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November 1, 2015 12:01
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Orthogonal distance regression in Python, with the same interface as the linregress function
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# Copyright (c) 2013, Robin Wilson | |
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# | |
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from scipy.odr import Model, Data, ODR | |
from scipy.stats import linregress | |
import numpy as np | |
def orthoregress(x, y): | |
"""Perform an Orthogonal Distance Regression on the given data, | |
using the same interface as the standard scipy.stats.linregress function. | |
Arguments: | |
x: x data | |
y: y data | |
Returns: | |
[m, c, nan, nan, nan] | |
Uses standard ordinary least squares to estimate the starting parameters | |
then uses the scipy.odr interface to the ODRPACK Fortran code to do the | |
orthogonal distance calculations. | |
""" | |
linreg = linregress(x, y) | |
mod = Model(f) | |
dat = Data(x, y) | |
od = ODR(dat, mod, beta0=linreg[0:2]) | |
out = od.run() | |
return list(out.beta) + [np.nan, np.nan, np.nan] | |
def f(p, x): | |
"""Basic linear regression 'model' for use with ODR""" | |
return (p[0] * x) + p[1] |
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