All these numbers and what they mean

There are innumerable facts which are indecipherable and paradoxes which make up facets unexplored in terriotories unscaped. I recently happened to talk about the two envelope exchange paradox and how we define the expected return value. The sample space defined can make up many numbers giving me a higher expected return to always make a change in the envelope. This problem is a paradox, because once you made the change and if you were offered the chance again, we would make the switch back. Of course, the problem is innane if we consider only the two envelopes.

To make things interesting, lets say you chose the other envelope, without actually knowing the values quantitaively. Now instead of on the next trial, being offered the same two envelopes, you are offered a new envelope and posed the same argument, would this change your behaviour. The fact is that it is still the same problem, but you somehow think you can deduce the "objective payoff" better.

So what do the numbers mean, the numbers don't exactly mean what they are. It is embedded in a deep sense in the way you see it and understand it. Dabbling in the science of data and visualizations, I have realized there are many ways to look at the same numbers to infer different mechanims and parameters. The objective function bias is very subjective and is inherently fixed in the representation of the problem.

Perhaps, in the future, my bias will be neutral and I will be able to see through the indecipherable facts and paradoxes.

Deciphering the stats

So, I have been busy the past few months with an experiment in measuring attentional drift. The majority of the time was spent in data analysis. Coming from a computer science, more specifically a c++/python background, I had to get used to the R way of doing things.

I figured I need to learn R and decided to undertake the due process of wrecking my own mind with the absurdities of yet another language. I know I am not an expert at c++ or python, but I feel I can use it to my advantage and organize my coda (pieces of code). I am of course borrowing the phrase from the musical theory, which is definitely more artful than coding. Although the underpinning complexities and structures you may find are similar and the expressive nature of the coda is at hands of an able artist.

After many goof ups and fall downs I now feel I am in a place to talk about R and build an understanding of how it works and why it works. The primary draw of R is the innumerable number of packages you have to accomplish tasks statistical in nature. R is primarily a statistical language and should be used for such purposes.

To install R:

sudo echo "deb http://ftp.ussg.iu.edu/CRAN/bin/linux/ubuntu precise/" >> /etc/apt/sources.list
sudo apt-get udpate
sudo apt-get install r-base r-base-dev

As a first step to the introduction to R I will establish a very nice protocol, I use emacs and ESS. These two together have made my life a lot easier. I also split my sources and write all code in functions. I have to start using objects, hopefully sooner to organize my code.

I shall describe how to install ESS and the basic run in ESS:

To intall ESS just run

$ sudo apt-get intall ess

Once you have installed ESS, go to your emacs and type

M-x R

This should bring up a prompt which asks you for the starting data directory, you can enter the path to the directory you want to work from or just work from the current directory.

You can of course always change the working directory using setwd(path). To see all the files in a directory just say list.files(path). You can include a R source file into the shell by using source(filename).

I will write about installing and using libraries in my next post. Happy coding !!