Follow Email For the past couple years, I’ve been using Kieran Healy’s lovely template for my academic CV. Kieran’s code is a customised *.tex file which, of course, has the virtue of simplicity. All a person needs to do is update it with glorious achievements from time ... [Read more...]

Follow Email Last week I posted about an orphaned paper of mine, a piece of research that hasn’t yet found a home and which I’m unlikely to find further time to work on. Abandoning something that you’ve worked hard on is disappointing and so it made sense ... [Read more...]

Follow Email I wanted to add my two-cents worth to the “Deflate-gate” statistics row that’s been going on recently. If you want to catch up with the story, then you should start with the original articles at Sharp Football Analysis (1, 2) and this critique by Gregory J. Matthews and Michael ... [Read more...]

Email In my last post, I looked at the success rates for EPSRC Fellowship applications using funnel plots. As luck would have it, Alex Hulkes and Derek Gillespie from EPSRC then got it touch to say that they had done a similar internal analysis and would I be interested in ... [Read more...]

Email I was recently at a presentation where the success rates for EPSRC fellowships were given by theme. The message of the talk was that Engineering fellowships were under-subscribed and so we should all be preparing our applications. But just because a theme is under-subcribed doesn’t mean that you’... [Read more...]

My new R package, scholar, has just been posted on CRAN. The scholar package provides functions to extract citation data from Google Scholar. In addition to retrieving basic information about a single scholar, the package also allows you to compare multiple scholars and predict future h-index values. There’s a ... [Read more...]

Email Previously I looked at how to simulate Gaussian processes in R, following the methods in Rasmussen and Williams. But now that Andrew Gelman et al. (of [Read more...]

Email One of the historic difficulties of doing research on urban energy systems has been the limited availability of data at sufficiently detailed spatial resolutions. Without this data, you might end up relying on aggregate information about the built environment, building occupants, and local geography that doesn't apply to the ... [Read more...]

In which Mo Farah et al show James that he's got a nasty heel-strike [Read more...]

I’m currently working my way through Rasmussen and Williams’s book on Gaussian processes. It’s another one of those topics that seems to crop up a lot these days, particularly around control strategies for energy systems, and thought I should be able to at least perform basic analyses ... [Read more...]

I’ve been working through Gelman et al.’s otherwise excellent Bayesian Data Analysis and it’s going reasonably well. My statistics is a little bit rusty so it’s taken time to work through all of the exercises and really understand what’s going on. But I say “otherwise ... [Read more...]

I've knocked together a quick function for generating efficient Monte Carlo samples. It takes a bit of the legwork out of running Monte Carlo simulations. [Read more...]

Tweet When I first started using R, one of the things that attracted me was its claim to be an object-oriented programming (OOP) language. Coming from a Java background, I was used to designing software with OOP concepts like encapsulation and inheritance but, when I turned my hand to R, ... [Read more...]

Ever have a regression model where the coefficients don't make sense? I've been trying to predict electricity and gas consumption from daily activity schedules but a simple linear regression kept saying that demands should go down the more an activity is performed. Fortunately I found the nnls package and show ... [Read more...]

In the early stages of a literature review, you may have hundreds of papers and not know how to even begin sorting through them. In this post, I show you how to perform a two-stage clustering analysis with R so that you can identify the main groups within your data, ... [Read more...]

Apples, oranges, pears or bananas? Bus, train, car, or walk? Many choices are made between more than two options, a situation that can be represented by multinomial choice modelling. Here's a quick tutorial on how to do it in R. [Read more...]

Ever wondered how to predict success or failure as a function of other variables? Here's a quick tutorial on binomial regression in R. [Read more...]

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