i], ", Model 2", sep="")), 2, outer=T, las=0)
#dev.off()

####################################################################### DevCalc1=function(mcmc, data) {
NamesGrp=paste("muGrp[", data$Group, "]", sep="")
-2*sum(dnorm(data$Y, mcmc[NamesGrp], mcmc["sigma.y"], log=TRUE))
}
DevCalc2=function(mcmc, data) {
Mean=unlist(tapply(data$Y, list(data$Group), mean))
Mu=mcmc["mu0"]
N=length(data$Group)/length(unique(data$Group))
-2*sum(dnorm(Mean, Mu, sqrt((mcmc["sigma.y"]^2)/N + mcmc["sigma.Grp"]^2), log=TRUE)) }
DevCalc2beta=function(mcmc, data) {
Mean=unlist(tapply(data$Y, list(data$Group), mean))
Mu=mcmc["mu0"] + mcmc["betaGrp"]*(as.numeric(names(Mean)) - 5.5)
N=length(data$Group)/length(unique(data$Group))
-2*sum(dnorm(Mean, Mu, sqrt((mcmc["sigma.y"]^2)/N + mcmc["sigma.Grp"]^2), log=TRUE))
}
DIC.calc=function(dat, mcmc, func) {
Dev=apply(mcmc$sims.array, c(1,2), func, data=dat)
mean=apply(mcmc$sims.array, 3, mean)
pD=mean(Dev) - func(mean, dat)
DIC=mean(Dev) + pD
return(list(DIC=DIC, pD=pD, Dbar=mean(Dev)))
}
PrintDIC=function(DIC, Label) { cat(Label, " DIC:", DIC$DIC, " pD:", DIC$pD, "\n")}

# Focus on theta
DIC.D1M1=DIC.calc(DataToBUGS1, Data1Model1.post, DevCalc1) DIC.D1M2=DIC.calc(DataToBUGS1, Data1Model2.post, DevCalc1)
DIC.D2M1=DIC.calc(DataToBUGS2, Data2Model1.post, DevCalc1)
DIC.D2M2=DIC.calc(DataToBUGS2, Data2Model2.post, DevCalc1)
PrintDIC(DIC.D1M1, "Data 1, Model 1")
PrintDIC(DIC.D1M2, "Data 1, Model 2")
PrintDIC(DIC.D2M1, "Data 2, Model 1")
PrintDIC(DIC.D2M2, "Data 2, Model 2")

# Focus on phi
DIC2.D1M1=DIC.calc(DataToBUGS1, Data1Model1.post, DevCalc2beta)
DIC2.D1M2=DIC.calc(DataToBUGS1, Data1Model2.post, DevCalc2)
DIC2.D2M1=DIC.calc(DataToBUGS2, Data2Model1.post, DevCalc2beta)
DIC2.D2M2=DIC.calc(DataToBUGS2, Data2Model2.post, DevCalc2)
PrintDIC(DIC2.D1M1, "Data 1, Model 1")
PrintDIC(DIC2.D1M2, "Data 1, Model 2")
PrintDIC(DIC2.D2M1, "Data 2, Model 1")
PrintDIC(DIC2.D2M2, "Data 2, Model 2")

Finnish curiosities

2007-12-04T13:02:00.000+02:00

The Finns are a curious people. This morning I got a phone call asking me if I owned a TV.
I don't. End of conversation. We actually spent longer ascertaining that the conversation had to be in Enlgish.

The Beast is Happy

2007-11-24T18:22:00.000+02:00

The beast is evil. How can you not like him when he's this happy?

But why is he so happy...

Because he has a new prime minister. I told him the guy's name is Rudd, and he thought it was great having a fish as PM - if he's a failure, some lucky moggie can eat him.

I guess this explains why he was never impressed with Bush in the US - he has a self-cleaning litter tray already.

Damn, it was only spam

2007-11-12T10:48:00.001+02:00

I've just cleaned up my RNI mailbox, and amongst the spam I got these two subjects:

Drum Snail Bible Necklace Festival

Snail Foot Dung Rope Leather jacket

Is the leather jacket made from a drum snail, one wonders.

Thrilling

2007-11-04T20:45:00.000+02:00

Some people have far too much time on their hands. They could be doing something profitable, like writing a blog. Instead...

They re-make Jacko's Thriller in lego.

Incidentally, I came across this whilst trying to find a photo to illustrate oozing.

Chasing The Beast back to its lair

2007-10-28T21:08:00.000+02:00

After the Wombat's success, we chased the beast back to its lair (every beast has to have a lair). This is all we could see:

Neither of us were brave enough to go any further.

The Wombat Strike Back

2007-10-22T19:51:00.000+03:00

For those of you worrying about the wombat and the beast, there is good news. The wombat has been learning a few moves.

The beast was beaten off (at least temporarily).

Jerry Fodor fails Evolution 101

2007-10-17T20:15:00.000+03:00

The latest "fun" on the evolutionary (pro and anti) parts of the web has been discussion of an article in the London Review of Books by Jerry Fodor. In it he proclaims that natural selection is on its way out. Alas for him, his argument is based on an impressive ignorance of evolutionary biology. Jason Rosenhouse has done a good job taking down this mess, but still left something for the rest of us.

Fodor's claim is that there are two problems with theories of evolution by natural selection, one conceptual, one empirical. The conceptual problem first:

Here’s the problem: you can read adaptationism as saying that environments select creatures for their fitness; or you can read it as saying that environments select traits for their fitness. It looks like the theory must be read both ways if it’s to do the work that it’s intended to: on the one hand, forces of selection must act on individual creatures since it is individual creatures that live, struggle, reproduce and die. On the other hand, forces of selection must act on traits since it is phenotypes – bundles of heritable traits – whose evolution selection theory purports to explain. It isn’t obvious, however, that the theory of selection can sustain both readings at once.

Here he's bringing up a well-known issue, part of the levels of selection debate. The complete debate is more extensive than I'll describe here, and is largely solved. The problem that Fodor is focussing on is that genes code for traits - a gene might affect colour of a bear, for example, with one allele making it white. But, it is the whole bear that lives or dies. What, then, is being selected? Fodor:

It couldn’t, for example, be literally true that the traits selected for are the ones Mother Nature has in mind when she does the selecting; nor can it be literally true that they are the traits one’s selfish genes have in mind when they undertake to reproduce themselves. There is, after all, no Mother Nature, and genes don’t have, or lack, personality defects. Metaphors are fine things; science probably couldn’t be done without them. But they are supposed to be the sort of things that can, in a pinch, be cashed. Lacking a serious and literal construal of ‘selection for’, adaptationism founders on this methodological truism.

Of course, we do have an explanation, and one which does not rely on metaphor. Instead, we use mathematics. The ideas were developed by George Price, and the main result is the Price Equation. In the simpler form, it says that the change in the average value of a trait is equal to the covariance between the trait and fitness. Now, as fitness is measured on individuals, we have the explicit connection between a trait and selection. The more complex form of the Price equation allows for changes within individuals (e.g. if the environment changes, and this causes a change in the traits).

Price's equation helps us get Fodor out of the dilemma he's talked himself into:

Maybe one can, after all, make sense of mindless environmental variables selecting for phenotypic traits. ... The crucial test is whether one’s pet theory can distinguish between selection for trait A and selection for trait B when A and B are coextensive: were polar bears selected for being white or for matching their environment? Search me; and search any kind of adaptationism I’ve heard of. Nor am I holding my breath till one comes along.

Poor Fodor is going to asphyxiate as he waits for a theory that went shooting past him in the early seventies. Put simply, it doesn't matter whether we can distinguish between selection for traits A and B if they're correlated. Price tells us what will happen (and, of course, the Price is right). Some polar bears were whiter than white, and this meant that they were more likely to survive, and hence whiteness was correlated with fitness. Others presumably were a bluey-whiteness you'll really like, but this was correlated with a lower fitness. Hence that colour reduced in frequency. We can even deal with evolution of trait A when trait B is under selection - just follow the covariances!

Fodor's empirical problem is a wonderful straw man, dressed up in word salad. Follow this if you dare:

Adaptationism is a species of what one might call ‘environmentalism’ in biology. (It’s not, by any means, the only species; Skinnerian learning theory is another prime example.) The basic idea is that where you find phenotypic structure, you can generally find corresponding structure in the environment that caused it. Phylogeny tells us that phenotypes don’t occur at random; they form a more or less orderly taxonomic tree. Very well then, there must be nonrandomness in the environmental variables by which the taxonomic tree is shaped. Dennett has put this idea very nicely: ‘Functioning structure carries implicit information about the environment in which its function “works”. The wings of a seagull . . . imply that the creature whose wings they are is excellently adapted for flight in a medium having the specific density and viscosity of the atmosphere within a thousand metres or so of the surface of the Earth.’ So, phenotypes carry information about the environment in which they evolved in something like the way that the size, shape, whatever, of a crater carries information about the size, shape, whatever, of the meteor that made it. Phenotypes aren’t, in short, random collections of traits, and nonrandomness doesn’t occur at random; the more nonrandomness there is, the less likely it is to have been brought about by chance. That’s a tautology. So, if the nonrandomness of phenotypes isn’t a reflection of the orderliness of God’s mind, perhaps it is a reflection of the orderliness of the environments in which the phenotypes evolved. That’s the theory of natural selection in a nutshell.

Now, what he's trying to say (and I'll spare you the rest of the section) is that evolution is constrained - there are some things that a species can't evolve into, because they is no way of developing in that direction:

For example, nobody, not even the most ravening of adaptationists, would seek to explain the absence of winged pigs by claiming that, though there used to be some, the wings proved to be a liability so nature selected against them. Nobody expects to find fossils of a species of winged pig that has now gone extinct. Rather, pigs lack wings because there’s no place on pigs to put them. To add wings to a pig, you’d also have to tinker with lots of other things. In fact, you’d have to rebuild the pig whole hog: less weight, appropriate musculature, an appropriate metabolism, an apparatus for navigating in three dimensions, a streamlined silhouette and god only knows what else; not to mention feathers. The moral is that if you want them to have wings, you will have to redesign pigs radically. But natural selection, since it is incremental and cumulative, can’t do that sort of thing. Evolution by natural selection is inherently a conservative process, and once you’re well along the evolutionary route to being a pig, your further options are considerably constrained; you can’t, for example, go back and retrofit feathers.

I wonder in Prof. Fodor knows what chiroptera are? Of course, Fodor is right that there are developmental constraints. But so f*cking what? Of course that restricts how a species might evolve - it's one of the more powerful arguments for evolution by natural selection. It's why bats don't have feathers - they didn't evolve them, but found another way of taking to the air. This is why Fodor's problem is so silly - we know that there are endogenous constraints on species, but we also see that they still adapt. They work their way around problems the best they can, even if it means having a nerve 10 to 15 feet longer than it has to be. Fodor talks about species developing along one path, and that cutting out others, but why does this stop adaptation? I can't think of anyone who would have problems with this - hey, even game theorists understand it.

Apparently Fodor is writing a book with Massimo Piattelli-Palamarini about evolution without adaptation. Somehow I don't think it'll be about genetic drift.

Lunacy and the Planets

2007-10-15T16:22:00.001+03:00

I was just clearing my inbox, and found this in Science:

Excitation of Lunar Eccentricity by Planetary Resonances -- Cuk 318 (5848): 244 -- Science
I assume this means that lunacy and astrology are linked. Or perhaps that the star signs of werewolves are important.

It almost makes me feel homesick

2007-10-12T10:29:00.000+03:00

In today's ScienceExpress (the online first service of Science):

Widespread Morning Drizzle on Titan
Máté Ádámkovics, Michael H. Wong, Conor Laver, Imke de Pater

Evidently the first man (or woman!) onto Titan should be English. The bad news, though, is that the rain is of methane not water. I haven't tried, but I'm guessing the tea won't taste as nice.

An epic struggle

2007-10-09T21:07:00.000+03:00

The reinforcements have started their action.

At first, the beast didn't know what was coming

"What are you cheering for? Is something going on?"

"OK, that's another round of shredded sofa later."

"*sigh*. I suppose I'll have to wait for the two of you to finish before starting on the carpet."

Ha-wha'?

2007-10-08T20:38:00.000+03:00

Whilst going through my backlog of email, I went through the Science table of contents. It included a paper with this title:

Major Australian-Antarctic Plate Reorganization at Hawaiian-Emperor Bend Time

OK, I recognise every word, but don't expect to see them in that order.

On reflection, I think it makes more sense if we assume there's a typo, and "Bend" is actually "Bed". Quite why antipodeans would want to reorganise their china at that time of day I'll let someone else explain. Or if anyone has a better (if not more accurate) interpretation, please tell! Bonus marks if it involves hedgehog pathways.

Reference (for those who care): Whittaker et al. (2007) Science 318: 83-86 DOI: 10.1126/science.1143769

Back again

2007-10-08T10:00:00.000+03:00

I've been away for a few days on a course in Estonia, on management and supervision of academic research. This has spawned a few random thoughts:

1. If you take your laptop, remember the power cord. It's more important than the mouse.
2. As I might have expected, some of what we were told I had already worked out, but some I hadn't. It's nice to know which is which. There were also a few ideas that I hadn't thought about.
3. Sharing a room with someone who snores is not a good idea. And going down with 'flu isn't sufficient for revenge.
4. Management theory can actually be useful - obviously there is a lot that is common sense, but having it organised does help. In particular, there was a discussion about project management. In classical project management, one sets out the stages that are needed to complete the task (say, build a bridge), and set out the schedule by working backwards from completion, to decide when each task should be scheduled. I guess this is used all over the place, and is one of the reasons western society works (shocking, isn't it?). Working out how to take an organised approach to this sort of task, and then how to teach this to managers is actually a good thing. IOW, MBAs are not necessarily useless.
5. Thanks to one of the speakers, I'm now reading text with a Welsh/Swedish accent. Should I seek medical help?
6. I hope my students don't mind doing a lot of writing.
7. If you find yourself on the M/S Star between Helsinki and Tallinn, don't try the pizza from the fast food place. It's awful (and remember, I'm English, so I know bad food) - a slab of congealed artificial cheese with chicken and battery-farmed pineapple. It was called Hawaij, presumably because they feared a law suite for defamation from the good people of Hawaii. The people of Four Seasons do not seem to have as much of a reputation for litigation.
8. I don't know what happened in the rugby World Cup on Saturday. I think the media must be lying to us. Ah well, an England-France semifinal, just like last time.

Remind me, who were in the other semifinal in 2003?

Reinforcements

2007-10-01T21:35:00.000+03:00

I have finally got some help to defend my computer from The Beast:

I don't know if a wombat is the best animal to help, but we're off to a good start. Incidentally, I think it might be a Fijian wombat.

My Week in Pictures

2007-09-29T15:58:00.000+03:00

I had a good week work-wise this week, I got a few things to work. I also realised that I can pretty much summarise it in pictures.

The first is from something I was playing with the previous Friday afternoon, seeing if I could get the ODE solver in BUGS to work. For this I fitted the Lotka-Volterra equations to some famous data on Lynx-Hare cycles from the Hudson bay Company. I got it working this week, and the model fits reasonably well:

I'm happy with the lynx fit, but the hares aren't great - I might have to add carrots into the model. But considering how simple the model is, it's pretty good.

The second result is something I've been working on for some time, a multi-trait QTL analysis. This is genetics - trying to locate genes that affect different traits. We've been looking at models that can include several traits in the same analysis. Here I plotted what is roughly the probability that each locus (on the x-axis) affects each trait (each line).

Locus 12 seems to be affecting several traits - yes, we have pleiotropy! The numbers aren't totally convincing, but it looks like there's something there.

At the same time, I've also been doing another predator-prey analysis, on voles and weasels. That worked as well, and this is a plot showing that the interactions are local:

The ideas is that if the circles overlap, there is a correlation between the sites. Actually, this interpretation may be a bit dodgy, but the main thing that comes out of this is that most of the sites are behaving independently.

Finally, this Friday's afternoon playing around involved more ODEs, modelling the numbers of butterflies over a season. This was fairly quick to fit, although it could behave better.

It also needs a bit more work on it (hey, it was only about an hour's work!), because there is more variation than the model accounts for.

Other than showing I had a good, interesting week, does this really say anything? One thing is that none of the graphs will look like that in their final versions - these are all produced as part of the process of doing the analysis. But "quick and dirty" graphs are still very useful for looking at the results, and getting a feel for what's happening. It also shows the worth having packages with good, flexible graphing functions - two of the plots are standard BUGS plots, the other two are drawn in R. Plots are also an easy way of showing collaborators what you've done - I've drawn several other plots this week, and not all will end up in the final write-ups of the work.

Now I just have to write all this up.

Training the Beast

2007-09-28T09:20:00.000+03:00

It's a generally held view that cats are independent, and hence can't be trained beyond the litter tray. Well, this is false even for older cats.

I knew I had to take Jack's training slowly, so I started with the order "Eat!". He learned that one surprisingly quickly. We've now progressed onto "Squeak!", and he's doing very well at that now.

It's all matter of timing.

Rechtschreibung ist schwierig

2007-09-24T13:44:00.001+03:00

I just read this in a discussion on Pharyngula

Go easy on him; spelling is hard.

Even Einstein, the smartest man in the world managed to get "i" before "e", except after "c" wrong twice in his own name.

Posted by: John McKay | September 24, 2007 12:33 AM

Sheer class, and one I'll have to remember to steal.

Re-Cell Cycling Front-loading Pt. II

2007-09-22T18:18:00.001+03:00

Remember Dover? For those of you who don't, it was the latest stand by creationists to get creationism into schools (under the guise of "intelligent design"). At the centre of the case was a text designed for schools called Of Pandas and People. During Michael Behe's testimony, he was presented with a quote from the new version of the text, which is still in preparation:

Sudden emergence holds that various forms of life began with their distinctive feature already intact, fish with fins and scales, birds with feathers and wings, animals with fur and mammary glands.

I bring this up because I want to continue where I left off fisking this paper

Sherman, M. (2007). Universal genome in the origin of metazoa. Cell Cycle 6: 1873-1877. Link

Previously, I went through the evidence Sherman put forward to suggest that there was a problem for evolutionary biology, and humbly suggested that it may not be as much as a problem as he thought, and that if he was to make sweeping statements, he might like to support them. So, now let's get on to what he suggests explains the problems he sees. In a nutshell, it is sudden emergence with huge brassy shiny knobs on.

This is what he writes:

Here I propose a hypothesis that answers the questions posed above, and offers experimentally testable predictions. This hypothesis postulates that (1) shortly (in geological terms) before Cambrian period a Universal Genome that encodes all major developmental programs essential for every phylum of Metazoa emerged in a unicellular or a primitive multicellular organism; (2) The Metazoan phyla, all having similar genomes, are nonetheless so distinct because they utilize specific combinations of developmental programs. In other words, in spite of a high similarity of the genomes in phyla X and Y, an organism belonging to phylum X expresses a specifc set of active developmental programs, while an organism belonging to a different phylum Y has a distinct set of "working" programs specifc for phyla Y.

(as in my previous post, all grammatical mistakes are in the original. I don't want to indicate them all, so let's just give Sherman a sic note)

In other words, every species had every gene, but not all of them are used. Now, to experienced watchers of ID on the blogosphere, this is a familiar notion, John A. Davison's Prescribed Evolutionary Hypothesis. Davison is a crackpot, but let us not judge Sherman on those grounds. Certainly, Sherman has thought through his ideas more, and is probably a sane, normal person.

So, Sherman's idea is that a Universal Genome appeared in the Cambrian, causing an explosion. Since then, the process of evolution has just been one of changes in the switches in pre-existing developmental programmes. Sherman implies that these changes are not genetic (as all organisms started with the same Universal Genome), so it's not awfully clear how he thinks developmental changes have occurred. Perhaps he should throw some epigenetics into the mix.

So, how did the Universal Genome appear? Ah, Sherman says nothing. It's taken as a given that supporters of ID will say that it can't say anything about the identity of the designer, but this is going one step further - stuff just appeared without mentioning the possibility of a designer. You see, it's Sudden Emergence, only without the mammaries.

Sherman quickly seeks to address what he sees as a fundamental problem: if the universal genome was in all Metazoa initially, why don't we see it in all now? The solution is obvious - genes have been lost over time:

A beautiful illustration of such a loss is a Wnt gene family. In humans, there are nineteen Wnt genes belonging to twelve families. In Hydra. on the other there are two Wnt genes that correspond to two families found in humans. Simple analysis of this finding within the framework of the classical model suggests that additional human genes have developed from ancestral Wnt genes found in Hydra. However, Anemona that belongs to a distinct branch of Cnidaria has eleven Wnt genes belonging to eleven families found in humans. Therefore, it is quite obvious that many Wnt gene families, possibly the entire set, exists in the gene pool of the primilive Metazoan phylum, and various members of Wnt families were lost in different species within this phylum.

Waste not, Wnt not.

Accordingly, the proposed model predicts that in various groups of Cnidaria we will find many diverse gene families that function in more advanced phyla.

Or that they have also become lost.

Sherman expands on his ideas:

The "Universal Genome" hypothesis does not contradict any well-established data on the genetic evolution (e.g., gene duplications or accumulation of mutations, molecular clock. etc). but suggests that genetic evolution could shape and improve function of developmental programs.

It's a pity Sherman missed out all that well know data about large chunks of DNA suddenly appearing in a genome, without any immediate function. I'm a bit sceptical about his suggestion that the hypothesis doesn't contradict data on the accumulation of mutations, so let's see how he defends that:

...

Yep, that's how he does it. Once more, make a bold statement, don't give any argument or evidence to support it and move on. For the moment, so shall we.

Sherman fills in a couple of details (including a bit of special pleading for genes have only been lost is some lineages - there must be a special mechanism for their conservation in others. Again, just a a statement, not backed up), and then gets on to the "this is really science" part:

There are two main testable predictions of the presented hypothesis, which are absolutely critical for validation of the model; (1) full or parts of the developmental programs characteristic to higher taxons must be encoded in genomes of lower taxons, and (2) blocks of genetic information encoding these developmental programs in more primitive taxons must be useless in these taxons.

What happened to waste not Wnt not? Sherman is saying that primitive organisms must still carry genetic code for more advanced developmental programmes. But he's already accepted that they can lose genes. So, (1) can't be critical. (2) is more interesting - one would need to show that the genetic information was present, but not expressed in the "primitive" taxa. Sherman, of course, doesn't suggest this. Instead, he tells us that the common ancestor of Arthropoda and Chordata didn't have eyes (as we have already seen, this is not quite right), but yet jellyfish have eyes, and they are controlled by similar genes to those in Drosophila. Oh, and as part of this argument, he points out that jellyfish don't have a central processing unit like a brain, so it is unclear how they could process and integrate what they see. Once again, because we are ignorant of something, it can't possibly happen.

Sherman also suggests that we could try and induce development of these more advanced programmes, for example by over-expressing Pax6 in the sea urchin, and seeing whether it develops eyes (and becomes a see urchin, I suppose). It's not clear to me why over-expression would make a difference: we already know that it is expressed in the sea urchin's foot. Without, apparently, an eye being developed. Could it be that other genes involved in eye production are missing? The public needs to know!

We also get another prediction:

Another indication that latent developmental program is present in a lower taxon would be expression of such a program in higher taxons derived from the lower one in a seemingly convergent processes. For example a possible experiment would be to activate development of circulation systems of mammalian or bird types in lizards or even in Xenopus [a frog]. The circulation systems in mammals and birds appear to be very similar, however, they developed from the reptilian system independently in these taxons. Therefore it seems likely that Reptilia possess the program of development of the circulation system of the mammalian/bird type and requires only a minor switch to activate it.

This sounds like we would end up with a reptile that was developmentally very confused. Note we're told that Reptilia have the programme for development, but we're not shown any evidence. Where are the homologs and orthologs for the genes? Is Sherman unable to BLAST the Xenopus genome to see if the genes are there? (in case you're wondering, I'm too lazy to chase this up myself).

Sherman suggests his second prediction could be tested by deleting genes and seeing if there is a physiological effect, e.g. the genes for the adaptive immune system in the sea urchin. This makes sense (although you might have to attack the sea urchin with a pathogen afterwards, to see if it responds). Except that it's not clear what one would conclude if nothing happened - Sherman has already discussed the possibility that genes are lost, so he could claim that that's what has happened in this case. In other words, the predictions don't provide potential falsification (although if they were found to be correct, they would provide a powerful verification of the idea).

So, we have a paper that makes some dodgy claims from ignorance that evolution can't explain the Cambrian explosion or the evolution of body plans, followed by and alternative hypothesis which explains nothing that can't be explained by evolutionary biology, relying on gaps in our knowledge to create doubt. And it says nothing about the elephant in the room - how sudden emergence happened. The key part of the hypothesis - how developmental information appeared - is just stated and then left aside.

The whole premise of Intelligent Design as science was that one could investigate design without asking about the designer (because, obviously, that would mean admitting you thought the designer was the god of Abraham). Sherman has taken this to the next stage - he doesn't even mention the possibility of a designer.

I mentioned in my first post that I was suspicious about this paper. That was because the grammar looked odd (the mistakes were in simple grammar, but complex structures were correct). That was before I read this, from the Disco Institute (p20) (pdf):

On the other hand, reading the papers on evolution published in respectedscience journals like Proceedings of the National Academy of Science or Nature, one is surprised at the weakness of the arguments. Indeed, the standards of proof in the field are much lower than in the rest of biology. Such papers would never make it through the peer-review process if they concerned molecular or cellular biology. Of course, there are obvious reasons for such low standards, including the difficulty of testing evolutionary hypotheses through experimentation. But if the theory is based on poor arguments, why have criticisms of it not succeeded in convincing mainstream scientists?

Poor arguments? Ha ha ha ha! Now, this guy must be spoofing us, and the DI.

As for the jourmal, it is asking to be spoofed. This is part of the journal's explanation of why one should publish there:

Rapid response to presubmission
inquiries (usually within the hour). Most papers are rejected without
external review. (papers send for external peer-review are expected to
be published). During presubmission inquiry, reviewers will be
contacted to accelerate further review. Authors are encouraged to
suggest and decline potential reviewers.

Ultra-rapid peer-review (usually within one-two days)

Papers
rejected from other top journals (e.g., Nature, Science, Cell), if the
authors choose, may be submitted with previous reviews and decision
letters. This allows for the consideration of a paper without sending
for additional review.

So, the journal takes an hour to decide if a paper is good enough - if it's sent out to review, then it will have to be pretty crap to be rejected. And there is pressure on the referees to comment on a manuscript very quickly. This must have a negative impact on the quality of the refereeing - sometimes you have to go through a paper carefully, and spend time checking references, and also thinking about it - often I need time to work out why I'm not sure about a paper, or to work out what to recommend. The final bulletpoint says to me that the journal is desparate - they want good papers (doesn't every journal?), so they are prepared to cut corners to do so.

The journal is desparate to do things quickly, so it looks like if you wanted to get a dodgy or hoax paper published, this is a good journal to do it with. I hope that is what happened. Otherwise Michael Y. Sherman will have to justify why he can submit a paper in whcih he ignores basic norms of writing science - you know, backing up your argument with evidence. And whether this is a hoax or not, Cell Cycle has to justify how it can publish paper which nobody there has even read properly.

EDIT: Forgot to hat-tip Albatrossity for the pdf. Thanks!

Why We Admire the French

2007-09-21T21:44:00.000+03:00

As we're discussing rugby, I thought it was about time I dug out one of those moments that you don't mind being on the receiving end of.

Oh, and for any Aussies out there, you can guess what's below the fold.

I think it says something that the greatest moment of English rugby is a fly-half kicking a drop goal.

My lips are sealed

2007-09-19T19:13:00.000+03:00

I've just come back from a meeting at a pharmaceutical company. If you want to know what we discussed, you'll have to ask me in twenty years' time. Mind you, I will have forgotten it by then.

More on the Beast

2007-09-18T21:13:00.000+03:00

The beast is still around. Sometimes I can ward him off with a fluffy purple thing on a stick, but he returns, with evil intent etched across his face:

Even when he's supposedly resting, I know he's still plotting to keep me away from the blog:

I wonder - which one of you is paying him to do this?

What ecologists discuss

2007-09-17T21:03:00.000+03:00

I've just got an email from an ecology listserv with the intriguing title of:

Inducing vomiting in salamanders

Alas it's asking for instructions, not giving.

Re-Cell Cycling Front-loading Pt. I

2007-09-17T18:29:00.000+03:00

Last week, my bestest friend DaveScot put up a post at Uncommon Descent (the intelligent design blog of William Demski and other illuminaries) about a paper on front-loading. This is an idea that DS is keen on - that there was an ur-cell that had all of the instructions necessary for all of life, and these were turned on at the right time to produce whatever The Designer wanted to appear. I thought it was worth having a look at the paper, if only to stave away boredom. This is the citation:

Sherman, M. (2007). Universal genome in the origin of metazoa. Cell Cycle 6: 1873-1877. Link

The paper advances a suggestion that goes totally against mainstream evolutionery biology, and is therefore nuts and wrong.

It's almost tempting to stop there, but I doubt anyone would get the joke. So, I'll use a different rhetorical strategy to Sherman, and if I make any grand statements, try to back them up with evidence and argument.

Before laying into it properly, I should state that the paper should never have been published in the form it was. The grammar is awful. I'll comment more on this after I've finished with the text - it makes me a bit suspicious about the whole thing. For the moment, it is enough to point out that the grammatical mistakes in the quotes are in the original, and if I were to acknowledge all of them with the usual sic, this post would look like a vomitorium.

The paper starts by laying out some facts that the author thinks need to be explained:

(1) seemingly simultaneous appearence of paleontological remains of all presently existing Metazoan phyla, both simple and advanced; (2) similarities of genomes among Metazoan phyla of diverse complexity; (3) seemingly excessive complexity of genomes of lower taxons; (4) similar genetics switches of functionally similar but non-homologous developmental programs.

Let's take these one by one:

(1) seemingly simultaneous appearence of paleontological remains of all presently existing Metazoan phyla, both simple and advanced. OK, this one is easy - it's boiler-plate creationism. CC300 and CC301 (go to the links for rebuttals).

(2) similarities of genomes among Metazoan phyla of diverse complexity. Grr, now I have to do some work. Sherman points out that some genes (or rather their orthologs) are found in diverse taxa, and not always doing the same thing. He states:

...one does not expect to find genes responsible for development of bilateral organisms in primitive Metazoa with radial symmetry. Surprisingly, such genes, e.g., orthologs of hox genes, were found in Cnidaria, and furthermore they are expressed in Cnidaria in an asymmetric manner, as if to define segments in these radial organisms.

Why is this unexpected? Sherman does not explain. Perhaps he hasn't heard of common descent. Or co-option, where genes that have one function are used to do something else (any intelligent intelligent design supporter should know that the bacterial flagellum took some of its structure from the Type III Secretory System). Sherman does discuss genes changing function over evolutionary time:

A possible response to these arguments within the classical model would be a suggestion that the genes responsible for eye development in Arthropoda or vertebrates serve different functions in lower taxons (so-called gene sharing). In fact, several examples of gene sharing have been described, e.g., recruiting of small heal shock proteins to serve as crystallines. These examples, however, are exceptionally rare, and it is unclear whether they indeed can be responsible for making de-novo complex developmental programs serving unrelated functions.

So, Sherman, if this is exceptionally rare, why does Conway-Morris declare co-option to be "rampant" (pdf)?

...co-option and redeployment are rampant both in a developmental
context (e.g. Eizinger et al., 1999; Heanue et al., 1999 (see also Relaix and Buckingham, 1999); Merlo et al., 2000; Damen, 2002; Locascio et al., 2002; Lowe et al., 2002; Fabrizio et al., 2003) and in related topics such as those concerned with enzymatic pathways (e.g. Peregrin-Alvarez et al., 2003).

Look, look! Conway-Morris acts like a fusty old academic and gives citations! Curse the man for making it so easy to check his assertion!

Of course, it could be that Sherman is unaware of this work, because he hasn't read this paper. Except ... it's cited in his paper. Not that that means much (find the Know-Thine-Own-Self Results).

It's around here that Sherman makes a comment so factually wrong even I spotted it. He writes

In fact, many of the regulatory genes were lost later in evolution, and are not present in Drosophila or C. elegans, e.g., hedgehog gene,5 indicaling that their presence is not necessay for development and life of vey complex Arthropoda.

Um, but hedgehog is found in Drosophila. It must be - it has the requisite silly name. Even funnier, it was discovered in the fruit fly! Oh, and the same page shows that there are genes similar to hedgehog in C. elegans.

Where were we? Oh, next point...

(3) seemingly excessive complexity of genomes of lower taxons; An immediate problem here is how one defines complexity. PZ Myers has a nice essay on this. But let us proceed. Sherman points out that the sea urchin, which apparently is primative (I guess this means it doesn't know how to eat spaghetti properly), has a whole suite of genes involved in eye development:

While the presence of the opsins could be explained by their possible function in a simple light sensing, sea urchin has the entire set of orthologs of major genes involved in the eye development ... Therefore, it appears that information on the eye development is encoded in the sea urchin genome, while no eye is actually developed, and thus the genetic information seems to be excessive.

He also points out that the sea urchin has the genes for an adaptive immune system.

Yet, sea urchin does not have antibodies, and possibly lacks adaptive immunity in general. Genes that are seemingly useless in sea urchin but are very useful in higher taxons exemplify excessive genetic information in lower taxons.

Or perhaps they exemplify our lack of knowledge about the sea urchin. Now, I know that Sherman is at Boston University Medical School, but I have no idea what he does there. I'm not, though, going to infer that he's useless. Or at least not on this basis.

One can't simply point at a gene and say "we don't know what it does in this organism, so it must be useless". In the paper Sherman cites about the presence of the adaptive immune system genes in the sea urchin, the authors point out that we know very little about the immune systems of most species. To nake his case, Sherman has to show that these genes are not used by the sea urchin, e.g. show that they are not expressed. Put the promotor next to GFP, transform it into the sea urchin, and watch to see when GFP is expressed (it glows green - very pretty). Perhaps the genes are active in the early part of the development of the visual sensory system, and this has been well conserved. Or, again, we could posit co-option of genes from one function to another. Just slap it in, and see when it glows!

There's a recurring theme in this paper - the author makes bold statements and utterly fails to back them up, with evidence, argument or citation. I'm not a developmental geneticist, so I don't want to follow all the claims up, although a few are certainly false (e.g. hedgehog above). Others may be correct, but how are we to know? Can we assume divine revelation?

Right, last point.

(4) similar genetics switches of functionally similar but non-homologous developmental programs. Oh, now the evo-devo people are going to love this:

A distinct set of data that call for a novel approach to evolution comes from comparison of genes that control functionally similar genetic programs in Chordata and Arthropoda. There appears to be a high degree of similarity in some of these genes. A classic example of such similarity is Pax6 gene that controls development of visual systems. According to all current accounts, a common ancestor of Chordata and Arthropoda was a very primitive organism that lacked eyes, and therefore the evolution of eyes in these groups was convergent.

All current accounts? Well, I guess Wiki isn't current then. Neither is Sean Carroll, who suggests a common ancestor had proto-eyes at least (p123 of From DNA to Diversity). Or perhaps Sherman is making a bold claim without any evidence. Again. Carroll et al. suggest that the development of the proto-eye that the common ancestor had was under control of Pax6, as it is so conserved. Sherman appears to be unaware of the idea of common descent. I hear it's a rather popular theory that's doing the scientific rounds.

So, to summarise where we've got to: Sherman has suggested that there is evidence for a problem, but has been unsuccessful in actually providing it. He goes on to suggest an alternative, which is a delight to snigger at. But that, dear fools who have gotten this far, is for another day.

Moral dilemmas

2007-09-13T09:05:00.000+03:00

Is it unethical to gloat to the cat over the Aussies being beaten in the Twenty20 World Cup by Zimbabwe? Especially as he didn't say a word when they were hammering England last winter.

Always nice to hear from an Aussie captain: "Our batting was diabolical, you can't afford to get off to those starts and that's where we lost the game."

Of course, well one to Zimb, I hope they went out and had a good party last night. And that they're still hung over today when they play England....

Filling the intertubes

2007-09-10T16:29:00.000+03:00

Earlier today I sent out a message to a mailing list, and I got the inevitable out of office replies. I can accept that they're a useful idea, but not when this is the message:

I will be out of the office starting 09/10/2007 and will not return until 09/11/2007.

I will be travelling on business on September 10th. I will respond to your message no later than September 11th.
Thank you for your patience.

In particular note the "I will respond...", not "I may" or "If any action needs to be taken, I will...".

I'm debating whether I should respond with this:

Thank you for your out of office reply. Unfortunately I will be out of my office on September 11th, so I will not be able to reply to the response you have promised until September 12th. I hope the delay this causes does not unduly inconvenience you.

BBC headline writer clueless

2007-09-10T07:59:00.001+03:00

OK folks, what's wrong with this headline?

BBC NEWS | Health | Potato 'fuel of human evolution'

Yes, human evolution occurred in Africa, whilst the potato was happily growing in the Americas.

The story is better, the main results are summarised as:

Compared with primates, humans have many more copies of a gene essential for breaking down calorie-rich starches, Nature Genetics reports.

And these extra calories may have been crucial for feeding the larger brains of humans, speculate the University of California Santa Cruz authors.

So it might be the ability to digest starch that was important.

Perhaps it's also worth pointing out that the author might have been writing for an African audience. I recently found out that what is usually called a potato in Europe and the Americas is called an Irish potato in Africa (or at least some parts of it). This is to distinguish it from other potatoes such as the sweet potato.

EDIT: the headline has now been changed to "Starch 'fuel of human evolution'". I guess someone else pointed out the absurdity too.

The snipers were extra

2007-09-07T16:34:00.000+03:00

Most of the time police forces should be respected and supported - they do a difficult job, and I'm sure most are trying to serve their communities.

But (and you knew one was coming) sometimes they make total arses of themselves. This time its the turn of the Aussie police. As reported worldwide, some comedians drove through the security around the APEC meeting using nothing more devilish than some Canadian flags (the Osama look-alike was merely a detail). The police just waved them through to the hotel where President Bush is staying.

So, what do the police do? They allowed these comedians through to the hotel, didn't stop them or apparently check their ID. They've charged them with "entering a secure area". Errrm, you let them. Does this mean that anyone who is allowed into a secure area should be arrested? Including Mr. Bush? Please?

Apparently the police are considering other charges. The comedians evidently have considerable skill at making people laugh at them and appearing absurd, so presumably they'll be charged with impersonating a police officer.

Why I've not been blogging much

2007-09-06T20:43:00.000+03:00

There's a monster guarding my laptop

I managed to drive it off for long enough to post this. But it'll be back.

Oh yes, it'll be back.

Wambam: the photos

2007-09-03T19:53:00.001+03:00

Last week I was in Gotland for a meeting on the animal model (if you don't know, don't ask. It's safer that way). I took some photos, of varying quality.

All of the photos are on my Flickr page, but I thought this was worthy of more attention. It's Henrik demonstrating that mathematics really is gobbledigook, something we had merely suspected:

If anyone who was at the meeting would like to add links to their photos, or add anything else (e.g. transcriptions of Lars' poetry), please do!

Last but not least, the meeting was co-organised by Jon Brommer. He couldn't join us though, because he was waiting to become a father. He duly did that just before the meeting started, so congratulations to Jon and Marianne!

ESEB Saturday

2007-09-02T19:25:00.000+03:00

Phew, the last day.

We started with a plenary lecture by Scott Edwards. He was talking about phylogenetics, and making the point that gene trees are not the same as species trees. Err, OK, let's back up. A method used a lot to work out the relationship between species is to pick a gene (e.g. CO1), sequence it in different species, and use that to draw a tree. This works because when a species is split into two species, variation in the gene sequence can accumulate: mutations occur in one species, and can become fixed by random drift. The longer two species have diverged, the more differences in the sequences have accumulated.

One thing people in phylogenetics often want to estimate is when two species have diverged. They do this by drawing a phylogenetic tree from a gene sequence, and looking at time of the the relevant split in the tree. Edwards pointed out that this may not be the time when the species diverged. The problem is that just before two species diverge, there may be variation in the gene sequences in the population. The time of the gene's split would therefore be before the species' split, so the estimate of the split would be too old. The soluion to this is to use several genes in the building of the tree, and use a model that estimates the time of the species tree splits from the distribution of gene tree splits: the gene trees put an upper bound on the species tree splits (because the species have to split after the genes), and says more something about the distribution (if all gene split between 100 and 110 million years ago, the species trees split is unlikely to be 2 million years ago).

After the plenary, I wandered off to the session on evolution in agriculture. This has nothing to do with what I work on, so I was there out of curiousity. A couple of talks were about the origins agriculture in the middle east, and its spread through Europe. They used looked at gene sequences and inferred one or two originations, and Huw Jones showed that in barley they could find a signature of spread aong the Meditterranean to Spain, and a second spread through central an northern Europe. In contrast, Hazel Goodwin was unable to find any pattern of spread in wheat. The reason for the difference isn't clear.

After lunch, I went to the final session, on association mapping. This is an approach to mapping genes based on the correlation between genes or markers and a phenotype from individuals sampled from a population. It can go badly wrong because the association may not be because the markers are close to a gene affecting the trait, but instead because of population history: if the population being sampled is made up of two sub-populations, then there may be divergence in the trait, and also in genes nowhere near the any affectig the trait, just by chance. Association mapping would then pick up these relationships, and suggest they are causal.

After the talks were over, we adjourned to the castle for the banquet. The food was good, the wine was excellent, the speeches were short. And an evil Norwegian talked me into organising a session at the next ESEB meeting, in Turin in 2009.

I'm back.

2007-09-01T11:03:00.000+03:00

I got back from the wilds of Sweden yesterday morning (1am): after stressing all day about getting the connection from Stockholm to the airport in time, that was fine, but the bus to take us from the aeroplane to the terminal in Helsinki was late.

I spent yesterday running around on errands (deleting spam, picking up the cat etc.). I'll post more goodies later (e.g. the final report on ESEB, and some photos from Gotland). But now I'm off to a wedding (not mine - I'm still in the "all reasonable offers accepted" state). No doubt there will be some very bad jokes in Swedish today.

ESEB: Friday. Starts with sex, ends with Aussie Rules

2007-08-25T08:47:00.000+03:00

It's always good to start the day with sex. Alas, today we were only talking and thinking about it.

One of the big mysteries in evolutionary biology has been how sex evolved. John Maynard Smith pointed out in the 1960s that it really shouldn't have - there's a huge cost to any gene (because with sex it only has a 50% chance of being passed on), so a modifier that stops sex and have a 100% chance of being passed on will be fitter. Since then a lot of people have been worrying about this problem. In her plenary talk, Sally Otto talked about recent work that suggests we are close to a resolution of the problem.

There have been a couple of explanations that have been around for some time. The first is that sex helps evolution because it breaks up bad combinations of genes, particularly when the disadvantages are magnified, so that the cost of carrying two bad genes is more than the cost of carrying one bad gene twice (technically this is called epistasis). This does give sex and advantage, but it's so small, and only occurs in limited and unlikely conditions.

The second explanation is the Red Queen hypothesis, again. A species is being subjected to all sorts of attacks (pathogens, parasites etc.), which are co-evolving with them, so there is a constant arms race (this is the Red Queen bit). A species evolves defences, and sex can help combine them together, to increase the speed at which the species runs away from its enemies. This has some empirical support, but Otto showed that the theoretical results suggested it only worked under a narrow set of circumstances.

She then introduced a third idea - to look at finite populations. All of the previous work she had presented had been done assuming infinite populations. But in a finite population gene combinations can be combined randomly by genetic drift, and also not every combination of genes will be present in the population. Sex can then work to combine gene combinations and give an advantage. Adding the Red Queen improves the advantage (and I suspect that any sort of environmental variation will give an advantage to sex, more work needs to be done etc.).

After the plenary I ran off to the session on integrating evolution and ecology. Lots of different things. Carol Eunmi Lee talked about a lot of work on invasing species (err, were they isopods? Something small and aquatic anyway), and suggested that species that invade tend to live in more variable environments in their core ranges (there was more than this, but I turned off during the physiology). Virpi Lummaa talked about a huge data set on agricultural Finns (the church has kept the records on births marriages and deaths for over 200 years, so they put a few of them in a database), looking at twins. She showed that a female twin is less fit if her sibling was male, and that this was due to conditions in the womb (because it persisted even if the sibling died in the first three months of life).

After the sessions there was the ESEB business meeting. As always with these things, some of it was interesting, some just needed to be said (e.g. the accounts). A couple of interesting things appeared from the meeting - the SSE (our US counterparts) wanted to orgnaise a joint meeting. One option had been to have it in 2011, when there should be an ESEB meeting. Nobody liked that, but the second option was to to have a meeting in 2012 in Canada. This looks like the option that will be followed up, so we'll see what happens. The other thing is that there will be a new journal starting next year called Evolutionary Applications.

Today's the last day, and is followed by the banquet. Tomorrow I have to get an early train to go to Gotland, so there may not be any report for a few days.

ESEB: Thursday

2007-08-24T08:44:00.000+03:00

A short day today: there are excursions in the afternoon, as well as several barbequeues (including the one I attended).

But before then we had to "work". The plenary was given by Michael Majerus. He talked about the classic peppered moth example of evolution. The original story is well known, but some of the original work by Kettlewell has been criticised, and a book appeared a few years go which accused Kettlewell of fraud. The book has been panned (and when I read it, I wasn't convinced either), but is used by creationists as the basis of one of their assaults on evolution. Actually, there are some good criticisms of Kettlewell's work, so Majerus decided to re-run the experiments (actually not quite - he would have needed a wood which is still polluted) but redesigning them to take the criticisms into account.

Majerus ran his studies for 6 years in his field site (actually his back garden), and also trapped moths near by to look at the change in the melanic form. The long and the short of it is that he replicated Kettlewell's results (qualitatively - after half a century we would expect some differences). So, it looks like Kettlewell was right after all (to the surprise of nobody in the room). We then had a sermon about how we need to keep faith and belief out of science, and that the peppered moth is such a good example for education that it should be used. Not really controversial for this audience, but good to rally the troops.

The parallel sessions weren't too interesting for me, but I went to Jim Mallet's talk on mimicry in Heliconius and other butterflies. This is a story that's been going on for years, and I look in on it when I'm at meetings. The latest thing is that they are thinking about how mimicry can affect speciation - mimicking a distasteful species means you don't get eaten, and different populations mimic different species in different areas. But wing colouration also affects mating behaviour (quite simply, the male can't recognise the female that's the 'wrong' mimic), so that could lead to reproductive separation and speciation. SO, they're now looking for hybrids between species, and also into the genetics and development of wing colour. More next time.....

In the afternoon I didn't go on an excursion, instead we had a barbaqueue in the house I'm staying (err, actually outside the house). Lots of beer drunk, a new wealth index invented, and just after a pregnant lady sat down next to her, Vilppu asking "So, how do you get the sperm?"

ESB Wednesday: "my Red Queen's map's better than yours"

2007-08-23T15:42:00.000+03:00

Now we're seriously into the conference: the first full day of talks (without poster sessions, excursions etc.).

We kicked off with Douglas Schemske talking about evolution in the tropics. It's well known that the amount of diversity in the tropics is huge compared to the more temperate parts of the world. People have surveyed forest plots a few hectares large, and found a huge number of tree species - more than in the whole of North America. This has puzzled people, and there are several theories being kicked round. Schemske was arguing that the diversity is driven by biotic interactions - i.e. predators eating prey, plants living in a mutualistic interaction with ants, pathogens living off hosts etc. His claim is that in colder regions, the main threats come from abiotic conditions (cold, frost, drought etc.), so species invest in reactions to those.

In the tropics, the environment isn't from inclement weather, but from other species, so a species has to adapt to overcome its predators, pathogens etc. This leads to an arms race - the predators and pathogens are also evolving as well. This sort of interaction is called the Red Queen hypothesis, from Alice Through the Looking Glass, where the Red Queen says

Now, here, you see, it takes all the running you can do, to keep in the same place.

Species have to keep evolving to keep half a step ahead of their competitors. In different parts of a species' range, the populations will be assailed by slightly different competitors, so will evolve in different ways (mimicry rings are a great example, wander around Jim Mallet's pages for a bit and you'll see why). This can then lead to speciation.

This sounds all well and good - more biodiversity leads to more biodiversity, but how does it get started? Schemske didn't address this problem. He did mention an alternative hypothesis to his, which is that the World has been tropical for most of the time there has been life, so the tropics may just be older, and hence had more time to produce more biodiversity, which has then exploded.

After the plenary, I spent most of my day listening to talks about mapping genes. A lot of them were "I got some DNA from a cross/several crosses, made a linkage map, compared it to once from a model species, and then mapped some genes". The quality of the maps improved over the day, and people found genes (a couple I wasn't convinced about, but hopefully they'll check further). One interesting point came from Hopi Hoekstra. She has been looking at genes for coat colour in a mouse, and found a locus affecting variation in the trait, and then found out that it wasn't in the protein sequence, it was in the regulatory region (i.e. the part of the genome that controls when a gene is switched on and off). I liked this, as I've been wondering if regulatory sequences will appear in these sorts of analyses.

The non-mapping talk I went to was by Leonard Nunney. He has been working on Haldane's dilemma, which sets a limit on the rate at which a population can evolve in a worstening population. He put this into the context of the Red Queen, because her full reply to Alice is:

Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!

So, not only does one have to run as fast as you can to stay in the same place, but if the environment is changing, you have to run even quicker. Actually, this was the point of the original Red Queen paper (by Van Valen): that extinctions occur at roughly a constant rate, which suggests that species aren't adapting any better to the environment, presumably because the environment keeps on changing.

The evening presentation was by Steve Stearns (now of Yale) on how the ESEB was set up. Basically, a publisher wanted him to start a journal, but wanted a society behind it. So, he had to set up a society too...

Best Conference Poster Evar!

2007-08-22T08:37:00.001+03:00

Below the fold is the best poster ever seen at an evolutionary biology meeting.

Email Chris and tell him you saw the poster on the web, and think it's brilliant (but don't tell him where you found it. Please).

ESEB: Tuesday

2007-08-22T08:34:00.000+03:00

Today the conference started for real. We kicked off with a plenary talk by Sean Carroll (of Endless Forms Most Wonderful fame), talking about evo-devo, and how what's important is the regulation of genes involved in development, not their actual protein product. I've read about some evo-devo, but now I feel I understand the point (I might blog more on this later).

After the plenary there were parllel session. Of course, there are always more than one that look interesting, so I started in the evolution in complex environments session. This kicked off with a nifty talk by Graham Bell, talking about adapation in bacteria. His starting argument was that the classic experiments of bacterial dynamics set up simple environments - one or two substrates, one or two clones etc. But this isn't the sort of environment the bacteria live in. He then talked about some experiments with many clones, in more complex environments (lots of substrate etc.).

Classically, we might expect to see trade-offs and specialisation in the use of different substrates, so that a clone that prefers glucose will be worse at using fructose, for example. But they didn't see this - instead everything just adapted equally. His conclusion was that the bacteria were "overlapping incomplete generalists": they could use a wide variety of resources reasonably well. The rest of the sesion was less interesting - playing with models and drawing pairwise invasability plots (beloved of adaptive dynamicists, obscure to everyone else).

After coffee (during wich I explained Genetics 101 to a senior professor), I went to the evolutionary epidemiology session. I missed the first half of the first talk because I didn't realise it was in another building (but did have a fascinating chat with a physchiatrist). The talk that was mostly interesting for me was Anna-Liisa Laine's. She's in Helsinki, she works on powdery mildew, but most of the times I see her, we discuss something else. So this was a good chance to find out what she's been doing.

Lunch was in a variety of restaurants - I was in Il Forno, where we didn't fornicate (perhaps they had better luck in Hörs).

After lunch, more epidemiology. The final talk was about where food poisoning comes from: using molecular tools to compare bacteria from food poisoning to samples from the environment and from animals (chickens, sheep, cows etc.). They threw some neat statistics at the problem, and could show that most of the poisoning came from meat, and not the environment. So, no need to wash your hands after playing with pigeon shit, then!

After this was the first poster session. This is hell if you're a student. You stand by a poster that you've sent two weeks on as people walk past thinking "can't be bothered - too much text on it". I don't have a poser (ha!), so I wondered round looking for the two or three interesting ones. I found a few - one using a really simple model for mating strategies - number of mates, variation in mating success etc. (a Dirichlet-multinomial. OK, simple if you're a statistician). There was also another poster that deserves its own entry....

That was the end of the official session - it was followed by the informal session, chatting to people, having lunch with the folks from Jyväskylä. For once I avoided the pub.

So, more today - looks like QTLs and conservation genetics.

Start of the meeting...

2007-08-21T08:42:00.000+03:00

Well, I got here.

Typically for a conference, I met a bunch of other people going to the conference as well. Some I knew, some were rather obvious because they were carrying long plastic tubes (for posters). Monday was just for registration and a reception, so I did a bit of networking - catching up with old friends, meeting people who send me blank emails, that sort of thing. There are 1300 people here, so I guess I won't see everyone.

The conference abstract book is huge, about 600 pages of A4.

This is just silly: they have had to print out 390 000 sheets of paper (anyone know how much forest that is?). If they were piled one on top of another, they would be 55m high (that's as tall as, err, a very high building. For reference, Uppsala cathedral is 118m high).

So, today we start with the talks etc. We kick off with a plenary (Sean Carroll), and then 5 parallel sessions. Of course, I want to go to two of them.

Oh, and we also found the pub.

Off again...

2007-08-19T18:52:00.000+03:00

Tomorrow I'm off to Uppsala to the ESEB meeting, after which I'll be in Gotland for another (smaller) meeting. I'll try and blog (the place I'm staying in Uppsala has wireless), but I might be too busy working on my comparative study of Nordic beers.

So all three of you regular readers should expect an even less regular service. And if anyone's in Uppsala as well, then say hello if you see me!

The Wonders of Modern Technology

2007-08-17T22:13:00.000+03:00

I think I submitted a paper today, but I'm not sure. At least I tried to.

The problem was the electronic submission. I was sending the manuscript (it's on Bayesian variable selection, for anyone who cares) to a statistics journal, so they like things in LaTeX. I had prepared the manuscript that way (my co-author is a LaTeX guy as well, so it was a way of learning how to use it). But they wanted the initial submission as a .pdf.

LaTeX writes documents out as Postscript files, and the pdf maker on TeXmaker doesn't like my figures. So, I had to make a Postscript version, then found out I couldn't convert to pdf on my Windoze machine, so I moved over to my Linux box, and used ps2pdf to convert it. The manuscript looked fine in Acrobat. Then I tried to submit. OK, first I realised we had forgotten to write an abstract, so had to go back, do that, write a postscript version, move to Linux and convert to a pdf. When I (finally) uploaded the pdf, the journal's software tells me it's not a pdf! Wha? It looks like one to every pdf reader I can lay my hands on (which is 3 or 4 - isn't Linux great?). But for some reason...

In the end I just emailed everything to them, with an apologetic note, asking how I could convert my pdf to a pdf. They haven't answered.

Turf War!

2007-08-15T21:49:00.001+03:00

The latest from the Beeb:
BBC NEWS | Science/Nature | Chernobyl 'not a wildlife haven'

The back-story is that in 1986 a nuclear reactor in Chernobyl blew up, throwing radioactivity over a wide area. The Soviet authorities decided that the area wasn't safe, so they evacuated everyone from around it, and basically built a big fence and put up signs with "Keep Out" written on them (in Russian or Ukranian, presumably). This sort of site is irresistible to ecologists: except for the radioactive bits, the habitat should revert back to a natural state.

So, the conclusion of one group of ecologists is that everything is rosy around Chernobyl, with lots of rare species hiding from mankind under a protective mushroom cloud. In contrast, the BBC report another paper by Anders Pape Møller and Tim Mousseau, Species richness and abundance of forest birds in relation to radiation at Chernobyl. This is the abstract:

The effects of low-level radiation on the abundance of animals are poorly known, as are the effects on ecosystems and their functioning. Recent conclusions from the UN Chernobyl forum and reports in the popular media concerning the effects of radiation from Chernobyl on animals have left the impression that the Chernobyl exclusion zone is a thriving ecosystem, filled with an increasing number of rare species. Surprisingly, there are no standardized censuses of common animals in relation to radiation, leaving the question about the ecological effects of radiation unresolved. We conducted standardized point counts of breeding birds at forest sites around Chernobyl differing in level of background radiation by over three orders of magnitude. Species richness, abundance and population density of breeding birds decreased with increasing level of radiation, even after controlling statistically for the effects of potentially confounding factors such as soil type, habitat and height of the vegetation. This effect was differential for birds eating soil invertebrates living in the most contaminated top soil layer. These results imply that the ecological effects of Chernobyl on animals are considerably greater than previously assumed. (emphasis added)

Going on this (unfortunately I can't get access to the full paper), what the authors found is that individuals and populations suffer more in areas of higher radiation. Makes sense. But look at the bit I bolded. In a scientific paper, this is really sniffy and aggressive: it's tantamount to accusing someone of distorting reality.

But, if we look at the science of both groups, there doesn't seem to be any conflict in what they report. The people who are saying that things are going well are comparing the exclusion zone to what it would have been like without the accident. As one of them writes:

However, it cannot be said that radiation is good for wildlife. Instead, the elimination of human activities such as farming, ranching, hunting and logging are the greatest benefit, and it can be said that the world's worst nuclear power plant disaster is not as destructive to wildlife populations as are normal human activities.

In other words, the accident caused the creation of a thriving conservation area. It wasn't planned, but it still happened. Isn't that great?

What Møller and Mousseau show is that, within the exclusion zone, the biodiversity is lower in areas with higher radiation. But they make no mention of comparisons with areas outside the exclusion zone. As far as I can see, there is no conflict between the results of the two groups (but if I see the paper, I might revise that opinion!): the biodiversity could still be higher than outside the zone.

Why the fuss? I don't know any of the people involved, but it looks like a typical academic spat. One side's pronouncements have almost certainly bruised the others' large egos, and this is the response. Because of the way science is supposed to work, we can't write in our papers that such and such is a tosser (checks: there are 201 papers in Web of Science with Tosser as an author. 172 are by A. Tosser. Hmmmm), so we are reduced to using hints, insinuations and politeness. Once you are familiar with the literature it's clear what's going on (and the example here is really not subtle). But those involvedm can still get your name on the BBC website by claiming that there is a real controversy.

This sort of thing happens quite regularly, and the best thing to do is either to step in to calm things down, or sit back with some popcorn to watch. Get a front-row seat in the right conference, and the blood will be lovely...

I have an immature Nature

2007-08-15T20:34:00.000+03:00

The title of a correspondence in the latest issue of Nature:

Scientific bodies must take own action on emissions

'nuff said

I do sleep deeply

2007-08-15T18:54:00.000+03:00

Apparently there was a big thunderstorm over Helsinki last night. And I slept through it all. Oddly, though, I did wake up last night - an insect tried to hide in my right ear.

Rove Move

2007-08-13T15:07:00.001+03:00

BBC NEWS | Americas | Top White House aide Rove resigns
Just two thoughts:

Let's see how many "rat leaves sinking ship" comments this story gets.

I don't think anyone predicted him leaving before Gonzales.

Rejoice! Rejoice!

Bunny show-jumping

2007-08-11T13:59:00.000+03:00

Via "suited" at the Guardian's YouTube roundup, we have this exercise in, well, bunny exercise:

The Christmas tree is a lovely touch.

No string this time

2007-08-08T10:31:00.000+03:00

At the moment I'm teaching a course in Mustiala, in central Finland (well, central compared to Helsinki). This is why I haven't been blogging much (and what I have has been odder than usual). My web addiction took a severe test on Monday, after a tractor drove over the cable connecting the university's web servers to the world.

Some photos from the course have been put on the web by this guy:

Most are, of course, fairly meaningless if you don't know who the people are, and have far too much meaning if you do know who the people are. But there are also a few that can be appreciated by everyone (I now have the swamp landscape as my desktop wallpaper).

Exceptional article in the Observer

2007-08-05T10:01:00.000+03:00

Today's Observer tells us this:

Gifted pupils: too many are just ordinary, say teachers
Whitehall estimates that one-in-10 children have exceptional talent are far too high, reveals study
Well, um, yes.

The obvious question is why 10%? There will be a lot of student who are just below this threshold, so are only marginally less exceptional. Why leave them out?

Of course, there are some children who certainly do not have exceptional talent (e.g. special needs pupils), so not every pupil should be categorised as exceptional. But with a judicious bit of mission creep, we should be able to include the top 99%.

Archaeologists Beware!

2007-08-04T11:40:00.000+03:00

Here we have the latest technology - a trap to catch archaeologists:

How does it work, you are asking yourselves. Well, it's based on the observation that archaeologists are fascinated by old rubbish. So, the trap is set up to mimic a mediaeval rubbish bin. The archaeologist is immediately attracted, and leaps in. When he is in there up to the middens, the trapper leaps out and ties up the archaeologist.

The one question I cannot answer is why anyone would want to capture archaeologists. Pest control?

stRing

2007-08-03T14:51:00.000+03:00

Here is some more code, to keep the troll confused and happy.

library(ggm)

# Figure 3.1 data
Data=read.table("http://pages.usherbrooke.ca/jshipley/recherche/NINA%20files/fig31.dat")

# Write the DAG
DAG1=DAG(V2~V1, V3~V2, V4~V2, V5~V3, V5~V4, V5~V6)
# Draw the DAG
drawGraph(DAG1)
# D-separation test
dSep(DAG1, "V3","V1", NULL)
# Shipley Test
shipley.test(DAG1, cov(Data), dim(Data)[1])

#####################################################
#### SEM

Data=read.table("http://pages.usherbrooke.ca/jshipley/recherche/NINA%20files/fig31.dat")
names(Data)=LETTERS[1:dim(Data)[2]]

library(sem)
# Input the model
path3.1=specify.model()
A -> B, beta.AB, NA
B -> C, beta.BC, NA
B -> D, beta.BD, NA
C -> E, beta.CE, NA
D -> E, beta.DE, NA
F -> E, beta.FE, NA
A <-> A, var.A, NA
B <-> B, var.B, NA
C <-> C, var.C, NA
D <-> D, var.D, NA
E <-> E, var.E, NA
F <-> F, var.F, NA

# Fit the SEM
model3.1=sem(path3.1, cov(Data), N=dim(Data)[1])
summary(model3.1)

# Specify an alternative model: remove the B->D path
path3.2=specify.model()
A -> B, beta.AB, NA
B -> C, beta.BC, NA
C -> E, beta.CE, NA
D -> E, beta.DE, NA
F -> E, beta.FE, NA
A <-> A, var.A, NA
B <-> B, var.B, NA
C <-> C, var.C, NA
D <-> D, var.D, NA
E <-> E, var.E, NA
F <-> F, var.F, NA

path3.2

model3.2=sem(path3.2, cov(Data), N=dim(Data)[1])
summary(model3.2)

# Compare the two models
anova(model3.1, model3.2)

###################
# String

# Read data (use your own file!)
string=read.table("string.txt")

# First parameterisation
path.string1=specify.model()
Length -> V1, NA, 1
Length -> V2, beta.2, NA
Length -> V3, beta.3, NA
Length -> V4, beta.4, NA
V1 <-> V1, var.1, NA
V2 <-> V2, var.2, NA
V3 <-> V3, var.3, NA
V4 <-> V4, var.4, NA
Length <-> Length, var.L, NA

model.string1=sem(path.string1, cov(string), N=dim(string)[1])
summary(model.string1)

# First parameterisation, Var(V1) set to zero
path.string1a=specify.model()
Length -> V1, NA, 1
Length -> V2, beta.2, NA
Length -> V3, beta.3, NA
Length -> V4, beta.4, NA
V1 <-> V1, NA, 0
V2 <-> V2, var.2, NA
V3 <-> V3, var.3, NA
V4 <-> V4, var.4, NA
Length <-> Length, var.L, NA

model.string1a=sem(path.string1a, cov(string), N=dim(string)[1])
summary(model.string1a)

# Second parameterisation
path.string2=specify.model()
Length -> V1, beta.1, NA
Length -> V2, beta.2, NA
Length -> V3, beta.3, NA
Length -> V4, beta.4, NA
V1 <-> V1, var.1, NA
V2 <-> V2, var.2, NA
V3 <-> V3, var.3, NA
V4 <-> V4, var.4, NA
Length <-> Length, NA, 1

model.string2=sem(path.string2, cov(string), N=dim(string)[1])
summary(model.string2)

# Second parameterisation, Var(V1) set to zero
path.string2a=specify.model()
Length -> V1, beta.1, NA
Length -> V2, beta.2, NA
Length -> V3, beta.3, NA
Length -> V4, beta.4, NA
V1 <-> V1, NA, 0
V2 <-> V2, var.2, NA
V3 <-> V3, var.3, NA
V4 <-> V4, var.4, NA
Length <-> Length, NA, 1

model.string2a=sem(path.string2a, cov(string), N=dim(string)[1])
summary(model.string2a)

# Profile likelihood, to look at likelihood of Var(V1)
GetDev=function(var, path, Cov, N, ...) {
path[5,3] = var
deviance(sem(path, S=Cov, N=N, ...))
}

Var1=as.character(seq(0,5, length=100))
Deviance=sapply(Var1, GetDev, path=path.string1a, Cov=cov(string), N=dim(string)[1])

plot(Var1, -Deviance+min(Deviance), type="l")
abline(h=-3.71, col=2)

DevDiff=-Deviance+min(Deviance)
max(Var1[which(DevDiff>-3.71)])

How long is a piece of string?

2007-08-02T12:47:00.000+03:00

Possibly the best post I will ever make....

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Statistical Modelling: The Bits Pt. 2

2007-08-01T16:37:00.000+03:00

In my last post, I outlined the ideas of statistical inference: that we have a model, and we have a philosophical scheme that tells us what maths to do to estimate the parameters of the model. But this is not the same as doing this in practice. The equations are often complex, and difficult or impossible to solve (anyone fancy trying to integrate in 1000 dimensions?). Because of this, a lot of methods have been devised for estimating the parameters, and particularly estimating their variability (which, from now on, I will write about as estimating the standard error).

For maximum likelihood, the problem of getting the estimates is an optimisation problem: find the set of parameters that give the largest likelihood. For the simple linear regression, this is easy, because the equations were worked out a couple of hundred years ago, by some French mathematicians trying to work out where Paris was. For more complex models, such as generalised linear models, there are algorithms that have been shown to efficiently iterate towards the ML estimates. In more complex models, more general or complicated search algorithms might be needed (for example, there is the EM algorithm which is useful for dealing with missing data. So useful that the missing data is sometimes invented, in order to use it).

These algorithms only give point estimates. We also want to know the standard error, and there are methods for doing this. Again, it may be that the equations can be derived directly, or can be found with a simple numerical search. For less standard problems, more complex algorithms can be used. Two popular ones are called the bootstrap and the jackknife. They both work by re-sampling the data, and using that variation to estimate the standard error. What is less well known is that they can also be used to estimate the bias in the point estimates.

Even more complex problems have even more complex solutions. For Bayesians, the most common method for fitting their models is a technique called MCMC. This estimates the parameters (actually their full distribution) by simulation. The technique is not simple to explain, but it is used because it is very flexible and for a lot of problems works well in practice. However, this is not the same as a Bayesian analysis: MCMC can be used by frequentists as well, and other complicated methods can also be used to fit Bayesian models (e.g. sequential importance sampling).

So, now we have a model fitted. There is still one part of the process that is often done: model selection. This is not always done, and is perhaps done more often than it should. It also creates a lot of heated debates, which may surprise some of you. That's because model selection is often disguised as hypothesis testing.

A general description of the role of model selection is that it is a way of choosing between different mathematical models for the data, to find the best one. For frequentists there are two principal ways of doing this: null hypothesis statistical testing and using information criteria to compare models.

Null hypothesis statistical testing (NHST) is the method everyone is taught to test hypotheses. What one does is set up a null hypothesis, for example that there is no difference between two groups, and then test whether this hypothesis is supported by the data. If it is, then the null hypothesis is accepted (strictly, it's not rejected), otherwise it is rejected. In one common formulation, whenever a null hypothesis is set up, a more general alternative hypothesis is also proposed: rejecting the null hypothesis means accepting the alternative hypothesis.

Where is the model selection in this? We can take the Harry Potter example, and test the null hypothesis that the change in book length over time is zero. The relevant part of the mathematical model is

m_i = a + b X_i

where m_i is the expected number of pages, and X_i is the year of publication. The null hypothesis is that b=0. But this is equivalent to this model

m_i = a

And m_i = a + b X_i is the alternative model for the alternative hypothesis. Hence, hypothesis testing becomes a problem of comparing two models. The models are compared by calculating the amount of variation in the data explained by the two models, and seeing if the larger model (i.e. the one with the effect of time) explains a significantly greater amount of variation.

The alternative method for model comparison uses information criteria. These are measures of model adequacy: they consist of a measure of model fit (the deviance: the smaller the deviance, the closer the fitted model is to the data), and a penalisation term for complexity (the more parameters in the model, the higher this penalisation). The different models being compared are fitted to the data, and the relevant information criterion (e.g. AIC or BIC) is calculated for each one. The model with the lowest criterion is declared the best. If there are several models with similar criteria, they are sometimes all examined, and one of them chosen to be the best, for other reasons (e.g. because it makes sense scientifically).

Both of these methods have their counterparts in Bayesian analysis: hypothesis testing can be carried out using Bayes factors, for instance, and there is an information criterion for Bayesians (DIC. Alas, not the Bayesian information criterion!).

So, there we have it. OF course, this is not all there is in statistical inference: for example, I have not dealt with model checking (i.e. asking whether the model actually fits the data well), and there are many details I have left out. But I hope this give a scheme that separates out the different parts of fitting statistical models, and reduces some of the confusion. To summarise, here is a list of the parts of the process, and examples of the actual methods used in each part:

The mathematical model

regression, ANOVA

ARIMA (time series)

Generalised linear models, and Generalised linear mixed models

The inferential framework

Frequentist (Maximum Likelihood and REML)

Bayesian

least squares

non-parametric

Model fitting (i.e. the computations)

bootstrap and jackknife

MCMC

importance sampling and sequential importance sampling

Model selection

Significance tests

information methods (?IC: AIC, BIC, DIC, etc)

Statistical Modelling: The Bits Pt. 1

2007-07-30T16:17:00.000+03:00

It's a sad fact of life that it's too short, so we don't have time to learn everything that we need to know. For most biologists (and probably most scientists, and indeed social scientists), one of the things they should learn to do science well is statistics. Of course, the poor dears are too busy learning about things like biochemistry or PCR, to have time to learn about the important stuff.

Because of this, there is a lot they don't know, and also a lot of confusion about the bits they do know. I therefore thought it was worth writing a few posts about some of these issues, to try and dispel at least some of the confusion.

Now we're below the fold, I should admit that I don't mind too much the lack of knowledge - there are large swathes of statistics that I don't know about either (I was too busy admiring the paper aeroplane building skills of my fellow biochemistry students). The confusion is more of a concern, and I think it is largely because biologists get little training in statistics as undergraduates, so don't have the grounding in the theory when they start doing research. And, yes, I am generalising - some biologists do become very good statisticians. But there is still plenty of confusion.

A lot of basic confusion comes from not understanding the different parts of using models to analyse data. For the moment I am ignoring things like model checking, and how to interpret the results. This is not because they are unimportant, but just because I want to write a blog entry, not a monograph. Anyway, There are, I think, 4 parts that can be separated out:

the mathematical model
the inferential framework
model fitting (i.e. the computations)
model selection

Point 1 is often passed over, and points 3 and 4 are often confused with point 2. I will deal with the first two points in this post, and treat the others later.

To help disentangle the different parts, it is useful to have an example. For this, we can use a simple linear regression, such as the one I used here. The mathematical model is self-explanatory. For the regression, it can be written as

m_i = a + b X_i
Y_i ~ N(m_i, s²).

The first line is the equation for a straight line. X_i is the covariate (year, in the example). This is then related to an expected value, m_i. The second line says that each data point, Y_i, is normally distributed with mean m_i and variance s².

Note that the data are assumed to be drawn from some distribution. Hence, they are random. However, they depend on parameters, in this case the mean and variance. Here the mean is modelled further, using an equation, so it is deterministic. i.e. if we knew the parameters, we would know the true value of the mean.

More complex analyses also have a model underneath them: the usual ANOVA actually has a model that is almost the same. The mean can be a function of several parameters, and might not be the mean, but just a parameter that controls the likelihood. The variance might also be modelled. And the parameters that are modelled could themselves be functions of more parameters. And all these parameters might themselves be random. Yes, things can get complex.

The problem, then, is to estimate the parameters. There are several schemes for doing this. For the model above, the two commonly used schemes are the frequentist and the Bayesian approaches. Each of these gives us a way to estimate the parameters. They are, in essence, philosophical approaches to understanding what we mean by probability and randomness. In the frequentist scheme, what we observe is random, with fixed parameters, and hence any statistic we calculate (such as the slope of the straight line) is a random function of the data. In contrast, the Bayesian approach is to say that anything we are uncertain about is random. Hence, we treat the data as fixed (because we know it: we have observed it), and the parameters are random.

Both of these philosophical schemes are useful in that they then can be used to construct methods for estimating the parameters of the mathematical model. Both use the likelihood, i.e. the probability of the data given values of the parameters, albeit in different ways. For example, the frequentist scheme tells us that we should find the values of the parameters which give us the maximum value of the likelihood. Hence, this is often spoken about as a maximum likelihood approach (there is another philosophical scheme which leads to the same equations, but to me it always looks like asking Fisher to wave his fiducial wand to make everything mysteriously right. I assume I'm missing something important).

Estimating the parameters is not enough, though. We also want to know how reliable they are. It makes a difference to our estimate that for every year J.K. Rowling writes an extra 300 pages if the range of possible values is 290-310 or 0-600. In the former case, we have a pretty good idea how much time to book off work to read her latest book: in the latter, we have little idea about whether we need to have a headache or major heart surgery. We can summarise the reliability in a variety of ways: a popular one is the probability that the estimate is less than zero. Alternatives include giving a range of likely values (typically a range for which there is a 95% probability that the estimate is within it), or a statistic such as a standard deviation to summarise the variability in the estimate (in the frequentist scheme, this standard deviation is called the standard error).

In my next post, I will describe the model fitting and model selection, i.e. how these schemes are used in practice to make inferences. I suspect this will include a rant about p-values.

Before I stop to work myself up into a frenzy, one last point is worth mentioning. The approaches discussed above assume that the data are normally distributed. If we discard this assumption, we can use different approaches. One, called least squares, is based on optimising a property of the model and data (i.e. minimising the sum of the squares of the differences between the data and the expected values). It turns out that this is the same as maximising the likelihood, i.e. it is mathematically the same as the frequentist approach. I, therfore, use the frequentist interpretation of the equations, and it gets round a lot of fiddly problems. Another, called non-parametric statistics, does away with any distributional assumption, but also throws away part of the data: rather than use the data themselves, the analysis concentrates on using the ranks of the data (i.e whether each Y_i is the smallest, second smallest etc.). In some cases, I think this can still have a likelihood interpretation, but I must admit that I haven't checked: it's just a hunch.

Still here?

2007-07-30T12:19:00.000+03:00

Wow! The virtual world is still here!

I'm co-teaching a course with Bill Shipley in (sort-of) central Finland. Yesterday we drove up here, and settled in. I tried to get a wireless connection (I had been told there was one) from my room. My laptop would search and find a weak connection, and then when I tried to connect, it would disappear. Repeatedly. Once it decided to mock me by giving me two networks that I couldn't connect to. It would tell me that they were secure networks, which is a hint that I might need a password. But, being rational, I tried to connect anyway.

This may not be relevant at all, but I've just been reading a book about happiness, which explained that we often do things that we expect will make us happier, but we don't get the thrill at the end. This leads to addictive behaviour: we just can't stop ourselves even if we are objectively aware of the futility.

OK, must stop: I have to check my email.

The Academic Cult

2007-07-28T19:09:00.000+03:00

Does anyone know who Bug Girl is? We have evidence that she needs to be re~~programm~~trained.

It's all for her own good. Without retraining, she'll never be able to get another grant.

Reactions to the world's largest glider

2007-07-26T13:07:00.001+03:00

A couple of weeks ago, a paper came out online in PNAS, describing the aerodynamics of Argentavis magnificens, a big bird (presumably it had yellow feathers too).

The serious report on this work was done a few weeks ago, but my, err, friend Henry Pihlström has been looking at the paper too closely.

Henry pointed out some details in Figure 4. Here is the figure:

And, for those who don't stare obsessively at journal figures (or at least not as obsessively as Henry evidently does), here is Fig. 4C in detail:

I think the man's reaction is understandable - you wouldn't see that coming towards you on Sesame Street.

Reference
Chatterjee S, Templin RJ, Campbell KE. (2007) The aerodynamics of Argentavis, the world's largest flying bird from the Miocene of Argentina. Proc. Natl. Acad. Sci. 104: 12398-12403.

Squirrels are dangerous

2007-07-25T22:19:00.000+03:00

Brought on by this post, I thought I should explain about the dangers of Helsinki squirrels.
In most of Helsinki, one is safe from squirrels: you can happily walk through the station in the early hours, and you'll be fine. Places like Kallio and Hertaniemi: no problem. But be careful if you venture into Seurasaari. There you can walk through the forests along the paths. But if you take a wrong turn, you find them staring at you:

As you go further down the path, you will find yourself becoming more nervous - what is that scratching behind you? Does the path lead back to civilisation, or are you going deeper into the wilds? Is that duck in on the schemes?

Then you turn a corner and discover - some of them are armed

Fortunately, the importance of the tourist industry means that Helsinki City Council has the matter in hand. It is bad press to have your visitors mugged by a squirrel, and have their peanuts stolen. Therefore, they have organised special squads, who are trained to track down and kill down these monsters, even the biggest ones.

Elephants, evolution, and hot testes

2007-07-25T16:47:00.000+03:00

Sometimes the news on the BBC World Service brings up strange associations (and I don't mean the Conservative Party). Yesterday morning I heard a report about the latest findings on the evolution of elephants. The first thing that struck me was how good the report was: concise, informative, and went for accuracy rather than sensationalism. The other thing that struck me was that elephants don't have balls.

The BBC report was about a paper in PLOS biology, which reports firstly the first complete mitochondrial sequence of the extinct American mastodon. This sort of thing is now de rigour: extracting DNA from a tooth that is at least 50 000 years old, sequencing all the little bits, and then putting them back together to give an almost complete sequence. They then compare this sequence to sequences of other proboscideans, i.e. African and Asian elephants and a mammoth. From this they could work out the phylogenetic relationship between the species, and the dates when the different species diverged:

The shape of the tree is roughly what was expected. What was interesting what the timings of the divergences. A couple of technical points need to be raised to see why this is interesting. First, any phylogenetic tree gives the distance between different species, but it does not show where the tree started. This is because the processes of sequence evolution are indistinguishable whether we run them forwards or backwards in time. Hence, we can't find the start of the tree - the root. The usual solution to this is to include a species which is known to have been the first to diverge from the rest. This is called an outgroup: often is is selected on the basis of the fossil record.

Previous studies of elephants and their ilk have used the dugong or hyrax as an outgroup. This is a problem because these diverged from the proto-elephants at least 60 million years ago (Mya), but the elephants started diverging from each other less than 30 Mya. Hence, there is a lot of time in which the sequences could have diverged. This affects the dating of the divergences because (a) the molecular clock has had plenty of time to start running faster or slower, and (b) the differences between the sequences can start to saturate. The second point is important because the methods for estimating the times of divergence assume that the number of differences between sequences is proportional to time. When saturation occurs, this is no longer true. Hence, it would be better to use an outgroup that diverged more recently. The mastadon sequence provides this, as the fossil record can be used to date their divergence to about 25 Mya.

With this new outgroup, the divergences are pushed back in time, suggesting the mammoths and elephants diverges about 6 to 9 Mya. As the authors of the paper note, this is about the same time that humans, chimpanzees and gorillas diverged from each other. Now, this might be happenstance, but it could also be related to the changes in the environment at the time, as grassland spread throughout the world.

But, you are wondering, what about the balls? Well, the authors also found that the mutation rate is about half as fast in the elephants as in primates. The authors do not give a good explanation for this (they admit it themselves!). I don't have a good explanation either, but it did remind me of another paper, which was thrust into my hands whilst I was stood on the stairwell of the University of Helsinki museum. This paper pointed out that, according to the fossil record, elephants evolve really quickly, and hence could adapt to varying environments. They then pointed out that elephants don't have scrota: the testicles are kept near the kidneys, rather than migrating to somewhere cooler (this is called testicondy). It is well known that the mutation rate increases with temperature, and this has been used to explain why such a delicate piece of the anatomy hangs around outside the body. The suggestion, therefore, was that having the testicles inside the body increased the mutation rate, and hence induced more variation, so that there was more opportunity for selection to work: in other words, evolution could go faster.

How does this line up with the finding of a low mutation rate in the mitochondria? Well, one reason why mitochondria are studied is that they are only inherited from the mother. So, their evolution is obviously not affected by testicondy. Is it possible that the lower mutation rate in mitochondria offsets the higher rate in male nuclear DNA? It seems curious to me that there would be such a difference. But perhaps the elevated mutation rate in males is enough, so an increased mutation rate in females would increase the mutation rate above the optimum.

Hmmm.

References
Werdelin L., Nilsonne Å., Fortelius M. (1999) Testicondy and ecological opportunism predict the rapid evolution of elephants. Evolutionary Theory 12: 39-45.
Rohland N., Malaspinas A.S., Pollack J.L., Slatkin M., Matheus P., Hofreiter M. (2007) Proboscidean Mitogenomics: Chronology and Mode of Elephant Evolution Using Mastodon as Outgroup. PLoS Biol 5(8): e207 doi:10.1371/journal.pbio.0050207

Book review: Bayesian Methods for Ecology

2007-07-24T13:48:00.000+03:00

Review of Bayesian Methods for Ecology (NHBS) by Mick McCarthy

I've been meaning to write this for a couple of weeks, but I've either been too busy, or the moment I sit down to write it, a cat appears and sits on the keyboard. Well, I sorted the cat problem: he leapt up, I cut his nails.

So, the title of the book is fairly explanatory: it's about Bayesian methods for ecology. It's clearly aimed at ecologists who are not trained in statistics, but who need to use statistical methods. Mick spends a lot of the first half of the book giving the background to Bayesian methods, justifying their use, and criticising the use of hypothesis testing. He then moves on to describing standard models (regression and ANOVA) and how to fit them in a Bayesian way. The final part of the book consists of case studies using Bayesian methods, showing how they work in practice.

On the whole, I really like the book: it provides a simple, easy to follow, explanation of what Bayesian methods are, and how to use BUGS to fit simple models. the latter point is important, as it means that ecologists can see how to use the methods in practice, and the code and data are available on the web.

One thing I did like about the book is its emphasis on using information from outside the data to improve the estimation. This is an aspect if Bayesian methods that I use less than I should, but is perhaps particularly important in practical conservation problems, where there is a lot of background information, and the aim is not to demonstrate some theory (where informative priors can bias the demonstration). If this book encourages scientists to use Bayesian method for problems where several strands of information have to be synthesised, then it will have done a useful service.

I have a couple of criticisms. The first is that I feel the strength of the Bayesian approach is in the way it can handle complex models. This relies on a hierarchical scheme for modelling data. Although this is mentioned, I would have liked to have seem more on it: I think a whole chapter would have been worth aiming for (and possible combining the regression and ANOVA chapters: they're really the same thing). My second criticism is over the excessive use of DIC. This is a criterion for comparing how adequately different models fit to the data. This is really a philosophical complaint: the problem with using criteria like DIC is similar to one problem with hypothesis testing: it says something about the statistical properties of the models, but nothing about the substansive, ecological, properties. Given enough data, DIC will show that the more complex model is better. It will not show whether the extra effects are important in any real way. I would rather see a greater emphasis on examining the parameters, and using them to decide if the more complex model explains anything: does removing the parameters reduce the predictive ability substansively (rather than statistically)?

So, the book isn't perfect, but I would still recommend it to ecologists wanting to understand the basics of Bayesian methods: not only does it give a glimpse of what they can do, but it also allows them to do it. I hope it will help ecologists (and others!) get over the first few steps to doing Bayesian analyses, by giving a simple explanation, and code to run (and change!) on simple problems. After that, what follows is the same, but bigger. Once the first hurdle is passed, more advanced problems can be tackled with the help of Andrew Gelman's latest book, so it should not be too difficult to progress to the complex models of the sort I find myself working on.

Harry Potter and the Dying God

2007-07-21T22:07:00.000+03:00

Phew. Just finished the latest Harry Potter. And the trousers were a great help.

Yesterday I finished reading another book, Jealous Gods and Chosen People by David Leeming (it's on sale at the moment). For me, the two raised a couple of issues. So as not to spoil things, if you don't want to know what happens, don't read on...

Two things struck me. The first is something that's been bothering me about some strands of Christianity for some time, which David Leeming brings up and which is also a big theme in the Potter books. This is the idea of Love as an important force. The Christian message, for me, is that it is through Love, i.e. actually having concern and consideration for other people, that we are redeemed. This theme of the importance of love also plays a big part in the Harry Potter books, even if it is not always pushed down our throats (e.g. Narcissa). For me this is an important message, but one that is easily forgotten: it's why some of the fundamentalist Christian sects are so wrong. It's also why I get annoyed with atheists who attack anyone who believes in a God for being deluded and what have you: I have known several people (e.g. my ex-landlady in Norwich) who have been good, generous, loving Christians, and it has been their Christianity that has been the focus of this. Personally, I'm happy for people to believe in a God, if it helps them lead good lives.

Of course, some Christian groups will attack the Harry Potter series for promoting witchcraft (of course, because they already have). It's a shame, and rather ironic, that this means attacking a series of books that espouse and promote the philosophy of the person who's philosophy they claim to follow.

My other thought is more academic. Leeming talks about the Dying God myths, where a god or hero goes into the underworld to reappear later, signifying some sort of rebirth such as of the seasons, or of a spiritual redemption. This is a theme in several myths, not just in the Middle East, and the tales tell of these heroes descending to strange places, possibly crossing seas and rivers to get there, and gaining wisdom whilst amongst the dead, and letting their bread go mouldy. Harry Potter does this too. But why does his underworld have to be Kings Cross Station?

Harry Potter and the Change of Trousers

2007-07-21T10:22:00.000+03:00

It's been warm this week, so I've been wearing shorts. Today, however, I'm back in long trousers, even though it's no cooler. Why?

Well, because of this:

Yes, I have the new Harry Potter, and I'm not afraid to read it.

But what I am afraid of is the beast. For his claws are sharp, and if I'm going to be laying on the sofa all day, he's going to want to sit on me. With shorts, I have no protection from when he catches things in his dreams. Hence the long trousers.

I sense a long (607 page) day ahead, with brief breaks for food, toilet and turning on Test Match Special. No doubt Kevin Pietersen will be praying for rain, so he can sit in the corner all day reading.

African or European?

2007-07-18T09:48:00.000+03:00

Sometimes I feel obliged to make the obvious joke, just so that nobody else has to embarrass themselves. So it is with this paper.

I'll look at it properly later, but for the moment it suffices to say that they look at the flight speed of different birds, and try to explain that by allometry (shape) and other things. The important point is that they miss of one species: the fully-laden swallow.

Shame on them!

Reference: Alerstam T, Rosén M, Bäckman J, Ericson PGP, Hellgren O (2007). Flight Speeds among Bird Species: Allometric and Phylogenetic Effects PLoS Biology 5(8), e197 doi:10.1371/journal.pbio.0050197

On the growth of literary yeast genes

2007-07-17T09:45:00.000+03:00

I think one reason I enjoy working as a statistician so much is that we can use the tools of my trade to tease out patterns in data, and actually learn something about the world we live in. One aspect of this is the realisation that if one is trained in statistics, then one is aware of how much more can be extracted from the data. Oh, and also spot subtle mistakes. So, whilst I feel statistics is a service industry: there to help others in their work, there is also a strong element of either pride or egotism in my work. In the light of this...

A couple of computer scientists have just had a paper released in PNAS on "Temporal patterns of genes in scientific publications" (all figures below are from the paper). They looked at how references to genes changed over time in the scientific literature, in particular yeast genes, and whether there was variation in the way different genes were treated. I suspect this was done because it looked like a fun thing to do (a motivation I thoroughly approve of), but it does have implications for the way science is done. We might expect that some "super genes" are intrinsically more important, and hence there is more work on them than on others. Or the patterns of investigation might just be a result of following the herd: people study genes just because others are studying them. Now, a paper like this can't give a full explanation for the patterns of investigations, but it can give some useful information: it can show what the patterns are, and so help focus a more detailed investigation on the interesting aspects of what's going on.

What they did was to get their computers to trawl through the scientific literature and find all the mentions of thousands of yeast genes between 1975 and 2005. Having done that, they then asked how the pattern of references to these genes evolved. The data for two genes look like this:

ACT1 is the most popular gene, whereas PET54 is more typical. The solid lines give the actual data, the dotted line is from a simulation. They assumed a simple model where the number of references to a gene depended on three things:

the number of references to that gene in the previous year,
the number of references to all genes in the previous year, and
a background rate of reference.

So, they're imagining a system where work on a gene starts occurs randomly (this is the background rate), but the more work there is on yeast genes, the more likely work on a new gene will start. Makes sense: for whatever reason, someone gets a grant and starts to work on a gene. And this can be seeded by work on another related gene that is part of the same biochemical system. Once work on a gene is started, it should beget more work on that gene; it's good scientific practice, every answer brings up 3 more questions (and hence three more grant applications).

This model wasn't quite good enough though, so they added a saturation term: looking at the data, eventually the rate of citations slows down (for those who are interested, they use the Maynard-Smith and Slatkin model of density dependence). They then fitted this model to the data, assuming the same parameters for all genes (*). At about this point I think "whooo, dodgy". Determined to thwart me in proving my superiority, they then show that this model predicts the distribution of references pretty well. They suggest that what this is showing is that the assumption that the parameters are the same is reasonable. In other words, the genes are behaving equivalently, and there aren't any genes that are more important (and hence more highly cited) than others.

What this implies is that there is nothing intrinsically more important about any one gene as compared to any others: they are behaving as if they are equal, and the reason any one gene is researched more is just happenstance. Once genes become popular, they get researched on more. I guess this could be explained simply because there are more questions that can be asked about a better studied gene.

A few things bother me about this research, though. First, the interpretation of the parameters is off. The authors claim that, if there is no saturation, the growth rate of citations is 0.23, the sum of the growth rates for the gene effect and the general rate of gene reference. Well, um, OK. Let's look at the equation (ignoring saturation, for simplicity):

N_i,t = 0.028 P^*_t-1 + 0.20 P_i,t-1 + 0.005

N_t is the number of new references about a gene in a year, P^*_t-1 is the total number of references about all genes (including gene i) up to year t-1, and P_i,t-1 is the total number of references about gene i up to year t-1. So, the growth rate is only 0.23 (=0.028+0.20) if the number of citations for any one gene (P_i,t-1) equals the total number of citations of all genes (P^*_t-1) (and so the total number of genes is... work it out for yourself). There are over 4000 genes in the data base, so the strength of effect of the total genes should be much higher: on
average, the effect of P^*_t-1 shoud dominate.

They compound this error by claiming that most of the growth is caused by the gene itself. The model above says that an increase in one reference to one gene in one year leads to an average of 0.2 references in the next year, whereas an increase in one reference to a yeast gene in general leads to 0.028 references (on average) the next year. Look like the effect of a single gene is stronger, doesn't it?

No. There are 4000 genes, so there is more variation in the total number of references of genes. If we only had 100 genes, all equally cited, then if they all increased by 1 reference in a year, then P_i,t-1 would increase by 1, leading to an increase in reference due to this of 0.2, but P^*_t-1 would increase by 100, so causing an increase of 0.028*100 = 2.8. i.e. over 10 times higher.

Statistically, the point is that the interpretation of regression coefficients depends on the variation in the covariate: one can get larger coefficients simply by changing the measurements from kilometres to nanometres. Substansively, the point is that the dynamics are probably not being driven by single genes, but by the overall rate of growth in yeast genetics. This could be because yeast genetics overall is experiencing growth, or because there has been an inflation in papers generally in science. But it suggests that the dynamics are even more neutral: work on a gene does not beget more work on that same gene. But, I am not convinced that we can conclude this, because...

My other concern is that it's not at all clear to me that the model is working well. One problem is that the model seems to predict too few intermediate genes. This is their plot:

The dots are the observed frequency, the crosses are the simulated frequency. In the middle of the plot, the dots are all above the crosses, so too few genes are being predicted with intermediate frequencies. I'm not sure what this means: usually one would expect the opposite pattern, as variation in the parameters make some genes more extreme. My initial guess is that the rate at which genes appear is not homogeneous: if genes appear at a greater rate in the middle of the time period studied, then this is what we would see. The simulation would spread these genes out over the whole time period, so there would be more genes appearing early (so having more citations) and late (so having very few citations).

Unfortunately, I'm guessing about this, which bring me on to one of my big Statistician Are Important points. The way the model is checked is very simple, so it could be missing a lot of problems. My main worry is that the data were collected at the gene level, so I would like to see how the model predicts the behaviour of individual genes. In the first plot above we see a couple plotted, and it looks like the prediction for ACT1 is poor: it badly under-predicts the behaviour from about 1995. However, this is only one simulation: what about the rest? If they all do this, then there is a problem with predicting the popular genes, i.e. the ones that yeast biologist have decided are important.

There is a lot of data, but something as simple as plotting the residuals (the difference between the observed values and those predicted by the data) against the predicted values is often very telling. For example, if the model fit plot above was plotted as a residual plot, it would give a curve that would be frowning at us, whereas a good residual plot shouldn't have any visible pattern. This could simply be done for all the data (giving one huge plot!), or for subsets (e.g. sub-setted by popularity). I suspect all sorts of problems would be seen: some important, others trivial and ignorable.

So, OK. I'm not a great fan of the analysis. What would I do? I think the first thing would be to look at the data. There is too much to look at it all, but it might be possible to cluster the genes into groups according to their publication record, and look at representatives from each group. From that, I could get a feel for how they are behaving: are most increasing exponentially? Do many reach a plateau? Do they decrease in publication (as researchers get bored and move on to other genes)? Do different genes become popular at different times? Or do all the plots look similar? This would guide me in deciding what sort of model to fit.

Then I would look at fitting a model with different parameters for each gene. I might drop rare genes first: the ones that only get mentioned once or twice. Any estimates for them will be crap anyway. Now, this leads to a lot of parameters, but we can use an approach called hierarchical modeling: basically, assume that the parameters are related, so knowing about 20 parameter values tells us about the 21st. This gives us a direct way of measuring how similarly the genes are behaving: if the parameter estimates are almost the same, then the genes are acting the same. We can even ask where the differences are: is it that genes are appearing in the literature at different times? Or are the growth rates different - i.e. do some genes spawn 10 publications per publication whilst other only spawn 1 or 2?

Once this has been done, we can then check if the model actually works, i.e. if it actually predicts the data well. If it doesn't, then we have to do the process again, based on what we have learned. This can be something of an iterative process, but eventually it should converge to a reasonable model. Hopefully.

The advantage of the approach I'd envisioning is that it becomes more of a conversation with the data (or an interrogation, if it doesn't want to behave). The process is one of learning what the data is telling us, using the plots and models to pick out the important information. We then need to interpret this information (correctly!) to be able to say something about the system - i.e. whether the genes are behaving the same, or what are the differences in rates of reference. And also the extent to which work on one gene is driven by previous work on the same gene, or whether yeast genetics is working by looking at genes in general. One could even try to ask questions about whether this has changed in time: has there been a shift from focus on a single gene to a more general, holistic approach (i.e. do the regression parameters in the equation above change over time. More hierarchical models)? These are all questions that could be tackled: the techniques are there, as long as the data is prepared to cooperate.

Stepping back a bit, and trying not to trip over the cat, this shows a response I have to many papers. The questions are interesting, but the data analysis and interpretation can be improved. For this paper, the problems in the analysis are fairly typical: I suspect the authors are not aware of the techniques that are available. This is understandable, as statistics is its own field, and I would be as lost in, say, yeast genetics. It's because of this that statisticians are needed: even if it's just to have a chat to over coffee, or a pint of Guinness. The interpretation aspect is more of a problem for me: it does not need a great technical knowledge to understand what is wrong, just an appreciation of what the equations mean.

Overall, could do better. But I hope I've shown how to do better. Constructive criticism, and all that.

Reference: Pfeiffer, T. & Hoffmann, R. (2007) Temporal patterns of genes in scientific publications. Proc. Natl. Acad. Sci., 104: 12052-12056. DOI: 10.1073/pnas.0701315104.

* Technical note: they used a general optimisation algorithm (optim in R) to do this. However, given the values of alpha and P_S, the model is a generalised linear model with an identity link. So, they could have used used this fact, and just written a function that would optimise alpha and P_{_S}, fitting the model as part of the function. Actually, I think I would have started with a density dependence function with one less parameter: even easier!

Asturias Report

2007-07-14T20:58:00.000+03:00

Right, folks.  Finally, having finished my week-long battle with a manuscript, I can finally report on my trip to Asturias.  I should start by thanking Alfredo, Cano and David for their hospitality: the food and drink was excellent, as was the conversation.  Especially the tail of the vegetarian dog (short version: owner claims dog is vegetarian.  Dog disproves this, using neighbour's rabbit).

My purpose was to try and teach Bayesian inference to the biologists there.  I don't want to report on that, other than to point out my new variant on the Monty Hall problem:


In my variant, you don't want to win the car.  Actually, you're not too keen on winning the bull either.  In fact, it looks like loosing is a decent option.

The only other things worth pointing out about the actual course are the exhibit downstairs:
(which is just an excuse to link to a well known science blog).  The other point is the view from the window of the room I was teaching in:
Yes, hills!  Scenery!  I suspect I'd really enjoy the weather in Asturias: I can enjoy the rainy, overcast weather when it looks really dramatic.  I think it's something about being English.  And we did have quite a bit of sun, although overall the weather was changeable (and only foggy on Saturday).

As to Asturias itself, I was teaching in Oviedo, which is the capital  of Asturias.  It has historical bits, it has modern bits, it has everything you expect from a town.  It also has strange statues - most of which would shock the sensitive.  Hence, no photos.

The kingdom of Asturias was founded by King Pelayo in the 8th century. To do this he had to beat off the Moors, who were intent on taking over the whole of Spain and installing wallpaper patterns in their palaces.  Pelayo found an effective strategy to combat them: one used by the Scottish previously against the Romans.  His simply made sure that the weather was awful enough that the Moors wouldn't want to invade. Hence he is honoured in Gijon with a statue of him, looking out to sea with an early anenometer:
The Asturians also honour him in their drinking traditions.  They pour their cider from a great height, enabling them to judge the wind speed and direction (and its strength, when they drink it, tells them how hard it's raining):
This, incidentally is a real cider: for those who haven't tasted it, it's like a good English scrumpy.  Which means it taste nothing like a commercial cider.  This stuff has had apples in it!

On the Wednesday, Cano took me to Gijon for the afternoon.  Gijon is his home town, and it more working class (read: could do with some paint in places).  But it is on the coast, and ahs a nice beach, and some decent touristy bits. This is Cano:
and these are his feet:
Cano had to go for a swim in the Atlantic: I think he was forgetting what waves are like, as Finland doesn't have waves, only wakes.  After Cano had had enough of waving his feet at us, we went for a walk around the town, and came across this curious sight:
Apparently when they were building the quay, the townsfolk got up one morning after a spring tide and found that an English captain had berthed his ship on top of the building site.  Rather than remove the ship, they decided to build the rest of the quay over the top, and just fill in with concrete.  The captain went down with his ship.

After that story, there was only one thing to do: go for a drink.  So we retired to a bar and had some cider:

North by north-west, force 6.

This is what I should look like

2007-07-11T19:42:00.000+03:00

I had been intending to post some photos from my Asturias tip, and a couple of other things. But I've been fighting a manuscript all week. In the mean time, as everyone else has been doing it, here is my Simpsons avatar:
Apart from the problem cause by a shortage of beards, I think it wasn't too bad.

Get yours from the Simpsons' movie page.

The Travails of Travel

2007-07-08T18:40:00.000+03:00

Phew, I finally got back from Spain. Air travel horror stories are hardly rare, and I'm sure this one deserves the response "so, what's all the fuss about?". To which I can only reply "but it's my story".

The plan was to take a taxi from the hotel in Oveido to Asturias airport, and make sure they give me a factura (sorry, not much use if you don't speak Spanish). This bit went off fine, I have the receipt and during the trip I was afforded magnificent views of the Asturian landscape, with the low-hanging cloud, and mist (I'm not being sarcastic - I actually like the scenery like this).

My original flight plan was Oveido - Barcelona - Frankfurt - Helsinki. When I got to the airport, I was told that the flight to Barcelona was delayed by two hours, because of the fog, so I was switched to a flight to Madrid, and after that to Frankfurt then the same flight to Helsinki. Great I thought (and sometimes I am sarcastic). I hate Madrid airport - after sitting around in it for a couple of hours, you realise that there is a real difference between bland and soullessness. Anyway, I checked myself and my luggage in and waited to be told we could go through the security gate. But then... the flight was cancelled. The plane from Madrid had been diverted to an alternative destination - Madrid. Poor passengers.

So, I had to get a new flight. The Spannair ladies were doing a magnificent job - once the cancellation was announced, I went to their desk (again), and found that they were already finding me a new route. They sorted that out, got my luggage back, and explained to the Iberia check-in staff what was going on. My new route was (and get your maps out for this, folks) Asturias - Paris (Orly) - Nice - Helsinki, getting into Helsinki at about midnight. Well, OK. If that's what it takes.

The good news is that this time the flight took off (despite the observation at check-in that they would tell us the gate later - if they could get us a plane), and got to Orly with no problems. I managed to get checked in and onto the flight to Nice with not much more than the occasional piece of Parisian impoliteness. I had been given a window seat, so I had the opportunity to watch the ground crew doing their stuff preparing the plane: refuelling, loading the luggage, and driving away from the plane. With my bag.

It's difficult to know how to respond to this. Leaping out of my seat shouting "Come back with my bag!" would hardly be effective. The British thing to do would be to ring the bell for the flight attendant to ask them if they could possibly tell the ground crew, that the bag they were driving away should be on the plane. And could they, if it wasn't too much trouble, bring it back and put it in the hold. But it was obvious that this wouldn't have any effect - I'm sure it would have taken too long to sort out, and instead nothing would be done - and this is probably the right reaction, otherwise the plane would have been horribly delayed. So, instead I did the other British thing - once we were in the air, I ordered a beer.

nice airport is quite Nice, even if I couldn't find any biscuits for sale. I managed to get checked in (again), and the lady was very sympathetic about my worries over my bag. Waiting for this last flight took me back into the Finnish sphere of influence - I guess I've been here long enough, so it's comforting to hear Finnish spoken, even if I don't have a clue what they're saying. This was a Blue 1 flight, which means you have to buy your own food and drink. Obviously, the prices are sky high (Laugh at that. Please) but equally obviously there are great profits to be made from keeping 200 Finns stuck in a metal box for 3 hours and selling them alcohol. Looking back, it is clear that there are some aspects of Finnish character that I have absorbed.

We touched down in Helsinki on time (i.e. midnight), and I had the odd experience of waiting for my luggage that I knew wasn't going to turn up. I think I had to do this - if I had just gone to report it missing, I'm sure I would have been asked how I could be so sure. Once my had luggage failed to turn up, I went to the desk to complain about this. The lady was very helpful, and discovered that it had been kept in Orly, and that apparently they didn't know what to do with it (it seems that it's not possible for luggage to travel from Paris, Orly to Paris, Charles de Gaulle), so they sent it to Nice. I wonder what the Nice people will think of this - the next direct flight is on Monday. So either they have to sort out its re-routing, or my baggage will be waiting for an extra day on the Cote d'Azur. I hope it doesn't get sun-burnt sitting on the beach all day.

All of this is a long-winded way of explaining why I haven't put up a load of photos of Oviedo and Gigon. Because you can guess where my camera is. Well, actually I'm not even sure you'll be able to do that.

ID advocate keeps quiet

2007-07-03T20:40:00.000+03:00

Time to come clean - I'm a regular reader of Uncommon Descent, and sometimes contribute. UD is the Intelligent Design blog for Bill Dembski, who's one of the better known ID advocates, and about whom much could has been written.

UD has a new post up, announcing the 2007 IDURC Casey Luskin Graduate Award. This is noteworthy for a couple of points. Firstly, IDURC is the Intelligent Design Undergraduate Research Center, but the award is for "excellence in student advocacy of intelligent design". Why is a research centre (sorry, center) supporting advocacy? Of course, this is just another symptom of one of the problems with ID: it spends its time and effort advocating a "scientific" that
hasn't been researched yet.

The second point is just beautiful. The winner of the prize is to remain anonymous. So the announcement is that they have decided to award a prize to a person. The reason given for anonymity is that they are worried that the student will be discriminated against for their views. So, we don't know who this student is who "...has demonstrated excellence and courage in research and promotion of intelligent design".

Spotted the punchline yet? The award is for advocacy of ID, and the student is being rewarded for promoting ID. If they've been any good at this, then it seems a bit late to keep their identity quiet.

In Spain, and bulls

2007-07-01T23:37:00.000+03:00

Well, I arrived in Spain OK, via various queues. The wireless in the hotel is working, sort of. Hopefully the university will be better tomorrow.

I flicked through the channels on the TV in the hotel, and came across some bull fighting. Now, being English, and therefore Fond of Animals, this is not something I've ever been greatly enamoured of. But I thought it was worth watching, just so that I knew what was going on. Overall, I can't see it taking off in Britain, although there would be some amusement to be had from them trying it at Headingley, as anyone who's sat in the Western Terrace could appreciate.

I think part of the problem (apart from the sock-down-the-trousers posturing) is that the poor bull reminded me of Jack, the cat. One way of keeping Jack entertained is to wave things around at him, and let him pounce on them, bat them away, chew them etc. The various bull-fighters do something similar to the bull - the main difference being that I have absolutely no intention of sticking any sharp objects into Jack (alas, he doesn't always reciprocate in this). By the end, I felt like I wanted to go up to the bull, put my arms round its neck and give it a big hug. Poor thing. Poor me if I ever try it. The social embarrassment alone would be enough for the bull.

The rest of Spain is better, though. Good food and wine already. I just have to work for it for the rest of the week. I'll try and post some photos of Asturians pouring cider later in the week.

Off to Spain

2007-06-30T21:32:00.001+03:00

OK, I'm off bright and early tomorrow to Spain, to spend a week complaining about the heat. I'll try and post some photos, if I can get time.

Until then, on the off-chance that someone reading this hasn't read it already, GrrlScientist has a wonderful post up about trans-Arctic dispersal in the rubber duck. She's been very kind and helpful with advice already, so go along and take a look. And if you're in the UK, make a note of what the ducks look like - you may meet one on a beach some day.

2007-06-29T19:53:00.000+03:00

The Royal Society Of London has just sent out the Table of Content for Proc. R. Soc. B. It includes this:

Genetic analysis reveals promiscuity among female cheetahs
p. 1993
Dada Gottelli, Jinliang Wang, Sultana Bashir, Sarah M. Durant

URL of article: http://www.journals.royalsoc.ac.uk/link.asp?id=8853823PG881552J

Some puns just shout so loudly, you have to ignore them. Need I say more?

Actually, I will: Vesa, my former office-mate has a paper in there too. Flying squirrels - cuter but less important than Harry Potter.

Oh, the irony

2007-06-27T22:12:00.000+03:00

So, the big British political news this morning was the defection of a Tory MP to Labour. As part of his resignation letter, Quentin Davis wrote

Under your leadership the Conservative party appears to me to have ceased collectively to believe in anything, or to stand for anything.
It has no bedrock. It exists on shifting sands. A sense of mission has been replaced by a PR agenda.

and

Although you have many positive qualities you have three, superficiality, unreliability and an apparent lack of any clear convictions, which in my view ought to exclude you from the position of
national leadership to which you aspire...

Is it just me, or is this, particularly the last part ironic on the day that Tony Blair resigned? We were hearing exactly the same things about him 10 years ago.

When did Peter Mandelson defect in the other direction?

The Big Harry Potter prediction

2007-06-26T20:18:00.000+03:00

I was (re-)writing a lecture for a course I'm giving next week in Asturias, and included an example of prediction. I thought it was sufficiently interesting to preview here. Feel free to disagree.

The background: even if you haven't heard now, you'll soon be sick of hearing that HP7, Harry Potter and the Deadly Hallows is released next month. There are lots of unknowns, which give us plenty of scope to predict what will happen, and then hope nobody remembers when they're wrong.

So, I decided to make my own predictions - after all, that is what the lecture is about. So, I've tried to predict how many pages the book will have. As the course is in statistics, we need data. For that I use the number of pages in the previous books, and the dates they were published:

Odd, it looks like Ursa Major. It must be a clue! Perhaps it's pointing north, so Harry will have to go to the frozen northlands, be helped by a polar bear in his quest, and in the final climax end up betraying a friend in order to save everything, and break through to a new universe.

*ahem*

Anyway, now we do a linear regression. Because the course is about Bayesian analysis, I did a Bayesian prediction. This meant firing up BUGS, fitting a straight line, and predicting the number of pages for a Harry Potter book published in 2007. I also made a prediction based on the least squares estimate of the line. This is what I got:

The thick bars are +/-one standard error, the thin bars are the 95% confidence/credible intervals. Next week I will blather on about why the Bayesian intervals are wider, but for the moment, I want to point out that I'm predicting that HP7 will be about 860 pages long, but with a wide margin of error: there's a 95% probability that it will be between 500 and 1200 pages long.

And I get paid to do this stuff.

OpenBUGS3.0.1 released

2007-06-25T15:29:00.000+03:00

Of the many "it seemed like a good idea at the time" things I have done in my life, one was to volunteer to set up the OpenBUGS pages. I did this in the spirit of helping out, rather than one of showing off my html skills (I hope this is obvious from the pages). Nowadays this mainly means transferring a zip file from a memory stick to our we server, and then checking I haven't broken anything. The real programming work is done by Andrew Thomas, so he's the one who should get all the thanks now.

Anyway, as you might have guessed from the title, I've just put version 3.0.1 of OpenBUGS up on the web. Actually, this is a slight cheat - I put a version up a couple of weeks ago, and then we promptly found a couple of bugs/features. Andrew solved both of them, although one solution is geopolitical - giving the Åland islands back to Sweden.

The good news about the changes is that it gave Andrew more time to work on the BUGS to LaTeX language converter, ably supported by Mikko Sillanpää. Of course, the first thing I tried with it was throwing some of my more complex models at it, but it seems to manage. For example, here is what the LaTeX code produces for this paper:

OK, it's not perfect (and looks better on its own: bits are disappearing off the edge of the window) but it is fairly easy to edit the LaTeX code. There are also a few changes to the BUGS code that would make it more readable (e.g. writing tau.S, rather than tauS, so that BUGS recognises the tau). Overall, I'm impressed, and suspect I'm going to drift even deeper into the clutches of LaTeX now.

So, I'm happy for a bit - even if I should be doing N other things this week.

Hello World!

2007-06-24T19:30:00.000+03:00

Hello?

*tap tap*

Is this thing on?

Hmmm, I wonder what this button does.

Ah.

I won't try that again for a bit.

Err, hello. After wandering the blogosphere for some time, and mugging posts with silly comments and games of Mornington Crescent, I finally decided to create my own blog, so I could pontificate on the world.

For anyone who doesn't know me, I'm an Englishman who has drifted to Finland, and now work at the University of Helsinki as a researcher, in statistics, ecology, evolutionary biology and such things. I have mainly been hanging round the science blogs, so I'll probably post on these matters, but there will also be other things that take my fancy as well (like possible uses for cat hair).

OK, now I have to think of something to write about.