[SciPy-User] How to estimate error in polynomial coefficients from scipy.polyfit?
josef.pktd@gmai...
josef.pktd@gmai...
Thu Mar 25 23:10:37 CDT 2010
On Thu, Mar 25, 2010 at 11:40 PM, David Goldsmith
<d.l.goldsmith@gmail.com> wrote:
> On Thu, Mar 25, 2010 at 3:40 PM, Jeremy Conlin <jlconlin@gmail.com> wrote:
>>
>> Yikes! This sounds like it may be more trouble than it's worth. I
>> have a few sets of statistical data that each need to have curves fit
>> to them.
>
> That's an awfully generic need - it may be obvious from examination of the
> data that a line is inappropriate, but besides polynomials there are many
> other non-linear models (which can be linearly fit to data by means of data
> transformation) which possess fewer parameters (and thus are simpler from a
> parameter analysis perspective). So, the question is: why are you fitting
> to polynomials? If it's just to get a good fit to the data, you might be
> getting "more fit" than your data warrants (and even if that isn't a
> problem, you probably want to use a polynomial form different from "standard
> form," e.g., Lagrange interpolators). Are you sure something like an
> exponential growth/decay or power law model (both of which are "more
> natural," linearizable, two-parameter models) wouldn't be more appropriate -
> it would almost certainly be simpler to analyze (and perhaps easier to
> justify to a referee).
>
> On this note, perhaps some of our experts might care to comment: what
> "physics" (in a generalized sense) gives rise to a polynomial dependency of
> degree higher than two? The only generic thing I can think of is something
> where third or higher order derivatives proportional to the independent
> variable are important, and those are pretty uncommon.
Taylor series expansion is a "natural" source for higher order
polynomials, although as Anne pointed out, it's not a "nice" base for
the space of functions. In most of the examples I have seen, 3rd order
was the highest, after that non-parametric, Fourier, Chebychev or
Hermite.
I'm attaching the statsmodels equivalent of most of the example in the
Stata FAQ. A few parts are (still) a bit more complicated than the
Stata equivalent.
Final result: F-test rejects null of equal coefficients (small
p-value) if the coefficients are far enough apart, and does not reject
if the two regression equations are samples from the same data
generating process.
Josef
>
> DG
>
>>
>> I can see what the parameters are and they are similar
>> between each set. I want to know if the coefficients are within a few
>> standard deviations of each other. That's what I was hoping to get
>> at, but it seems to be more trouble than I'm willing to accept.
>> Unless anyone has a simpler solution.
>>
>> Thanks,
>> Jeremy
>> >
>> > Anne
>> >
>> >> residuals, rank, singular_values, rcond : present only if `full` = True
>> >> Residuals of the least-squares fit, the effective rank of the
>> >> scaled
>> >> Vandermonde coefficient matrix, its singular values, and the
>> >> specified
>> >> value of `rcond`. For more details, see `linalg.lstsq`.
>> >>
>> >> I don't think any of these things are "design matrix" as you have
>> >> indicated I need. The documentation for linalg.lstsq does not say
>> >> what rcond is unfortunately. Any ideas?
>> >>
>> >> Jeremy
>> >> _______________________________________________
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>> >>
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-------------- next part --------------
# -*- coding: utf-8 -*-
"""F test for null hypothesis that coefficients in two regressions are the same
Created on Thu Mar 25 22:56:45 2010
Author: josef-pktd
"""
import numpy as np
from numpy.testing import assert_almost_equal
import scikits.statsmodels as sm
nobs = 10 #100
x1 = np.random.randn(nobs)
y1 = 10 + 15*x1 + 2*np.random.randn(nobs)
x1 = sm.add_constant(x1) #, prepend=True)
assert_almost_equal(x1, np.vander(x1[:,0],2), 16)
res1 = sm.OLS(y1, x1).fit()
print res1.params
print np.polyfit(x1[:,0], y1, 1)
assert_almost_equal(res1.params, np.polyfit(x1[:,0], y1, 1), 14)
print res1.summary(xname=['x1','const1'])
#regression 2
x2 = np.random.randn(nobs)
y2 = 19 + 17*x2 + 2*np.random.randn(nobs)
#y2 = 10 + 15*x2 + 2*np.random.randn(nobs) # if H0 is true
x2 = sm.add_constant(x2) #, prepend=True)
assert_almost_equal(x2, np.vander(x2[:,0],2), 16)
res2 = sm.OLS(y2, x2).fit()
print res2.params
print np.polyfit(x2[:,0], y2, 1)
assert_almost_equal(res2.params, np.polyfit(x2[:,0], y2, 1), 14)
print res2.summary(xname=['x2','const2'])
# joint regression
x = np.concatenate((x1,x2),0)
y = np.concatenate((y1,y2))
dummy = np.arange(2*nobs)>nobs-1
x = np.column_stack((x,x*dummy[:,None]))
res = sm.OLS(y, x).fit()
print res.summary(xname=['x','const','x2','const2'])
print '\nF test for equal coefficients in 2 regression equations'
#effect of dummy times second regression is zero
#is equivalent to 3rd and 4th coefficient are both zero
print res.f_test([[0,0,1,0],[0,0,0,1]])
print '\nchecking coefficients individual versus joint'
print res1.params, res2.params
print res.params[:2], res.params[:2]+res.params[2:]
assert_almost_equal(res1.params, res.params[:2], 13)
assert_almost_equal(res2.params, res.params[:2]+res.params[2:], 13)
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