Alternative function: Simple moving average

Download
movAv.m
(see also movAv2 - an updated version allowing weighting)

Description
Matlab includes functions called movavg and tsmovavg ("time-series moving average") in the Financial Toolbox, movAv is designed to replicate the basic functionality of these. The code here provides a nice example of managing indexes inside loops, which can be confusing to begin with. I've deliberately kept the code short and simple to keep this process clear.

movAv performs a simple moving average that can be used to recover noisy data in some situations. It works by taking an the mean of the input (y) over a sliding time window, the size of which is specified by n. The larger n is, the greater the amount of smoothing; the effect of n is relative to the length of the input vector y, and effectively (well, sort of) creates a lowpass frequency filter - see the examples and considerations section.

New graphics in Matlab 2014b and 2015 and creating nicer figures in one line of code

Download
og.m
ng.m
see also Matlab plot formats and how to quickly save high quality figures for hgx, a function to handle exporting figures.

The new graphics system in Matlab 2014b has a few handy features, including:

• Perceptually-equally spaced default colours (although be careful, red is near green)
• OpenGL hardware rendering - smooth lines!
• Easier access to handle properties using structure syntax - eg. handle.FontSize = 12, instead of set(handle, 'FontSize', 12). 

Even so, it still takes a little bit of effort to render nice figures and graph in Matlab, the default settings don't look great. Generally lines need to be made thicker, fonts bigger, etc., etc. Each setting requiring a line of code. Also, OpenGL can be a bit unreliable; it switches to software rendering on a whim, which disables features like anti-aliasing.

Alternative function: nansum (multi-dimensional)

Download
nansum.m

Explanation

See nanmean3 for a walk through of the code, the structure of the code is exactly the same, this version just uses SUM instead of MEAN.

Race model inequality script; R. Ulrich, J. Miller, H. Schröter, 2007

http://link.springer.com/article/10.3758/BF03193160#

Behavior Research Methods

May 2007, Volume 39, Issue 2, pp 291-302

Testing the race model inequality: An algorithm and computer programs

Rolf Ulrich, Jeff Miller, Hannes Schröter

In psychophysics the race model is used to compare reaction time data from two conditions (X,Y) to a third condition (Z). For example, response times to an auditory task and a visual task, compared to an audiovisual task. It tests if reaction times are faster in the third condition, and if so, they violate the race model and (possibly, depending on the task) represent multisensory integration. The race model inequality, simply, is defined as  P(RTz) < P(RTx) + P(RTy). For a better, and far more detailed explanation and run through - see the above paper!

In this paper, the authors present an algorithm to test the race model inequality and include a set of functions for Matlab that implements the algorithm, although no supplementary .m file appears to be included. The code is in the .pdf, but copying from a .pdf is, as usual, an unnecessarily painful experience.

Here is a slightly modified version of the code included in the paper, it's copied from the .pdf and corrected for various illegal characters, line breaks, etc. etc. Apart from a couple of minor edits, nothing else is changed from the authors original work.

Also included as an example of how to use RaceModel.m is a script (RaceModelTester.m) that generates some reaction time data in the correct form to use with RaceModel.m. The RT data supplied to RaceModel.m should be in three rows, rounded and in milliseconds. There should also be a vector of probabilities, and a true or false to indicate whether to create a plot or not.