[SciPy-user] Multivariate regression?

Ryan Krauss ryanlists@gmail....
Fri Oct 5 16:10:02 CDT 2007

This may be unnecessarily complicated, but I almost always use fmin
where I have defined some squared error that I want to minimize.  This
essentially lets you do an arbitrarily complicated expression.

To use it to do a linear regression, you would use a cost function like:

def myfunc(c):
    model = c[0]*x+c[1]
    error_squared = (model - y_exp)**2
    return error_squared.sum()

where x and y_exp would have to be defined in the script before the
function is called.

c_final = optimize.fmin(myfunc, [m0, b0])

would then fit the data using m0 and b0 as initial guesses.

fmin essentially varies the parameters in the vector c until a minimum
of the returned value is found.  You could use any model you wanted.



On 10/5/07, halish <mhaligowski@googlemail.com> wrote:
> Hello,
> in the first place, I'd like to say 'Hi' to all the subscribers, as
> this is my first mail.
> I study econometrics and statistics, and i wanted to try out my
> favourite programming as a scientific tool. Unfortunately, I could not
> find a function for ordinary least squares estimation. The
> stats.linregress() is insufficient for my needs, and stats.glm() seems
> to be for something else (not sure really).
> Would you guys suggest something? Or should I just write it on my own?
> Regards,
> halish
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