#!/usr/bin/env python3
"``naginterface.library.correg.linregm_fit_stepwise`` Python Example."
# NAG Copyright 2017-2019.
# pylint: disable=invalid-name,too-many-locals
import numpy as np
from naginterfaces.base import utils
from naginterfaces.library import correg
[docs]def main():
"""
Example for :func:`naginterfaces.library.correg.linregm_fit_stepwise`.
Stepwise linear regression.
>>> main()
naginterfaces.library.correg.linregm_fit_stepwise Python Example Results.
Stepwise linear regression.
Starting Stepwise Selection
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Forward Selection
Variable 1 Variance ratio = 1.260E+01
Variable 2 Variance ratio = 2.196E+01
Variable 3 Variance ratio = 4.403E+00
Variable 4 Variance ratio = 2.280E+01
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Adding variable 4 to model
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Backward Selection
Variable 4 Variance ratio = 2.280E+01
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Keeping all current variables
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Forward Selection
Variable 1 Variance ratio = 1.082E+02
Variable 2 Variance ratio = 1.725E-01
Variable 3 Variance ratio = 4.029E+01
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Adding variable 1 to model
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Backward Selection
Variable 1 Variance ratio = 1.082E+02
Variable 4 Variance ratio = 1.593E+02
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Keeping all current variables
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Forward Selection
Variable 2 Variance ratio = 5.026E+00
Variable 3 Variance ratio = 4.236E+00
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Adding variable 2 to model
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Backward Selection
Variable 1 Variance ratio = 1.540E+02
Variable 2 Variance ratio = 5.026E+00
Variable 4 Variance ratio = 1.863E+00
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Dropping variable 4 from model
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Forward Selection
Variable 3 Variance ratio = 1.832E+00
Variable 4 Variance ratio = 1.863E+00
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Finished Stepwise Selection
Fitted model summary:
Term Estimate Standard Error
Intercept: 5.258e+01 2.294e+00
Variable: 1 1.468e+00 1.213e-01
Variable: 2 6.623e-01 4.585e-02
RMS is 5.790e+00
"""
print(
'naginterfaces.library.correg.linregm_fit_stepwise '
'Python Example Results.'
)
print('Stepwise linear regression.')
# The design matrix:
z = np.array([
[7., 26., 6., 60., 78.5],
[1., 29., 15., 52., 74.3],
[11., 56., 8., 20., 104.3],
[11., 31., 8., 47., 87.6],
[7., 52., 6., 33., 95.9],
[11., 55., 9., 22., 109.2],
[3., 71., 17., 6., 102.7],
[1., 31., 22., 44., 72.5],
[2., 54., 18., 22., 93.1],
[21., 47., 4., 26., 115.9],
[1., 40., 23., 34., 83.8],
[11., 66., 9., 12., 113.3],
[10., 68., 8., 12., 109.4],
])
# Compute sums of squares and cross-products of deviations
# from the mean.
# No weights:
sw, wmean, c = correg.ssqmat(z)
# The variable-selection array:
m = z.shape[1] - 1
isx = [1]*m
# Other parameters for the regression function:
fout = 2.
# Use monitoring, NAG-supplied, without a NAG function prefix in
# the output:
monlev = 1
monfun = None
io_manager = utils.FileObjManager(locus_in_output=False)
fit = correg.linregm_fit_stepwise(
z.shape[0], wmean, c, sw, isx, fout=fout,
monlev=monlev, monfun=monfun, io_manager=io_manager,
)
print('Fitted model summary:')
print('Term' + ' '*9 + 'Estimate' + ' '*2 + 'Standard Error')
print('Intercept: {:.3e} {:.3e}'.format(fit.b[0], fit.se[0]))
for i in range(1, m + 1):
if fit.isx[i - 1] not in [1, 2]:
continue
print('Variable:' + ' '*2 + '{:d} {:.3e} {:.3e}'.format(
i, fit.b[i], fit.se[i]
))
print('RMS is {:.3e}'.format(fit.rms))
if __name__ == '__main__':
import doctest
import sys
sys.exit(
doctest.testmod(
None, verbose=True, report=False,
optionflags=doctest.REPORT_NDIFF,
).failed
)
