Andrew B. Collier / @datawookie

Following on from my previous post about Near Earth Objects, today we are going to solve Kepler’s Equation to find the eccentric anomaly, which is the next step towards plotting the positions of these NEOs relative to Earth.

With this year’s Comrades Marathon just less than a month away, I was reminded of a story from earlier in the year. Mark Dowdeswell, a statistician at Wits University, found evidence of cheating by some middle and back of the pack Comrades runners. He identified a group of 20 athletes who had suspicious negative splits: they ran much faster in the second half of the race. There was one runner in particular whose splits were just too good to be true. When the story was publicised, this particular runner claimed that it was a conspiracy.

The recent story about a skydiver nearly being hit by falling meteor got me thinking about all the pieces of rock floating around in near-Earth space. Despite the fact that the supposed meteor was probably just a chunk of rock mistakenly packed in with a parachute, the fact that something like that could actually happen is quite intriguing. And not a little frightening.

Myfxbook provides an interface to your FOREX trading accounts as well as an active trading community.

The next stage in my earthquake analysis project is to partition the events into groups with epicentre over land or water.

Around 199 years ago the largest volcano in recorded history, Mount Tambora, erupted, spewing an enormous volume of molten rock and ash into the atmosphere and onto the surrounding land.

I am working on a project related to secondary effects of earthquakes. To guide me in the analysis I need a chart showing the location, magnitude and depth of recent earthquakes. A host of such charts are already available, but since I had the required data on hand, it seemed like a good idea to take a stab at it myself.

Does the transition to and from Daylight Saving Time (DST) have a (significant) effect on the stock market?

A few days ago I posted about Filtering Data with L2 Regularisation. Today I am going to explore the other filtering technique described in the paper by Tung-Lam Dao.

I have just finished reading Momentum Strategies with L1 Filter by Tung-Lam Dao. The smoothing results presented in this paper are interesting and I thought it would be cool to implement the L1 and L2 filtering schemes in R. We’ll start with the L2 scheme here because it has an exact solution and I will follow up with the L1 scheme later on.

This morning my wife presented me with a rather interesting statistic: a healthy couple has a 25% chance of conception every month [1], and that this should result in a 75% to 85% chance of conception after a year. This sounded rather interesting and it occurred to me that it really can’t be that simple. A lot of variables influence this probability. Certainly age should be a factor and, after a short search, I found some more age-specific information which indicated that for a woman in her thirties, the probability is only around 15% [2,3].

I got quite inspired by the EXIF with R post on the Timely Portfolio blog and decided to do a similar analysis with my photographic database.

On Friday I received my copy of The Official Results Brochure for the 2013 Comrades Marathon. Always makes for a diverting half an hour’s reading. And the tables at the front provide some very interesting statistics. Seemed like a good opportunity to update my Chart of Comrades Winners.

I am busy working on a project which uses data from the World Wide Lightning Location Network (WWLLN). Specifically, I am trying to reproduce some of the results from Orville, Richard E, Gary R. Huffines, John Nielsen-Gammon, Renyi Zhang, Brandon Ely, Scott Steiger, Stephen Phillips, Steve Allen, and William Read. 2001. “Enhancement of Cloud-to-Ground Lightning over Houston, Texas”. Geophysical Research Letters 28 (13): 2597–2600.

Amy Cuddy gives a great talk. Provided me with lots to think about and I will happily confess that I have struck a few power poses (but only after ensuring that I am quite alone)!

Following up on my recent post about implementing a queue as a reference class, I am going to derive a Priority Queue class.

I am working on a simulation for an Automatic Repeat-reQuest (ARQ) algorithm. After trying various options, I concluded that I would need an implementation of a queue to make this problem tractable. R does not have a native queue data structure, so this seemed like a good opportunity to implement one and learn something about Reference Classes in the process.

The Implementation

We use setRefClass() to create a generator function which will create objects of the Queue class.

According to Wikipedia, an iterator is “an object that enables a programmer to traverse a container”. A collection of items (stashed in a container) can be thought of as being “iterable” if there is a logical progression from one element to the next (so a list is iterable, while a set is not). An iterator is then an object for moving through the container, one item at a time.

Iterators are a fundamental part of contemporary Python programming, where they form the basis for loops, list comprehensions and generator expressions. Iterators facilitate navigation of container classes in the C++ Standard Template Library (STL). They are also to be found in C#, Java, Scala, Matlab, PHP and Ruby.

A short while ago I was contracted to write a short piece entitled “Introduction to Fractals”. Admittedly it is hard to do justice to the topic in less than 1000 words.

In my previous post about estimating the Percolation Threshold on a square lattice, I only considered flow from a given cell to its four nearest neighbours. It is a relatively simple matter to extend the recursive flow algorithm to include other configurations as well.

Malarz and Galam (2005) considered the problem of percolation on a square lattice for various ranges of neighbor links. Below is their illustration of (a) nearest neighbour “NN” and (b) next-nearest neighbour “NNN” links.