Its about time Python get serious with Statistics! Introducing Scikits.statsmodels (Linux Ubuntu)), Part 1

You can of course perform powerful statistical routines using Python, Rpy module and R. But greater joy is now coming to pure Python users with the Scikits.statsmodels package.

• Install the scikits.statsmodels module
Be sure that your versions of scipy and numpy are the latest or later than the following:

Python >= 2.5
NumPy >= 1.4.0 
SciPy >= 0.7

You can install the latest versions by typing sudo apt-get install python python-setuptools python-numpy python-scipy
• Download the software scikits.statsmodels module
In a terminal type wget -ct 0 http://pypi.python.org/packages/source/s/scikits.statsmodels/scikits.statsmodels-0.3.1.tar.gz#md5=1f55b53d161544b95ca2709c9731c00c Untar it,then descend to the directory.

tar -xvvf scikits.statsmodels-0.3.1.tar.gz
sudo python setup.py install

• Test the installed module
Lets try this from ipython(Again, do a sudo apt-get install ipython if it is not yet installed.

import scikits.statsmodels.api as sm
help(sm)

The following will be printed:

Help on module scikits.statsmodels.api in scikits.statsmodels:  NAME     scikits.statsmodels.api - Statistical models  FILE     /home/toto/testsoftware/scikits.statsmodels-0.3.1/scikits/statsmodels/api.py  DESCRIPTION      - standard `regression` models            - `GLS` (generalized least squares regression)       - `OLS` (ordinary least square regression)       - `WLS` (weighted least square regression)       - `GLASAR` (GLS with autoregressive errors model)            - `GLM` (generalized linear models)       - robust statistical models            - `RLM` (robust linear models using M estimators)       - `robust.norms` estimates       - `robust.scale` estimates (MAD, Huber's proposal 2).       - sandbox models       - `mixed` effects models       - `gam` (generalized additive model 

What is available by importing sm? Type dir(sm) and the following functions or attibutes will be listed:

In [17]: dir(sm)
Out[17]: 
['GLM',
 'GLS',
 'GLSAR',
 'Logit',
 'MNLogit',
 'OLS',
 'Poisson',
 'Probit',
 'RLM',
 'WLS',
 '__builtins__',
 '__doc__',
 '__file__',
 '__name__',
 '__package__',
 'add_constant',
 'categorical',
 'datasets',
 'families',
 'iolib',
 'nonparametric',
 'regression',
 'robust',
 'test',
 'tools',
 'tsa',
 'version']

• Additional resources
Home page of pystatsmodels: http://statsmodels.sourceforge.net/ Subscribe to the Google group: http://groups.google.com/group/pystatsmodels
• The regression example from the homepage
It is time to show what statsmodels can do. Here is a regression example from the home page: http://statsmodels.sourceforge.net/

import numpy as np
import scikits.statsmodels.api as sm


# get data
nsample = 100
x = np.linspace(0,10, 100)
X = sm.add_constant(np.column_stack((x, x**2)))
beta = np.array([1, 0.1, 10])
y = np.dot(X, beta) + np.random.normal(size=nsample)


# run the regression
results = sm.OLS(y, X).fit()

# look at the results
print results.summary()

Here is the results by running the above published example:

     Summary of Regression Results     
=======================================
| Dependent Variable:            ['y']|
| Model:                           OLS|
| Method:                Least Squares|
| Date:               Wed, 07 Mar 2012|
| Time:                       17:58:02|
| # obs:                         100.0|
| Df residuals:                   97.0|
| Df model:                        2.0|
==============================================================================
|                   coefficient     std. error    t-statistic          prob. |
------------------------------------------------------------------------------
| x1                      1.060         0.1353         7.8344         0.0000 |
| x2                    0.09398        0.01309         7.1802         0.0000 |
| const                   9.848         0.2927        33.6490         0.0000 |
==============================================================================
|                          Models stats                      Residual stats  |
------------------------------------------------------------------------------
| R-squared:                     0.9729   Durbin-Watson:              1.803  |
| Adjusted R-squared:            0.9724   Omnibus:                    3.108  |
| F-statistic:                    1742.   Prob(Omnibus):             0.2114  |
| Prob (F-statistic):         9.776e-77   JB:                         2.791  |
| Log likelihood:                -139.9   Prob(JB):                  0.2477  |
| AIC criterion:                  285.8   Skew:                     -0.4176  |
| BIC criterion:                  293.6   Kurtosis:                   2.927  |
------------------------------------------------------------------------------

So far so good. We hope that the statsmodels module be more robust, easier to use and inspiring enough for Python users users to contribute code!